bluuebunny/crossref_metadata_2025 huggingface dataset:
Sentiment and Emotion-Aware Multi-Criteria Fuzzy Group Decision Making System
Authors: Yerkin Adilet , Shamoi Pakizar , Kadyrgali Elnara
Published: December 2024

In today’s world, making decisions as a group is common, whether choosing a restaurant or deciding on a holiday destination. Group decision-making (GDM) systems play a crucial role by facilitating consensus among participants with diverse preferences. Discussions are one of the main tools people use to make decisions. When people discuss alternatives, they use natural language to express their opinions. Traditional GDM systems generally require participants to provide explicit opinion values to the system. However, in real-life scenarios, participants often express their opinions through some text (e.g., in comments, social media, messengers, etc.). This paper introduces a sentiment and emotion-aware multi-criteria fuzzy GDM system designed to enhance consensus-reaching effectiveness in group settings. This system incorporates natural language processing to analyze sentiments and emotions expressed in textual data, enabling an understanding of participant opinions besides the explicit numerical preference inputs. Once all the experts have provided their preferences for the alternatives, the individual preferences are aggregated into a single collective preference matrix. This matrix represents the collective expert opinion regarding the other options. Then, sentiments, emotions, and preference scores are inputted into a fuzzy inference system to get the overall score. The proposed system was used for a small decision-making process – choosing the hotel for a vacation by a group of friends. Our findings demonstrate that integrating sentiment and emotion analysis into GDM systems allows everyone’s feelings and opinions to be considered during discussions and significantly improves consensus among participants.
A Fuzzy Measurement-Based Algorithm for Spoken English Evaluation
Authors: Dong Jinwei , He Ziting , Chen Hao , Zhang Jinchun , Zhi Limin
Published: December 2024

This study explored an improved English language evaluation algorithm for virtual learning environments, particularly in metaverse educational settings. By integrating big data analysis with fuzzy measure theory, the model extracted learners’ language usage habits, progress, and difficulties from large datasets, overcoming the limitations of traditional subjective evaluation methods. Fuzzy measures quantified subjective factors such as clarity, fluency and intonation, while a Sugeno integral approach combined these measures into an overall score. Comparisons with traditional methods have shown significant improvements in the assessment of speaking skills across a range of proficiency levels.
An Efficient Approach for Mining Temporal Fuzzy High-Utility Itemsets
Authors: Kawshik Sadnan Kibria , Nayeen Zulker , Ahmed Chowdhury Farhan , Alam Md. Tanvir , Leung Carson K. , Connor Connor C.J.
Published: December 2024

The motivation behind fuzzy logic in data mining is to address the inherent uncertainty and imprecision in real-world data and make the mined results more interpretable for humans. Temporal Fuzzy High Utility Itemset Mining, which incorporates transaction time, is an emerging field with significant potential for analyzing time-sensitive data. Although several studies in this area have been conducted, for instance, recently fuzzy list-based approaches, a significant challenge remains in joining operations of conditional fuzzy lists when generating candidate itemsets. To solve this, we have proposed a pruning strategy based on item co-occurrences to reduce the number of join operations using anti-monotonic property. Experiments on real datasets show our approach outperforms traditional algorithms in terms of runtime and candidate generations with little memory overhead, up to 95% of non-promising candidates are pruned.
Electric Power Load Forecasting: Discovering Causal Links with Meteorological Data
Authors: Ma Hongwei , Peng Huimin , Ren Weijie
Published: December 2024

Power system load forecasting is the foundation of power system operation and planning. Meteorological factors such as temperature, humidity, and wind speed have a significant impact on the load of the power system. In recent years, with the popularity of data-driven methods, power system load forecasting has developed. However, in existing research on power systems, the study of relationships between variables is predominantly based on correlation, with little mention of the concept of causality. In this paper, an adaptive time window strategy is proposed to identify time periods where causal relationships may change. Then, the Time Causal Discovery Framework (TCDF) was used at each time window to explore the causal relationship between electricity load and climate data. Finally, compared with the traditional correlation-based analysis method, the experiment shows that our method not only explains the causal relationship between power load and climate, but also effectively improves the prediction performance.
Vision Transformers and CNN-Based Knowledge-Distillation for Histopathological Image Classification
Authors: Boudissa Seddik , Debsarkar Shyam Sundar , Kawanaka Hiroharu , Aronow Bruce , Prasath V.B. Surya
Published: December 2024

