In this paper, we propose deliverable partitioning in prompt design to assist Large Language Models (LLMs) in improving response correctness for network design and configuration. While recent research has explored the use of LLMs to enhance network management efficiency, their responses often remain inconsistent, incomplete, or inaccurate. Often, LLM-generated configurations contain missing or erroneous configuration commands, which can lead to operational failures. Our proposed partitioning methodology aims to mitigate these issues by decomposing complex network configuration tasks into simplified and focused tasks. To evaluate the effectiveness of this approach, we introduce a scoring policy and conduct extensive experiments across three levels of network complexity and varying degrees of design choice ambiguity. We also compare the performance of leading LLMs, including ChatGPT, Copilot, and DeepSeek. Our findings indicate that partitioning the inquiry process leads to more accurate and consistent responses than non-partitioned approaches, especially in scenarios where design parameters are explicitly defined and leave some but small room, as ambiguity, for inference.

The flight time of uncrewed autonomous vehicles (UAVs) is constrained by its battery capacity, restricting its application in long-duration missions. To address this challenge, we propose eFlight, a hybrid scheme that uses a reinforcement-learning heuristic to augment A* for path finding. eFlight reduces both node expansions and computation time while finding energy-efficient paths in obstacle-dense 3D airspace. We compare eFlight with conventional path-planning algorithms for point-to-point flights on areas of various dimensions and with various obstacle densities. The results show that eFlight achieves a dual advantage: finding low-energy paths with short computation times. In high-density obstacle environment, eFlight identifies the lowest energy consumption path in 89.5% of the trials. Compared to the baseline scheme, eFlight reduces computation time by 90.6% ± 26.6% and energy by 7.13% ± 9.96%.

The impact of events and associated public health announcements on COVID-19 incidence remains an interesting and open question for future response and prevention. To address this issue, we propose a policy event impact assessment framework (PEAK) that quantifies the impact of policies and events on COVID-19 incidence in this paper. PEAK uses timeseries change point detection to estimate how health policies and events affected COVID-19 incidence during the most difficult period of the pandemic experienced in New York City and uses the long short-term memory for impact analysis at each change point. We analyze 26 public announcements on COVID19 and events that occurred in New York City from March 2020 to February 2021. The results show the top 10 largest-impact change points identified by PEAK and the events that caused such impacts.

Understanding key factors that affect users’ commute mode choice is essential to design policies that promote sustainable transportation. However, the reliance on survey data for these studies often faces incomplete data challenges. One of the regional transportation surveys obtained for the study on commute mode decision-making misses 97% of the parking cost data, an important factor in people’s decision-making. To tackle the problem, we propose the data imputation for cost estimates (DICE) scheme to synthesize data from multiple sources to infer the missing data. DICE linearly maps imputed values to missing entries based on the assumption that higher-income users can spend more on their commute. In the absence of ground truth data, we propose to use the accuracy of the regression model trained with the imputed data as a metric to evaluate DICE. We train the regression model with 75% of the imputed data, test it with the remainder, and evaluate it with the complete cases. The prediction accuracy of the test data and the evaluation data are 0.89 and 0.77, respectively. The results indicate that the imputed data and complete cases share similar distributions and the model trained with the imputed data can perform classification. We tested DICE using a 1995 transportation survey and a 2021 housing survey data sets where cost is considered a key feature in decision-making. In both cases, the regression model achieves higher than 0.7 prediction accuracy, which proves the applicability of DICE on different data sets.

Intersections of flight paths in multidrone missions are indications of a high likelihood of in-flight drone collisions. This likelihood can be proactively minimized during path planning. This article proposes two offline collision-avoidance multidrone path-planning algorithms: 1) DETACH and 2) STEER. Large drone tasks can be divided into smaller ones and carried out by multiple drones. Each drone follows a planned flight path that is optimized to efficiently perform the task. The path planning of the set of drones can then be optimized to complete the task in a short time, with minimum energy expenditure, or with maximum waypoint coverage. Here, we focus on maximizing waypoint coverage. Different from existing schemes, our proposed offline path-planning algorithms detect and remove possible in-flight collisions. They are based on a constrained nearest-neighbor search algorithm that aims to cover a large number of waypoints per flight path. DETACH and STEER perform vector intersection check for flight path analysis, but each at different stages of path planning. We evaluate the waypoint coverage of the proposed algorithms through a novel profit model and compare their performance on a work area with different waypoint densities. Our results show that STEER covers 40% more waypoints and generates 20% more profit than DETACH in high-density waypoint scenarios.

