Supernumerary teeth (ST) not only commonly obstruct adjacent permanent teeth, causing ectopic eruption, rotation, and root absorption but also present the potential for cystic lesions extending into the nasal cavity. Early intervention is crucial to avoid severe complications like periodontal abscesses and severe dental caries. However, ST may occur in any oral region with variable shapes and growth directions, posing a significant challenge for image recognition. This prevalent dental condition in the Asian population (3-5%) lacks sufficient representation in AI-assisted systems. Given this, the study suggests a system reliant on Convolutional Neural Network (CNN) for identifying supernumerary teeth in occlusal radiographs. The algorithm incorporates feature enhancement and AI integration methods. Additionally, the study introduces a color mapping approach to address the variability in ST shape and growth direction, significantly improving recognition accuracy from 63.16% to 76.32%. The result of this research demonstrates an improvement of 18.43% in accuracy and 78.95% in recall rate for supernumerary tooth detection compared to existing technology. This study successfully addresses the challenge of identifying supernumerary teeth, which were previously difficult to recognize. To ensure compliance with ethical and regulatory standards, the proposal has obtained certification from the Institutional Review Board (IRB) with the reference number 202400084B0.

Enterprises need to obtain information about not only specific customer preferences, but also, more importantly, customers' psychological characteristics that significantly influence their consumption behaviors and response to intelligent-based marketing activities. If enterprises want to implement more precise intelligent selling activities for customers, customers' personality information will serve as a highly valued reference. The automatic detection method proposed in this study is based on techniques such as text semantic mining and machine learning to conduct personality type prediction on the target by collecting and analyzing the target's social media data. In the test, 5,858 statuses were obtained, 815 of which were labeled, with 122 effective tags. In general, when n = 5, the labeling rate can reach 60-80%. The status property classifier (SPC) proposed in this study can predict the personality type (PT) of the user publishing the status set with a high degree of accuracy by conducting text semantic mining on the status set.

Abstract Companies need to find out about the personal values of customers and identify customer segments before developing effective business models (BMs) or marketing strategies. Therefore, understanding the personal values of customers is critical in BM design. Traditional personal value evaluation methods are laborious and time‐consuming. In recent years, social media have become an important platform for people to share ideas and express views, making the presence of a huge amount of opinions on platforms that can cater for the demand of data for personal value analysis of customers. This study took Facebook and Instagram as the targets and developed a novel personal value forecasting method to help enterprises obtain the various personal values of customer segments automatically at a lower cost. This study adopted Schwartz's value theory as the value model and proposed a consistency model and a relativity model for weighted calculations, so as to determine the feature of a value tag. Finally, the feature selection algorithm and classification algorithm were used for judging values. In the evaluation phase of this study, 61 participants were recruited to test the proposed method. The proposed method could assist enterprises in better understanding personal value information.

Classical multiuser information theory studies the fundamental limits of models with a fixed (often small) number of users as the coding blocklength goes to infinity. This paper proposes a new paradigm, referred to as many-user information theory, where the number of users is allowed to grow with the blocklength. This paradigm is motivated by emerging systems with a massive number of users in an area, such as the Internet of Things. The focus of this paper is the many-access channel model, which consists of a single receiver and many transmitters, whose number increases unboundedly with the blocklength. Moreover, an unknown subset of transmitters may transmit in a given block and need to be identified as well as decoded by the receiver. A new notion of capacity is introduced and characterized for the Gaussian many-access channel with random user activities. The capacity can be achieved by first detecting the set of active users and then decoding their messages. The minimum cost of identifying the active users is also quantified.

The read channel of a Flash memory cell degrades after repetitive program and erase (P/E) operations. This degradation is often modeled as a function of the number of P/E cycles. In contrast, this paper models the degradation as a function of the cumulative effect of the charge written and erased from the cell. Based on this modeling approach, this paper dynamically allocates voltage using lower voltage write thresholds at the beginning of the device lifetime and increasing the thresholds as needed to maintain the mutual information of the read channel in the face of degradation. This paper introduces the technique in an idealized setting and then removes ideal assumptions about channel knowledge and available voltage resolution to conclude with a practical scheme with performance close to that of the idealized setting.

Current generation Flash devices experience significant read-channel degradation from damage to the oxide layer during program and erase operations. Information about the read-channel degradation drives advanced signal processing methods in Flash to mitigate its effect. In this context, channel estimation must be ongoing since channel degradation evolves over time and is a function of the number of program/erase (P/E) cycles. This paper proposes a framework for on-line model-based channel estimation using limited channel measurements (reads). This paper uses a channel model characterizing degradation as a function of retention time and the amount of charge programmed and erased. For channel histogram measurements, equal-probability (equal-height) bin placement yields a good approximation to the original distribution using only ten bins (i.e. nine reads). With the channel model and binning strategy in place, this paper explores candidate numerical least squares channel estimation algorithms and ultimately demonstrates the effectiveness of the Levenberg-Marquardt algorithm, which provides both speed and accuracy, in an algorithm for voltage allocation.