Histopathological image analysis remains at the forefront of computational pathology presenting numerous challenges and demanding tasks, primarily due to the complex nature of tissue structures and the extensive scale of whole slide images (WSIs). Deep learning models have been widely used in histopathology image analysis, especially convolutional neural network (CNN)-based models for classification. However, CNNs have certain limitations due to their small receptive field. Recent works employed adaptations of the classical transformer architecture to visual data [1] [2]. Models such as Vision Transformer (ViT) and Swin Transformer leverage the powerful multi-head self-attention mechanism and have demonstrated comparable or superior performance to state-of-the-art CNN-based classification models. Despite their successes, these models require huge amounts of training data to effectively learn representations as they lack the inherent inductive biases of CNNs. This work compares Vision Transformers with baseline CNN models using a breast cancer histopathological dataset. Further, we employ a novel knowledge-distillation approach to enhance the learning efficiency of vits, When trained with a limited amount of data, Unlike previous works, we aimed to minimize convolution operations when generating patch embeddings to preserve spatial information before reaching the transformer attention layers, we achieved an accuracy of 87.7% for the ViT-base trained as a student of ResNet50, which represents a 1.2% improvement in accuracy over the standalone ViT-base. [3]
Efficient Early Sparsification for Accelerating Solutions to the Traveling Salesman Problem
Authors: Jiang Yonglu , Xing Hongyu , Shi Hanchen , Wei Zijing , Yang Zhongguo
Published: December 2024

The Travelling Salesman Problem (TSP) is a well-established NP-hard problem, often tackled using heuristic algorithms. While state-of-the-art learning-driven approaches for TSP perform comparably to classical solvers when trained on trivially small instances, they struggle to generalize the learned policy to larger, practical-scale instances. Consequently, as the number of cities increases, the efficiency of these algorithms diminishes significantly. This paper focuses on the early elimination of non-advantageous edges while retaining advantageous ones, thereby accelerating the TSP solution. Our approach involves capturing the differential frequencies of various edges during the early stages of an intelligent evolutionary algorithm, which we term as “knowledge” derived from the evolutionary process. We then design effective features to characterize the similarities and differences between edges. Leveraging this “knowledge”, we label edges as either advantageous or non-advantageous. By integrating the guidance of a predictor, we intervene in the intelligent evolutionary algorithm to sparsify the TSP graph, ultimately enhancing the TSP solution. Our method effectively prunes edges in TSP instances, preserving the optimal path while minimizing redundant edges. Experimental results demonstrate that our approach significantly improves both computational efficiency and solution quality on the TSP50 and TSP100 test sets compared to other models. This methodology offers a novel perspective by extracting and utilizing latent knowledge among edges, thereby enhancing the performance of intelligent evolutionary algorithms in solving the TSP. Our findings not only improve current methodologies but also encourage further exploration and development in related fields and practical applications.
Correlation Analysis over Big Multidimensional Datasets: A Powerful Paradigm for Next-Generation Big Data Analytics Research—Definitions, Models, Implementations
Authors: Cuzzocrea Alfredo
Published: December 2024

Correlation analysis has been a powerful paradigm to discover and analyze hidden properties and patterns of large-scale datasets for decades. At now, correlation analysis turns to be a perfect tool for supporting big multidimensional data analysis and mining, with a wide range of relevant properties, including the amenity of supporting meaningfully exploration and discovery of multidimensional ranges kept in such kind of datasets. These operators are thus the basis for several multidimensional big data analytical tools that can be designed and implemented on top of the foundations defined by correlation functions. In line this this scientific area, the talk will provide introduction and motivations, models and algorithms, and, finally, best-practices guidelines for effective and efficient implementations of correlation-analysis-based tools over big multidimensional datasets.
A Preliminary Investigation into the Current Status and Prospects of Visual Analysis of Acoustic Properties for the Singing Voice
Authors: Hua Jie , Yi Wei
Published: December 2024

Acoustic analysis is an objective measurement approach commonly used in the assessment of the singing voice. It involves parameters such as fundamental frequency, formant, intensity, vibrato pattern, and others, providing parameters on the internal view of the singing voice. However, presenting multi-dimensional voice data from multiple audio files simultaneously in current studies is a challenging task. This review summarises existing methodologies, highlighting their limitations and proposing future directions, particularly integrating Information and Communications Technology to enhance vocal analysis visualisation. This study also emphasises the critical role of advanced visualisation methods in enhancing the accuracy, accessibility, and impact of vocal analysis, and it aims to enhance the understanding of vocal mechanisms, support singers and students in vocal practice, and drive forward the field of vocal research through innovative, user-friendly technologies. Our future work will focus on developing interactive visualisation techniques to simplify and enrich the interpretation of vocal data, making complex acoustic properties accessible to a broader audience, including non-experts.
Corrugated Cardboard Defect Detection Based on Attention Mechanism and Lightweight Improvements in YOLOv8
Authors: Zhang Yali , Xu Fang , Liu You , Zhang Guowei , Liu Tongshan , Yan Dan
Published: December 2024