Detection of toxicity in online commentary is a growing branch of Natural Language Processing (NLP). Most research in the area rely only on text-based toxic comment detection. We propose a machine learning approach for detecting the toxicity of a comment by analyzing both the text and the emojis within the comment. Our approach utilizes word embeddings derived from GloVe and emoji2vec to train a bidirectional Long Short Term Memory (biLSTM) model. We also create a new labeled dataset with comments with text and emojis. The accuracy score of our model on preliminary data is 0.911.

Our research objective is to compare the effectiveness of standard online learning methods versus the utilization of virtual reality in education in terms of student focus and information retention. Our proposed platform will have identical lesson plans in virtual reality as our online learning methods. Eye gaze tracking and a recall test will be used on both platforms to measure focus on the screen and retention, respectively. The ultimate goal of the project is to use this data to evaluate the effectiveness of VR as a digital learning environment.

Unmanned Aerial Vehicles (UAVs) have been increasingly used in environmental sensing and surveying applications. Coverage path planning to survey an area while following a set of waypoints is required to complete a task. Due to the battery capacity, the UAV flight time is often limited. In this paper, we formulate the UAV path planning problem as a traveling salesman problem in order to optimize UAV energy. We propose a genetic algorithm to solve the optimization problem i.e. to minimize the energy consumption for the UAV to complete a task. We also consider reducing the number of turns to allow the UAV to optimize the flight path and to minimize its energy consumption. We compare the energy consumption of the proposed genetic algorithm to the greedy algorithm with different number of waypoints. Results show that our proposed algorithm consumes 2-5 times less energy than that of the greedy algorithm by reducing the number of turns while covering all the waypoints.

We propose a configuration scheme for a load-balancing Clos-network (LBC) packet switch that has split central modules and buffers in between the split modules. Our split-central-buffered LBC switch is cell-based. The switch has four stages, namely input, central-input, central-output, and output stages. The proposed configuration scheme uses a pre-determined and periodic interconnection pattern in the input and split central modules to load-balance and route traffic. The LBC switch has low configuration complexity. The operation of the switch includes a mechanism applied at input and split-central modules to forward cells in sequence. The switch achieves 100% throughput under uniform and nonuniform admissible traffic with independent and identical distributions (i.i.d.). The switch uses no speedup nor memory expansion. We demonstrate the properties of the switch through traffic and timing analysis.

Research conducted in the field of localization with passive light, or using the intrinsic properties of light to determine a person's location, has seen increased growth in recent years. Specifically, fluorescent lights have been shown to exhibit distinct frequencies which can be recorded, along with their positions, for future lookup and positioning. Developments have been made in utilizing this phenomenon with a smartphone's high-resolution back-facing camera, however the constant flipping between the camera and the screen results in a poor user experience. In this paper, we propose an algorithm for extracting and analyzing both loop-shaped and tubular fluorescent lights. Similarly, we contribute an improved method for detecting frequency characteristics of unmodified fluorescent lights using a smartphone's front facing camera, therefore eliminating the need to constantly flip the phone.

With the extensive use of smartphones, technology improving secure communication between smartphones is a growing field of research. As a form of Visible Light Communication, a color video barcode system creates a smartphone-tosmartphone communication channel. This color video barcode system, effectively an evolved form of QR codes, provides a secure alternative to WiFi, Bluetooth, and Near Field Communication. Recent improvements in smartphone screen resolution and camera capabilities allow for data transmission with larger amounts of information. In this paper, we investigate if these hardware changes will allow for improvements in data transmission over a screen-to-camera color barcode link. Our system, TEtra- TRansmISsion (TETRIS) achieves a communication throughput of 311.22 Kbps with 90% accuracy. We discuss how this could be improved with further work.