This paper proposes protograph-based Raptor-like (PBRL) codes as a class of rate-compatible low-density parity-check codes for binary-input AWGN channels. As with the Raptor codes, exclusive-OR operations on precoded bits produce additional parity bits providing extensive rate compatibility. Unlike Raptor codes, each additional parity bit in the protograph is explicitly designed to optimize the density evolution threshold. During the lifting process, approximate cycle extrinsic message degree (ACE) and circulant progressive edge growth (CPEG) constraints are used to avoid undesirable graphical structures. Some density-evolution performance is sacrificed to obtain lower error floors, particularly at short block-lengths. Simulation results are shown for information block sizes of k = 1032 and 16 384. For a target frame error rate of 10-5, at each rate, the k = 1032 and 16 384 code families perform within 1 dB and 0.4 dB of both the Gallager bound and the normal approximation, respectively. The 16 384 code family outperforms the best known standardized code family, namely, the AR4JA codes. The PBRL codes also outperform DVB-S2 codes that have the advantages of longer blocklengths and outer BCH codes. Performance is similar to RC code families designed by Nguyen et al. that do not constrain codes to have the PBRL structure and involve simulation in the optimization process at each rate.

This paper presents a variable-length decision-feedback coding scheme that achieves high rates at short blocklengths. This scheme uses the reliability-output Viterbi algorithm (ROVA) to determine when the receiver's decoding estimate satisfies a given error constraint. We evaluate the performance of both terminated and tail-biting convolutional codes at average blocklengths less than 300 symbols, using the ROVA and the tail-biting ROVA, respectively. Comparing with recent results from finite-blocklength information theory, simulations for both the BSC and the AWGN channel show that the reliability-based decision-feedback scheme can surpass the random-coding lower bound on throughput for feedback codes at some blocklengths less than 100 symbols. This is true both when decoding after every symbol is permitted and when decoding is limited to a small number of increments. Finally, the performance of the reliability-based stopping rule with the ROVA is compared with retransmission decisions based on CRCs. For short blocklengths where the latency overhead of the CRC bits is severe, the ROVA-based approach delivers superior rates.

Classical multiuser information theory studies the fundamental limits of models with a fixed (often small) number of users as the coding blocklength goes to infinity. Motivated by emerging systems with a massive number of users, this paper studies the new many-user paradigm, where the number of users is allowed to grow with the blocklength. The focus of this paper is the degraded many-broadcast channel model, whose number of users may grow as fast as linearly with the blocklength. A notion of capacity in terms of message length is defined and an example of Gaussian degraded many-broadcast channel is studied. In addition, a numerical example for the Gaussian degraded many-broadcast channel with fixed transmit power constraint is solved, where every user achieves strictly positive message length asymptotically.

This paper considers finite-blocklength achievability for rate-compatible codes. For a fixed number of messages, random coding analysis determines a sequence of achievable error probabilities for a sequence of blocklengths. However, traditional random coding achievability draws each code independently so that it does not show that a family of rate-compatible codes achieves that same sequence of error probabilities. Using random code extension, this paper shows achievable error probabilities for rate-compatible channel codes with finite blocklengths. This paper also shows that for a class of input-invariant channels, the rate-compatible constraint does not affect the achievability bounds on error rates when a threshold decoder is used.

This paper studies variable-length coding with noise-less feedback for discrete memoryless channels. Yamamoto and Itoh's two-phase scheme achieves the optimal error-exponent, but due to the block-coding nature it is not optimal in the expansion of the message size logM. Polyanskiy et al. showed that with feedback, the back-off from capacity is logarithmic in the expected latency ℓ. The O(log ℓ) back-off is achieved by using an incremental redundancy (IR) scheme that only utilizes feedback to determine the stopping time. However, the achievable error-exponent of the IR scheme is not optimal. This paper shows that a two-phase coding scheme where each phase uses an IR scheme achieves the optimal error-exponent while maintaining an expansion on the message size that yields the O(log ℓ) back-off.