Corrugated cardboard is a crucial component of packaging materials, and its surface defects directly affect the quality and aesthetics of the products. To address the challenges posed by the diverse and uneven distribution of defects on cardboard surfaces, we propose the YOLOv8-GSP, a detection algorithm based on attention mechanisms and lightweight improvements in YOLOv8 for surface defects of corrugated cardboard. Firstly, to tackle the insufficient size of the cardboard defect dataset, Projected GAN was employed to augment the dataset, thereby enhancing the training effectiveness of the detection model. Then, Ghost convolution was introduced to reduce the computational complexity and improve the operational speed. Additionally, an SE attention module was incorporated into the Neck network to emphasize the feature information of the corrugated cardboard surface, thereby enriching feature fusion. We improved YOLOv8 by incorporating Ghost convolution and attention mechanisms to enhance the efficiency and accuracy of the detection model. These improvements not only reduce the computational complexity of the model but also enhance detection performance by better focusing on target features. Our method is experimentally validated on a self-constructed dataset, providing a more efficient and accurate solution for the automatic detection of corrugated cardboard surface defects. Indeed, the proposed method’s mean Average Precision (mAP) increased by 1.6% compared to the standard YOLOv8.
Some Brief Considerations on Computational Statistics Effectiveness and Appropriateness in Natural Language Processing Applications
Authors: Monteleone Mario , Postiglione Alberto
Published: December 2024

This paper addresses the challenges of managing and processing unstructured or semi-structured text, particularly in the context of increasing data volumes that traditional linguistic databases and algorithms struggle to handle in real-time scenarios. While humans can easily navigate linguistic complexities, computational systems face significant difficulties due to algorithmic limitations and the shortcomings of Large Language Models (LLMs). These challenges often result in issues such as a lack of standardized formats, malformed expressions, semantic and lexical ambiguities, hallucinations, and failures to produce outputs aligned with the intricate meaning layers present in human language. As for the automatic analysis of linguistic data, is well known that Natural Language Processing (NLP) uses two different approaches, coming from diverse cultural and experiential backgrounds. The first approach is based on probabilistic computational statistics (PCS), which underpins most Machine Learning (ML), LLMs, and Artificial Intelligence (AI) techniques. The second approach is based, for each specific language, on the formalization of morpho-syntactic features and constraints used by humans in ordinary communication activities. At first glance, the second approach appears more effective in addressing linguistic phenomena such as polysemy and the formation of meaningful distributional sequences or, more precisely, acceptable and grammatical morpho-syntactic contexts. In this paper, we initiate a scientific discussion on the differences between these two approaches, aiming to shed light on their respective advantages and limitations.
Enhancing Transformer Training Efficiency with Dynamic Dropout
Authors: Yan Hanrui , Shao Dan
Published: December 2024

We introduce Dynamic Dropout, a novel regularization technique designed to enhance the training efficiency of Transformer models by dynamically adjusting the dropout rate based on training epochs or validation loss improvements. This approach addresses the challenge of balancing regularization and model capacity, which is crucial for achieving fast convergence and high performance. Our method involves modifying the GPT model to accept a variable dropout rate and updating dropout layers during training using schedules such as linear decay, exponential decay, and validation loss-based adjustments. Extensive experiments on the Shakespeare char dataset demonstrate that Dynamic Dropout significantly accelerates training and improves inference efficiency compared to a baseline model with a fixed dropout rate. The validation loss-based adjustment schedule provided the best overall performance, highlighting the potential of Dynamic Dropout as a valuable technique for training large-scale Transformer models.
Challenges of Merging Generative AI with Metaverse for Next-Gen Education
Authors: Velev Dimiter , Steshina Liudmila , Petukhov Igor , Zlateva Plamena
Published: December 2024

The integration of Generative Artificial Intelligence (GenAI) with the Metaverse for a next-generation education is a complex but challenging task. The GenAI-enhanced Metaverse classrooms require innovative instructional designs that use virtual reality and augmented reality to enhance engagement and personalized learning. Educators must adapt to new roles over traditional teaching methods, while learners need to develop digital literacy skills that are essential for navigating and inhabiting in these environments. Such learning environments require significant advancements in real-time processing, scalability and interoperability of different platforms, while ensuring data privacy and security. The equity of access to high-speed internet and advanced devices still remains a serious barrier, which can increase the potential existing inequalities between different educational environments. Ethical considerations, including the responsible use of GenAI, the creation of unbiased educational content, and the psychological impacts of extended usage of virtual reality, are also of important consideration. The aim of the paper is to explore in detail the different challenges through a comprehensive analysis of the obstacles and potential solutions and to propose a collaborative framework involving educators, technologists, policymakers and industry stakeholders to address the effective implementation of the integration of GenAI and the Metaverse for a next generation education.
An approach to service provisioning with quality of service requirements in ATM networks
Authors: Thomas Panagiotis , Teneketzis Demosthenis
Published: December 2024