As transmission speeds increase faster than processing speeds, the packet processing time (PPT) of a host is becoming more significant in the measurement of different network parameters in which packet processing by the host is involved. The PPT of a host is the time elapsed between the arrival of a packet at the data-link layer and the time the packet is processed at the application layer (RFCs 2679 and 2681). To measure the PPT of a host, stamping the times when these two events occur is needed. However, time stamping at the data-link layer may require placing a specialized packet-capture card and the host under test in the same local network. This makes it complex to measure the PPT of remote end hosts. In this paper, we propose a scheme to measure the PPT of an end host connected over a single- or multiple-hop path and without requiring time stamping at the data-link layer. The proposed scheme is based on measuring the capacity of the link connected to the host under test. The scheme was tested on an experimental testbed and in the Internet, over a U.S. inter-state path and an international path between Taiwan and the U.S. We show that the proposed scheme consistently measures PPT of a host.

In this paper, we propose an optoelectronic switch that resolves contention in an optical switch through an electronic switch. The optoelectronic switch switches packets in the optical domain, and uses an electronic switch to store and forward packets that lose contention through other wavelengths. We investigate two modalities of the electronic switch, namely, local and global, and compare the performance of these modalities, in terms of packet loss rate. The simulation results show that the global modality achieves the lower packet loss rate. We also show that a small number of ports for wavelength conversion suffices to achieve a low packet loss rate with global modality.

Energy consumption of cloud data centers accounts for a major operational cost. This paper presents an optimization model for task scheduling to minimize task processing time and energy consumption in data centers for cloud computing. We formulate an integer programming optimization problem to minimize the expected energy consumption of homogenous tasks in a data center with a large number of servers and propose the most-efficient-server first greedy task scheduling algorithm to minimize energy expenditure. We show that the proposed task scheduling can minimize the energy expenditure while bounding the average task waiting time. We present a simulation of the proposed task scheduling scheme to show an optimum number of servers to achieve small task processing times and to minimize energy consumption.

This study presents a theoretical throughput analysis of two buffered-crossbar switches, called shared-memory crosspoint buffered (SMCB) switches, in which crosspoint buffers are shared by two or more inputs. In one of the switches, the shared-crosspoint buffers are dynamically partitioned and assigned to the sharing inputs, and memory is sped up. In the other switch, inputs are arbitrated to determine which of them accesses the shared-crosspoint buffers, and memory speedup is avoided. SMCB switches have been shown to achieve a throughput comparable to that of a combined input-crosspoint buffered (CICB) switch with dedicated crosspoint buffers to each input but, with less memory than a CICB switch. The two analysed SMCB switches use random selection as the arbitration scheme. The authors modelled the states of the shared-crosspoint buffers of the two switches using a Markov-modulated process and prove that the throughput of the proposed switches approaches 100% under independent and identically distributed uniform traffic. In addition, the authors provide numerical evaluations of the derived formulas to show how the throughput approaches asymptotically to 100%.

Out-of-sequence is a problem faced by multi-stage buffered Clos-network switches. This paper proposes two buffered three-stage Clos-network packet switches that service packets in sequence and provide high switching performance. The proposed switches require short configuration times as compared to existing bufferless or partially buffered Clos-network switches. The proposed switches use time stamps assigned at the input modules to identify the order of packets in the switch. The switches use time-stamp monitoring mechanisms either at the input modules in a switch called the MMM-IM switch, or at the output modules in a switch called the MMM-OM switch to keep packets in sequence. Synchronization among different switch modules is not required in the proposed switches. The switching performance study presented in this paper shows that in-sequence monitoring at the IM provides higher performance and larger scalability than in-sequence monitoring at the output. Furthermore, the throughput of the MMM-IM switch is comparable to that of a switch that may service packets out of sequence.