One advantage of feedback in a point-to-point memoryless channel is the reduction of the average blocklength required to approach capacity. This paper presents a communication system with feedback that uses carefully designed non-binary LDPC (NB-LDPC) codes and incremental transmissions to achieve 92–94% of the idealized throughput of rate-compatible sphere-packing with maximum-likelihood decoding (RCSP-ML) for average blocklengths of 150–450 bits. The system uses active feedback by carefully selecting each bit of additional incremental information to improve the reliability of the least reliable variable node. The system uses post processing in the decoder to further improve performance. The average blocklengths of 150–450 bits are small enough that feedback provides a throughput advantage but also large enough that overhead that might be associated with transmitter confirmation is more easily tolerated.

Multiple reads of the same Flash memory cell with distinct word-line voltages provide enhanced precision for LDPC decoding. In this paper, the word-line voltages are optimized by maximizing the mutual information (MI) of the quantized channel. The enhanced precision from a few additional reads allows frame error rate (FER) performance to approach that of full-precision soft information and enables an LDPC code to significantly outperform a BCH code. A constant-ratio constraint provides a significant simplification in the optimization with no noticeable loss in performance. For a well-designed LDPC code, the quantization that maximizes the mutual information also minimizes the FER in our simulations. However, for an example LDPC code with a high error floor caused by small absorbing sets, the MMI quantization does not provide the lowest frame error rate. The best quantization in this case introduces more erasures than would be optimal for the channel MI in order to mitigate the absorbing sets of the poorly designed code. The paper also identifies a trade-off in LDPC code design when decoding is performed with multiple precision levels; the best code at one level of precision will typically not be the best code at a different level of precision.

Theoretical analysis has long indicated that feedback improves the error exponent but not the capacity of single user memoryless channels. Recently Polyanskiy et al. studied the benefit of variable-length feedback with termination (VLFT) codes in the non-asymptotic regime. In that work, achievability is based on an infinite-length random code and decoding is attempted at every symbol. The coding rate backoff from capacity due to channel dispersion is greatly reduced with feedback, allowing capacity to be approached with surprisingly small expected latency. This paper is concerned with VLFT codes based on finite-length codes and decoding attempts only at certain specified decoding times. Note that with an underlying finite-length code, the transmitter may have to repeat code symbols. The penalties of using a finite block-length N and a sequence of specified decoding times are studied. This paper shows that properly scaling N with the expected latency can achieve the same performance up to second order terms as with N =∞. The penalty introduced by limiting the decoding is a constant term and hence the performance approaches capacity as expected latency increases as long as the interval between periodic decoding times grows sub-linearly with the expected latency.

This paper presents a reliability-based decoding scheme for variable-length coding with feedback and demonstrates via simulation that it can achieve higher rates than Polyanskiy et al.'s random coding lower bound for variable-length feedback (VLF) coding on both the BSC and AWGN channel. The proposed scheme uses the reliability output Viterbi algorithm (ROVA) to compute the word error probability after each decoding attempt, which is compared against a target error threshold and used as a stopping criterion to terminate transmission. The only feedback required is a single bit for each decoding attempt, informing the transmitter whether the ROVA-computed word-error probability is sufficiently low. Furthermore, the ROVA determines whether transmission/decoding may be terminated without the need for a rate-reducing CRC.

This paper presents a new construction of punctured-node protograph-based Raptor-like (PN-PBRL) codes that is suitable for long-blocklength applications. As with the Raptor codes, additional parity bits can be easily produced by exclusive-OR operations on the precoded bits, providing extensive rate compatibility. The new construction provides low iterative decoding thresholds that are within 0.45 dB of the capacity for all code rates studied, and the construction is suitable for long blocklengths. Comparing at the same information block size of k = 16368 bits, the PN-PBRL codes are as good as the best known AR4JA codes in the waterfall region. The PN-PBRL codes also perform comparably to DVB-S2 LDPC codes even though the DVBi-S2 codes have longer blocklength and outer BCH codes.

Recent work by Polyanskiy et al. and Chen et al. has excited new interest in using feedback to approach capacity with low latency. Polyanskiy showed that feedback identifying the first symbol at which decoding is successful allows capacity to be approached with surprisingly low latency. This paper uses Chen's rate-compatible sphere-packing (RCSP) analysis to study what happens when symbols must be transmitted in packets, as with a traditional hybrid ARQ system, and limited to relatively few (six or fewer) incremental transmissions. Numerical optimizations find the series of progressively growing cumulative block lengths that enable RCSP to approach capacity with the minimum possible latency. RCSP analysis shows that five incremental transmissions are sufficient to achieve 92% of capacity with an average block length of fewer than 101 symbols on the AWGN channel with SNR of 2.0 dB. The RCSP analysis provides a decoding error trajectory that specifies the decoding error rate for each cumulative block length. Though RCSP is an idealization, an example tail-biting convolutional code matches the RCSP decoding error trajectory and achieves 91% of capacity with an average block length of 102 symbols on the AWGN channel with SNR of 2.0 dB. We also show how RCSP analysis can be used in cases where packets have deadlines associated with them (leading to an outage probability).