We present an approach to Service Provisioning with Quality of Service (QoS) requirements in ATM networks. The ATM network provides services to users every $\widehat{T}$ units of time. Each service is characterized by its one way route, its input traffic constraints and its provided QoS. The input traffic constraints consist of the maximum input rate and an upper bound on the burstiness curve. The QoS provided is defined by the maximum percentage of cell loss along the route and the maximum end‐to‐end cell delay. The service provisioning problem is defined as follows: determine the amount, price and required resources (bandwidth and buffers) for each type of service, that maximize a social welfare function that consists of the network’s revenue and the users’ surplus. This problem is solved every $\widehat{T}$ units of time and the allocation is made only over the available resources, that is, those that are not used by connections that are still active. We prove the existence of a solution to the Service Provisioning Problem and we also suggest an iterative procedure that interprets the solution. The network adjusts the prices to maximize the welfare function and also to guarantee that the allocated resources do not exceed the available resources. Based on these prices, users request a new allocation that minimizes their cost and the network adjusts again the prices based on the new allocation. The above procedure has the following features: (i) the network needs to know only the average request rate for each type of service, their route and the resources requested by the users; (ii) users need to know only their private information (input traffic constraints and QoS requirements), their route, and the prices for resources announced by the network.
Optimization of call admission in a reduced state space
Authors: Ke Kai‐Wei , Lea Chin‐Tau
Published: December 2024

The large number of states that arises in a broadband network makes the design and analysis of call admission policies in a broadband network computationally intractable. State reduction, therefore, is an important step to tackle many broadband networking issues. In this paper we propose a method to reduce the state space. An important characteristic of the method is that the total gain of a policy remains unchanged in the reduced state space: If a policy improves performance in the reduced space, it does likewise in the original space. This means that the search for a better policy can be conducted in the reduced state space created by the state reduction method introduced in this paper. We present several examples to demonstrate the applications of the method for state reduction.
Routing through networks with hierarchical topology aggregation
Authors: Awerbuch Baruch , Du Yi , Khan Bilal , Shavitt Yuval
Published: December 2024

In the future, global networks will consist of a hierarchy of subnetworks called domains. For reasons of both scalability and security, domains will not reveal details of their internal structure to outside nodes. Instead, these domains will advertise only a summary, or aggregated view, of their internal structure, e.g., as proposed by the ATM PNNI standard.

This work compares, by simulation, the performance of several different aggregation schemes in terms of network throughput (the fraction of attempted connections that are realized), and network control load (the average number of crankbacks per realized connection). The simulation emulate a connection oriented network with a PNNI‐like hierarchical source routing algorithm.

Our main results are: (1) minimum spanning tree is a good aggregation scheme; (2) exponential link cost functions perform better than min‐hop routing; (3) our suggested logarithmic update scheme that determine when re‐aggregation should be computed can significantly reduce the computational overhead due to re‐aggregation with a negligible decrease in performance.
CA‐STAR: a centrally‐arbitrated broadcast‐and‐select star architecture for lightwave networks
Authors: Yau Victor , Pawlikowski Krzysztof
Published: December 2024

Parallel use of multiple channels in WDM star networks requires that either each station is equipped with one receiver per channel or we have to deal with destination conflicts whenever a station is reached by more packets than it is able to receive. Earlier proposed networks resolved destination conflicts either after they occur (applying the detect‐and‐retransmit‐if‐lost principle) or before packets are transmitted (applying the request‐schedule‐then‐transmit principle). This paper introduces networks in which such conflicts are resolved whilst packets are en route to their destinations, using a central arbiter station (CA) located at the star coupler. CA detects destination conflicts, buffers packets which would otherwise be lost, and forwards them to their destinations as soon as their destinations are free to receive them. Buffer operations at CA are streamlined, allowing either electronic implementation or a simple optical delay‐line implementation. Their performance is evaluated by studying their delay and throughput characteristics, protocol computational complexity, E/O conversion overhead, and hardware demands.
Alert communication primitives above TCP
Authors: Gouda Mohamed G. , McGuire Tommy M.
Published: December 2024

We consider communication primitives that can be executed by an application process to exchange messages with another application process over a TCP/IP network. A communication primitive is called alert iff it satisfies two conditions. First, if during any execution of the primitive no failure occurs, then the execution completes successfully. Second, if during any execution of the primitive some failure occurs, then the execution is aborted and the process that initiated the execution is informed of the failure. Clearly alert communication primitives are useful in designing reliable distributed applications. We argue that the send primitive over TCP is alert, but the receive primitive over TCP is not. Then, we propose three new receive primitives over TCP and show that each of them is alert. We also discuss how to implement these three primitives and compare their performance.
On cost‐based routing in connection‐oriented high‐speed networks
Authors: Ke Kai‐Wei , Lea Chin‐Tau
Published: December 2024

Not only does cost‐based routing achieve a higher throughput, it also removes the instability problem inherent in fixed‐path routing networks with alternate paths. In the paper we evaluate the performance of two cost‐based routing schemes: one centralized and one distributed. The latter has a much lower complexity than the former. Although the distributed version does not always use the least‐cost path, our results indicate the performance of both schemes are about the same. We also compare them with least‐loaded routing to demonstrate the ability of cost‐based routing to stabilize the network performance even under an over‐loaded condition.