Combined input-crosspoint buffered (CICB) switches can achieve high switching performance without speedup. However, the dedicated crosspoint buffers in a CICB switch may not be efficiently used, and throughput degradation may occur. This throughput degradation is especially observable under flows with high data rates and long distances between the line cards and the buffered crossbar. This paper introduces two load-balanced CICB switches: the load-balancing CICB switch with full access (LB-CICB-FA) and the load-balancing CICB switch with single access (LB-CICB-SA). The proposed switches use the crosspoint buffers efficiently and support long distances between the line cards and buffered crossbar with crosspoint buffers smaller than those in a CICB switch by a factor of N, where N is the number of ports. It is proven that the LB-CICB-FA switch with random selection of the configuration of the load-balancing stage, input queues, and crosspoint queues is weakly stable under admissible independent and identical distributed (i.i.d.) traffic. Additional simulation results support the correctness of the theoretical analysis. Furthermore, it is shown that the throughput of the LB-CICB-SA switch with the longest-queue first (LQF) and first-come first-served (FCFS) as input and output arbitrations, respectively, is 100% under admissible i.i.d. traffic. The proposed switches keep cells in sequence and use no speedup. The low implementation complexity of the load-balancing stage is discussed and shown to be small.

The incorporation of broadcast and multimedia- on-demand services are expected to increase multicast traffic in packet networks, and therefore in switches and routers. Combined input-crosspoint buffered (CICB) switches can provide high performance under uniform multicast traffic, however, at the expense of N2crosspoint buffers. In this paper, we introduce an output-based shared-memory crosspoint-buffered (O-SMCB) packet switch where the crosspoint buffers are shared by two outputs and use no speedup. The proposed switch provides high performance under admissible uniform and nonuniform multicast traffic models while using 50% of the memory used in CICB switches. Furthermore, the O-SMCB switch provides higher throughput than an SMCB switch with buffers shared by inputs, or I-SMCB, previously proposed, despite the strong similarities between the architectures of these two switches. In this paper, we study the performance of the O-SMCB switch under uniform and nonuniform multicast traffic models and compare it to the I-SMCB switch.

As data rates in the Internet increase, the Internet Protocol (IP) address lookup is required to be resolved in shorter resolution times. IP address lookup involves finding the longest matching prefix from a database of prefixes that better matches the destination address of a packet. The fastest IP-address lookup solutions are based on ternary content addressable memories (TCAMs), which can resolve the IP lookup in one memory-access time. However, TCAMs have a high power consumption and large complexity that may limit their scalability and storage capacity. An alternative is to use random access memory (RAM) that stores a forwarding table in a trie form. Proposed trie-based solutions for IP lookup require three or more memory-access times in the worst-case scenario. This makes them unattractive despite their reduced power consumption. In this paper, we propose a flexible and fast trie-based IP-lookup algorithm where parallel searching is performed. This algorithm performs lookup in two memory- access times whith a feasible amount of memory or three memory access times with reduced memory.

Combined input-crosspoint buffered (CICB) packet switches with dedicated crosspoint buffers require a minimum amount of memory in the buffered crossbar of N2ldr k ldr L bytes, where N is the number of ports and k is the crosspoint buffer size, which is defined by the distance between the line cards and the buffered crossbar, and L is the cell (packet) size in bytes, to avoid buffer underflow under high-speed data flows. To support P traffic classes with different priorities, CICB switches requires N2ldrkldrLldrP bytes to avoid blocking of high priority cells. In this paper, we study a shared-memory crosspoint buffered packet switch that uses small crosspoint buffers and no speedup to support differentiated services and long distances between the line cards and the buffered crossbar in practical implementations. The proposed switch requires 1/m of memory amount in a CICB switch to achieve similar throughput performance.

Combined input-crosspoint buffered (CICB) packet switches relax arbitration timing and provide high-performance switching. However, the amount of memory in buffered crossbars required to achieve 100% throughput under flows with high data rates is proportional to the number of ports, N, and the crosspoint buffer size k, which is defined by the distance between the line cards and the buffered crossbar. Long distances between the line cards and the buffered crossbar can make a CICB switch costly to implement. In this paper, we propose a load-balanced CICB packet switch to support long distances between the buffered crossbar and the line cards using crosspoint buffers of small size. The proposed switch reduces the required crosspoint buffer size by a factor of N and keeps the cells in sequence.