Recent results by Chen et al. and Polyanskiy et al. explore using feedback to approach capacity with short blocklengths. This paper explores Chernoff bounding techniques to extend the rate-compatible sphere-packing (RCSP) analysis proposed by Chen et al. to scenarios involving numerous retransmissions and different step sizes in each incremental retransmission. Williamson et al. employ exact RCSP computations for up to six transmissions. However, exact RCSP computation with more than six retransmissions becomes unwieldy because of joint error probabilities involving numerous chi-squared distributions. This paper explores Chernoff approaches for upper and lower bounds on the error probability to provide support for computations involving more than six transmissions. We present two versions of upper and lower bounds on the error probability for the two-transmission case. One of the versions is extended to the general case of m transmissions where m ≥ 1. Computing the bounds for general m requires minimization of exponential functions with the auxiliary parameters. The numerical results, however, show that weakening the bounds by considering marginal probabilities and the case of two transmissions is already tight. These bounds also provide good estimates of the expected throughput and expected latency, which are useful for optimization purposes.

This paper presents a new class of rate-compatible LDPC codes, protograph-based Raptor-like (PBRL) codes. The proposed PBRL codes are jointly decodable with an iterative belief propagation decoder. As with Raptor codes, additional parity bits can be easily produced by exclusive-or operations on the precoded bits, providing extensive rate compatibility. This paper provides a design procedure that optimizes this class of rate- compatible LDPC codes. The new PBRL codes outperform 3GPP rate-compatible turbo codes with the same short blocklength at high SNR and show no sign of an error floor at the FER region of 10-7.

Theoretical analysis has long indicated that feedback improves the error exponent but not the capacity of memoryless Gaussian channels. Recently, Chen et al. demonstrated that an incremental redundancy scheme can use noiseless feedback to help short convolutional codes deliver the bit-error-rate performance of a long blocklength turbo code, but with much lower latency. Such a latency improvement is suggested by the error-exponent analysis, but there is no theoretical work that estimates how much latency improvement is possible with feedback for practical blocklengths and rates. This paper provides a code-independent analysis that quantifies the latency benefits possible by using modified incremental redundancy with feedback (MIRF). A sphere-packing analysis yields the throughput vs. latency performance of both a baseline ACK/NACK scheme and MIRF. The sphere-packing analysis matches well with simulations using turbo and convolutional codes, showing that the analysis has a practical predictive value.

Success in a knowledge economy requires effectively using existing knowledge to create new knowledge. Security for knowledge sharing in enterprises is critical for protecting intellectual assets. This study develops the functional framework of a knowledge management system (KMS) with knowledge access control for effectively and securely sharing knowledge within an enterprise or across teams. The functional framework of the proposed KMS includes the following nine layers: user interface layer, knowledge access control and security layer, knowledge representation layer, knowledge process layer, conceptual knowledge layer, knowledge index layer, transport layer, middleware layer and physical knowledge layer. A method of conceptual knowledge representation in the knowledge representation layer is then proposed. Finally, an ontology-based knowledge access control model based on role-based access control (RBAC) model and the conceptual knowledge representation method is proposed for managing user knowledge privileges in a knowledge sharing enterprise. The proposed method can enhance (1) precision in describing knowledge and knowledge relationships, (2) ensure security of knowledge access and sharing within an enterprise and (3) accurately and rapidly identify user knowledge access privileges.

A virtual enterprise (VE) refers to a cooperative alliance of legally independent enterprises, institutions, or single persons that collaborate with each other by sharing business processes and resources across enterprises in order to raise enterprise competitiveness and reduce production costs. Successful VEs require complete information transparency and suitable resource sharing among coworkers across enterprises. Hence, this investigation proposes a formal flexible integration solution, named the formal VE access control (VEAC) model, based on the role-based AC model, to integrate and share distributed resources owned by VE members. The formal VEAC model comprises a fundamental VEAC model, a project AC policy (PACP) language model, and a model construction methodology. The fundamental VEAC model manages VE resources and the resources of participating enterprises, in which various project relationships are presented to facilitate different degrees of resource sharing across projects and enterprise boundaries, and cooperative modes among VE roles are presented to enable collaboration among coworkers in a VE. This PACP language model features object-subject-action-condition AC policies that jointly determine user access authorizations. In addition, the methodology supplies a systematic method to identify fundamental elements of the VEAC model and to establish assignments between elements and relations.