But to make cost‐based routing a viable option for a high‐speed network, we must find a way to compute the cost function. Recently a theory based on the concept of quantization was developed. It can significantly reduce the complexity of cost computation. With this problem solved, cost‐based routing becomes a serious contending routing algorithm for a high‐speed multirate network. The results in the paper demonstrate that the performance degradation caused by quantizing the cost function is insignificant.
Performance analysis of speeded‐up high‐speed packet switches
Authors: Diwan Aniruddha S. , Guérin Roch , Sivarajan Kumar N.
Published: December 2024

In this paper, we study the performance of high‐speed packet switches, where the switch fabric operates at a slightly higher speed than the links, i.e., a speeded‐up switch. Such structures are by no means new and there are two well studied architectures in the literature for such packet switches: pure input queueing (no speedup) and pure output queueing (speedup of N, the number of links), with output queueing switches offering substantial performance benefits. However, as link speeds keep increasing, the speedup of N needed for pure output queueing becomes a significant technical challenge. This is one of the main reasons for the renewed interest in moderately speeded‐up switch fabrics. The aim of this paper is to highlight the result that only a moderate speed‐up factor (less than two) is sufficient to achieve full input link utilization. In particular, we emphasize that this holds, even without relying on a central switch controller making intelligent decisions on which packets to schedule through the switch. As shown in recent works, i.e., [5, 17, 20, 23, 25] there are clearly benefits to using intelligent controllers, but they do come at a cost. Instead, in this paper we focus on what can be achieved by relying simply on switch speedup. We do so by means of analysis and simulations. Our analysis provides explicit expressions for the average queue length in switches with integer and rational speedups. The results are complemented by simulations that are used to obtain delay estimates, and which also allow us to extend our investigation to shared memory switches for which we find that good performance can be achieved with an even lower speedup.
The design and performance of Mobile TCP for wireless networks
Authors: Haas Zygmunt J. , Warkhedi Abhijit
Published: December 2024

In this paper, we propose a novel approach to reduce the wireless communication overhead associated with the transport layer. Portability is a key element of mobile computing. As mobile devices shrink in terms of size and weight, their computing capabilities are also reduced, constrained by battery capacity. To sustain a good performance level of mobile applications, we devise a solution that reduces the processing complexity on computing‐limited mobile devices. Additionally, we aim to minimize the use of wireless resources to further improve performance. Specifically, we develop a streamlined protocol architecture, Mobile TCP (MTCP), that achieves the elimination of IP processing on the wireless segment of the TCP connection. MTCP operates over a single hop wireless link, and, consequently, it eliminates the unnecessary overhead placed on mobile devices, such as the TCP congestion control mechanisms. In addition, we investigate the impact of streamlining the socket layer on offloading the processing overhead. Our experimental results indicate a substantial improvement in the efficiency of protocol processing. For instance, results show that the MTCP processing time per packet is approximately only one fourth that of TCP and the use of CPU resources is reduced by up to 50%. Furthermore, the protocol incorporates various robust, yet simple, loss recovery techniques to considerably improve the throughput in lossy wireless conditions.
Next generation Tbit/sec routers and switches: traffic modeling, scheduling algorithms, and simulations
Authors: Sadot D. , Nachmani Y. , Bar‐Noy A. , Kutten S.
Published: December 2024

Scheduling algorithms for next generation multi Tbit/sec routers and switches are evaluated. Concurrently, available packet interconnections do not exceed a few hundreds Gbit/sec of aggregate throughput, making traffic modeling and computer simulations an extremely important tool. Here, traffic modeling is investigated in terms of arrival process and destination distribution. ‘Real world’ traffic models are proposed, and extensive simulations are carried out and compared to theoretical predictions. It is shown that conventional scheduling algorithms, such as round robin, perform very well under ‘convenient’ simulation assumptions, e.g., heavy load and uniform distribution assumptions. However, when traffic modeling is oriented towards modern applications such as bursty arrival process as in compressed video, or non‐uniform destination distribution as in Internet sites popularity, detailed and quantitative simulation results prove that algorithm performances are significantly degraded from over 95% to less than 60% of maximum throughput. A modified ‘adaptive’ round robin algorithm is proposed, yielding significant improvement of 20% in the router's maximum throughput.
On determining the fair bandwidth share for ABR connections in ATM networks
Authors: Fahmy Sonia , Jain Raj , Kalyanaraman Shivkumar , Goyal Rohit , Vandalore Bobby
Published: December 2024

In a multi‐service network such as ATM, adaptive data services (such as ABR) share the bandwidth left unused by higher priority services. The network indicates to the ABR sources the fair and efficient rates at which they should transmit to minimize their cell loss. Switches must constantly measure the demand and available capacity, and divide the capacity fairly among the contending connections. In this paper, we propose a new method for determining the “effective” number of active connections, and the fair bandwidth share for each connection. We prove the efficiency and fairness of the proposed method analytically, and use several simulations to illustrate its fairness dynamics and transient response properties.
Statistical study of crosstalk accumulation in WDM optical networks with different topology
Authors: Song Tongyu , Zhang Hanyi , Guo Yili , Zheng Xiaoping
Published: December 2024

The robustness of optical networks with respect to the accumulation of in‐band crosstalks was statistically studied in this paper, with emphases on the effect of network topology, identified by the network scale and physical connectivity. The in‐band crosstalks, a major and irreparable signal quality degrading factor, are induced by non‐ideal optical switches and demux/mux pairs at optical cross‐connect (OXC) in optical wavelength division multiplexing (WDM) networks. For the first time, our numerical simulations revealed that in a given network scale, there exists an optimum physical connectivity: the resultant overall accumulation of in‐band crosstalks can be minimized. Then the network scale was taken into account and the optimum network topology with respect to the accumulation of in‐band crosstalks was discussed for practical networks, with the limit of crosstalk ratio of the state‐of‐the‐art optical switches and demultiplexers. It was shown that proper network design can greatly relax the requirements posed on the optical switches and demultiplexers.
A new fractional channel policy
Authors: Beigy Hamid , Meybodi M.R.
Published: December 2024

Dropping probability of handoff calls and blocking probability of new calls are two important QoS measures for cellular networks. Since the dropping probability of handoff calls is more important, call admission policies are used to maintain the upper bound of dropping probability of handoff calls. The fractional channel policy (FC) is a general call admission policy and includes most prioritized channel allocation schemes such as guard channel (GC) and limited fractional channel (LFC) policies. In this paper, we introduce a subclass of fractional channel policy, called uniform fractional channel policy (UFC) and study its performance. Expressions for both dropping probability of handoff calls and blocking probability of new calls are derived. Then it is shown that there is an optimal value for the parameter of UFC, which minimizes the blocking probability of new calls with the constraint on the upper bound on the dropping probability of handoff calls. An algorithm for finding the optimal parameter of UFC is also given. Conditions under which the UFC performs better than GC is derived. It is concluded that, the UFC policy performs better than GC policy under the low handoff/new traffic ratio.
Path selection under multiple QoS constraints – a practical approach
Authors: Giladi Ran , Korach Ephraim , Ohayon Rony
Published: December 2024

Path selection under multiple additive QoS constraints in high‐speed networks is an NP‐complete problem. Most of the algorithms proposed for QoS routing suffer from either excessive computational complexities or low performance. In this paper, we propose two practical heuristic algorithms that are based on iterative shortest‐path computations with a dynamic cost function. The cost function comprises a combination of several QoS constraints and a single optimization metric. A simulation program evaluates the performance of the algorithms. The simulation results indicate that our algorithms give very high performance and fast running time.
A practice of a collaborative multipoint medical teleconsultation system on broadband network
Authors: Lee Chien‐Cheng , Chung Pau‐Choo , Duh Dyi‐Rong , Han Yunghsiang S. , Lin Chi‐Wei
Published: December 2024

The paper presents and discusses a collaborative teleconsultation system built on next generation network (NGN) transmission considerations. Considering the capability of merging medical images with multimedia data and real‐time video conferencing in transmission provided by NGN networks, the system is built with computer‐supported cooperative work (CSCW), DICOM standard, security functions, and image processing/analysis tools. The built‐in CSCW creates a collaborative consultation environment for synchronous interactive face‐to‐face discussion. The security functions provide the privacy and integrity in patient data transmission. The DICOM standard enables the medical image access to the PACS connecting with various imaging modalities. The image processing/analysis tools supported by CSCW functions provide useful tools for physicians to examine the images, and short‐code messages are defined to transmit the image operation command for maintaining the system consistency between users. These functions are discussed and tested on the NGN network transmission for its characteristics including transmission latency, jitter, data loss rate, and multicast performance. The experiments show that adopting the short‐code message drastically reduces the bandwidth requirement and also the user waiting time, under which the basic bandwidth requirement of the system during consultation is about 160 Kbps. The security functions occupy 92 ms and 83 ms for encryption and decryption, respectively, for a 518‐Kbytes image file. The multicast transmission technology is adopted to avoid the increase of network traffic when the number of participants expands. The experiments also show that the use of tunneling slightly increases overhead; however, the system can be deployed on the network without supporting IP multicasting via tunneling.
Efficient IP forwarding engine with incremental update
Authors: Hu Shuo‐Cheng , Chan Chia‐Tai , Chang Hung‐Yi , Wang Pi‐Chung
Published: December 2024

Nowadays, the commonly used table lookup scheme for IP routing is based on the so‐called classless interdomain routing (CIDR). With CIDR, routers must find out the best matching prefix (BMP) for IP packets forwarding, which complicates the IP lookup. Since the IP lookup performance is a major design issue for the new generation routers, in this article we investigate the properties of the routing table and present a new IP lookup scheme. By using the proposed scheme, the size of the forwarding table can be compressed to 360 Kbytes for a large routing table with 58 000 routing entries. The data structure for the incremental update is also introduced by adding 40% storage. The new data structure, could accomplish a single route update within 100 ns. Even where route flaps impede lookup performance, the performance degrades by only 0.05% with 4000 route updates per second. Furthermore, this scheme is IPv6 scalable.
On achieving short‐term QoS and long‐term fairness in high speed networks
Authors: Kim Myoung‐Hun , Park Hong‐Shik
Published: December 2024

We address the problem of short term QoS, caused by unexpected nature of traffic and expectation of high utilization, by using measurement based adaptive bandwidth control. We develop a short term QoS guaranteed scheduler, called short term QoS Deficit Round Robin (SQ‐DRR), which adapts the allocated bandwidth to guarantee delay constraints. The major appealing aspect of the scheduler is that it guarantees delay constraint for short term aggregate burst traffic violating pre‐contracted descriptors at the same time without losing long term fairness. We then evaluate the scheduler performance with and without admission control scheme under non‐stationary long‐range dependence traffic. The simulation results indicate that the SQ‐DRR performs well in single‐hop and multi‐hop network under dynamic burst traffic conditions.
Neural networks based variable bit rate traffic prediction for traffic control using multiple leaky bucket
Authors: Ouyang Yen Chieh , Yang Ching-Wen , Lian Wei Shi
Published: December 2024

This work presents a novel feedback rate regulator using the multiple leaky bucket (MLB) for variable bit rate (VBR) self-similar traffic that is based on the traffic load prediction by time-delayed neural networks in ATM networks. In the MLB mechanism, the leak rate and buffer capacity of each leaky bucket (LB) can be dynamically adjusted based on the buffer occupancy. A finite-duration impulse response (FIR) multilayer neural network is used to predict the incoming traffic load and pass the information to the feedback rate regulator. Ten real world MPEG1 and ten synthesized traffic traces are used to validate the performance of the MLB and the MLB with an FIR prediction mechanism. Simulation results demonstrate that the cell loss rate using MLB and MLB with an FIR filter-based predictor can be significantly reduced compare to the conventional leaky bucket method.
A scalable intra-domain resource management architecture for DiffServ networks
Authors: Mantar Haci A. , Okumus Ibrahim T. , Hwang Junseok , Chapin Steve J.
Published: December 2024

With the rapid growth of the Internet into a global communication and commercial infrastructure, the need for Quality of Services (QoS) in the Internet becomes more and more important. With a Bandwidth Broker (BB) support in each administrative domain Differentiated Services (DiffServ) is seen as a key technology for achieving QoS guarantees in a scalable, efficient, and deployable manner in the Internet.

This paper presents the design and implementation of a new Bandwidth Broker (BB) model to achieve QoS across a DiffServ domain. Our BB uses centralized network state maintenance and pipe-based intra-domain resource management schemes. The proposed model significantly reduces admission control time and minimizes scalability problems present in prior research while optimizing network resource utilization. The experimental results verify the achievements of our model.
Optical burst switching network: A multi-layered approach
Authors: Farahmand Farid , Vokkarane Vinod M. , Jue Jason P. , Rodrigues Joel J.P.C. , Freire Mário M.
Published: December 2024

This paper defines a new multi-layered architecture for supporting optical burst switching (OBS) in an optical core network. The architecture takes into account both the control plane as well as the data plane. The paper describes the functionality and the primary protocols that are required at each layer and it explains how the layers interact with each other in the proposed architecture. This paper also identifies some of the challenges and issues surrounding the development of OBS networks, such as data burst scheduling, contention resolution mechanism, supporting QoS, and handling TCP/IP, and provides a brief survey of proposed OBS protocols.
A priority-aware CSMA/CP MAC protocol for the all-optical IP-over-WDM metropolitan area ring network
Authors: Ho Jih-Hsin , Hwang Wen-Shyang , Shieh Ce-Kuen
Published: December 2024

The paper proposes a priority-aware MAC (Medium Access Control) protocol for a core metropolitan area network in the next generation Internet, which is an OPS (Optical Packet Switch) network that all-optically and directly transfers IP packets over a WDM (Wavelength Division Multiplexing) ring network. It uses the concepts of CSMA (Carrier Sense Multiple Access), CP (Carrier Preemption), and the priority mechanism to support all-optical and priority-aware transferring of the IP packets of the nodes in the WDM ring networks; the new MAC protocol is named priority-aware CSMA/CP. Since the traditional IP provides the best effort service only, supporting IP packets with QoS transfer has become a crucial issue for multimedia transmission. Today, while the network bandwidth has grown dramatically, the kind of applications transferred are mostly high-bandwidth demanding multimedia transmissions. It is predictable that the end-to-end QoS will be an important area of study in the next generation Internet. This paper accordingly proposes an advanced mechanism for this, and gives a differential service model to analyze and simulate the average packet delay for each class.
Slack allocation techniques for intra-path load balancing
Authors: Gopalan Kartik , Chiueh Tzi-cker , Lin Yow-Jian
Published: December 2024

Network resource provisioning techniques need to perform both inter-path and intra-path load balancing to maximize the network's resource usage efficiency while supporting end-to-end QoS guarantees. This paper focuses on the intra-path load balancing problem: How to partition the end-to-end QoS requirement of an aggregate network flow along the links of a given path such that the deviation in the loads on these links is as small as possible? We propose a set of new algorithms, called Load-balancing Slack Allocation (LSA), to solve this QoS partitioning problem for unicast and multicast traffic. The key concept in the LSA algorithms is the notion of slack, which quantifies the flexibility available in partitioning the end-to-end QoS requirement across the links of a selected path or tree. We show that one can improve network resource usage efficiency by carefully selecting a slack partition that explicitly balances the loads on the underlying links. These algorithms can simultaneously partition multiple QoS requirements such as delay and delay violation probability bounds. A detailed simulation study demonstrates that, compared with previous approaches, the LSA algorithms can increase the total number of long-term flows that can be provisioned along a network path by up to 1.2 times for deterministic and 2.8 times for statistical delay guarantees.
Scalable packet classification by TCAM entry encryption algorithm
Authors: Lee Chun-Liang , Wang Pi-Chung
Published: December 2024

Ternary content-addressable memory (TCAM) has been widely used to perform fast packet classification due to its ability of solving the problem in O(1) time without considering the number of entries, mask continuity and their lengths. However, it also comes with several shortcomings, such as the limited number of entries, expensive cost and power consumption. We propose an efficient algorithm to reduce the required TCAM bits by encoding the IP address portion of the policies. With the proposed scheme, the achieved compression ratio can be as small as 9% for IPv6 policies. Moreover, the TCAM throughput is improved by a factor of 175% with pipelining design. Obviously, the proposed scheme is attractive for the forthcoming IPv6.
Active scheduling for programmable routers in open adaptive network environments
Authors: Hussain S.A.
Published: December 2024

Active and Programmable networks change the functionality of intermediate nodes by using agents and active packets. This paper presents a novel packet scheduling scheme called Active Scheduling to maintain QoS in virtual private networks (VPNs) within the domain of adaptive and programmable networks. In Active Scheduling agents on the router monitor the accumulated queuing delay for each service. In order to keep the end-to-end delay within the bounds, the weights for services are adjusted dynamically by agents on the routers spanning the VPN. If there is a rise or fall in queuing delay of a service, an agent on a downstream router informs the upstream routers to alter the weights of their queues. This keeps the end-to-end delay of services within the defined bounds and offers better QoS compared to VPNs using static WFQ. The paper describes Active Scheduling, and presents simulation results and these are compared with WFQ.
Identitas matematis mahasiswa calon guru matematika: asal sekolah berbeda, identitas matematis berbedakah?
Authors: Kurniawati Annisa Dwi , Saputra Muhammad Rizqi
Published: December 2024

Mahasiswa calon guru matematika yang berasal dari asal sekolah berbeda sering kali menunjukkan minat, kompetensi, maupun partisipasi yang berbeda saat pembelajaran matematika. Perbedaan ini dapat menunjukkan dugaan bahwa identitas matematis yang mereka miliki bisa saja berkaitan dengan asal sekolah sebelumnya. Penelitian kuantitatif dengan teknik analisis data uji Kruskall Wallis dilakukan untuk mengidentifikasi apakah ada perbedaan identitas matematis yang dimiliki mahasiswa calon guru matematika dengan asal sekolah yang berbeda. Sebanyak 88 mahasiswa dari 3 asal sekolah berbeda yaitu MA, SMA, dan SMK dilibatkan sebagai responden dalam penelitian ini. Hasil penelitian menunjukkan bahwa meskipun nilai mean rank mahasiswa calon guru matematika dari ketiga asal sekolah berbeda, namun tidak ditemukan perbedaan identitas matematis yang signifikan secara statistik (nilai asymptotic sig 0,335 > 0,05) pada mahasiswa calon guru matematika yang berasal dari asal sekolah berbeda, termasuk untuk setiap komponen pembentuk identitas matematis. Oleh karena itu, dosen atau pendidik matematika diharapkan dapat menciptakan iklim pembelajaran yang kolaboratif tanpa membedakan asal sekolah mahasiswa demi tercapainya tujuan pembelajaran dan berkembangnya identitas matematis yang positif bagi mahasiswa calon guru matematika.