Automatic speech recognition (ASR) models rely on high-quality transcribed data for effective training. Generating pseudo-labels for large unlabeled audio datasets often relies on complex pipelines that combine multiple ASR outputs through multi-stage processing, leading to error propagation, information loss and disjoint optimization. We propose a unified multi-ASR prompt-driven framework using postprocessing by either textual or speech-based large language models (LLMs), replacing voting or other arbitration logic for reconciling the ensemble outputs. We perform a comparative study of multiple architectures with and without LLMs, showing significant improvements in transcription accuracy compared to traditional methods. Furthermore, we use the pseudo-labels generated by the various approaches to train semi-supervised ASR models for different datasets, again showing improved performance with textual and speechLLM transcriptions compared to baselines.

Aligning Learning Outcomes (LO) to relevant portions of Learning Resources (LR) is necessary to help students quickly navigate within the recommended learning material. In general, the problem can be viewed as finding the relevant sections of a document (LR) that is pertinent to a broad question (LO). In this paper, we introduce the novel problem of aligning LOs (LO is usually a sentence long text) to relevant pages of LRs (LRs are in the form of slide decks). We observe that the set of relevant pages can be composed of multiple chunks (a chunk is a contiguous set of pages) and the same page of an LR might be relevant to multiple LOs. To this end, we develop a novel Lexico-Semantic Spatial approach that captures the lexical, semantic, and spatial aspects of the task, and also alleviates the limited availability of training data. Our approach first identifies the relevancy of a page to an LO by using lexical and semantic features from each page independently. The spatial model at a later stage exploits the dependencies between the sequence of pages in the LR to further improve the alignment task. We empirically establish the importance of the lexical, semantic, and spatial models within the proposed approach. We show that, on average, a student can navigate to a relevant page from the first predicted page by about four clicks within a 38 page slide deck, as compared to two clicks by human experts.

Voice forums are an effective intervention medium for marginalized communities to access information in a structured and localized manner. Users actively contribute by posting questions and responses in the form of audio messages, and thereby help in enriching the voice forum content. In order to build an audio library using the voice forums to disseminate information, significant manual effort is needed in analyzing and curating the data. This is one of the key impediments to the successful implementation of voice forums for knowledge dissemination and training.

We report on a study of affective states of learners in a Massive Open Online Course (MOOC) and the interplay of Affect, Behavior and Cognition at various stages of the course. Affect is measured through a series of self-reports from learners at strategic time posts during the period of study. Behavior is characterized in terms of a learners' engagement, interactivity, impatience and reflectivity, which constitute a set of novel high-level features derived from the clickstream of learner interactions. Cognition is evaluated from the performance of learners on assessments that are part of the course. We discover that learners in the MOOC experience multiple as well as mixed emotions as they go through the course, which we handle using the psychological dimensions of arousal and valence. This results in a set of emotional quadrants, whose co-occurrence analysis reveals a strong association with cognition and specific behavioral characteristics demonstrated by the learner. These results advance our understanding of the experience of MOOC learners to a more holistic level across the key dimensions of affect, behavior and cognition. They also have important implications for the design of the next generation MOOCs that can potentially leverage affect and behavior-aware interventions to drive greater personalization and eventually, improved learning outcomes.

This paper presents affect data collected from periodic emotion detection surveys throughout an introductory Statistics MOOC called "I Heart Stats." This is the first MOOC, to our knowledge, to capture valuable student affect data through self-reported surveys. To collect student affect, we used two self-reporting methods: (1) The Self-Assessment Manikin and (2) A discrete emotion list. We found that the most common reported MOOC emotion was Hope followed by Enjoyment and Contentment. There were substantial shifts in affective states over the course, notably with Anxiety and Pride. The most valuable result of our study is a preliminary description of the methods for collecting self-reported student affect at scale in a MOOC setting.

PESOS has established itself as a forum that brings together software engineering researchers and practitioners working in the areas of service-oriented systems to discuss research challenges, new developments and applications, as well as methods, techniques, experiences, and tools to support engineering, evolution and adaptation of service-oriented systems. The technical advances and growing adoption of Cloud computing is creating new challenges for the PESOS the software services community to explore the approaches to better engineer software systems that are designed, developed, operated and governed in the context of the Cloud. We again attracted high-quality submissions on a diverse set of relevant topics such as better approaches to engineering service-based collaborative systems, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) models of cloud computing and associated software quality attributes. PESOS 2015 will continue to be the key forum for collecting case studies and artifacts for educators and researchers in this area.

Managing the Quality-of-Experience (QoE) of video streaming for wireless clients is becoming increasingly important due to the rapid growth of video traffic on wireless networks. The inherent variability of the wireless channel as well as the Variable Bit Rate (VBR) of the compressed video streams make QoE management a challenging problem. In this paper, we investigate scheduling algorithms to transmit multiple video streams from a base station to mobile clients. We present an epoch-by-epoch framework to fairly allocate wireless transmission slots to streaming videos. In each epoch, our scheme reduces the vulnerability to stalling by allocating slots to videos in a way that maximizes the minimum “playout lead” across all videos. We show that the problem of allocating slots fairly is NP-complete even for a constant number of videos. We then present a fast lead-aware greedy scheduling algorithm. Our greedy algorithm is optimal when the channel quality of a user remains unchanged within an epoch. Our experimental results, based on public MPEG-4 video traces and wireless channel traces that we collected from a WiMAX test-bed, show that the lead-aware greedy approach results in a fair distribution of stalls across the clients when compared to other algorithms, while still maintaining similar or fewer average number of stalls per client.

TV Whitespaces, recently opened up by the Federal Communications Commission (FCC) for unlicensed use, are seen as a potential cellular offload and/or standalone mechanism, especially in dense metros where the demand for throughput is high. In this paper, we use real data collected from whitespaces databases to empirically demonstrate features unique to whitespaces-power-spectrum tradeoff and spatial variation in spectrum availability. From this study, we conclude the need for whitespaces-specific adaptations to cellular networks so as to be able to extract maximum throughput and guarantee reliability. To tackle the effects of the power-spectrum tradeoff, we propose a novel base-station design that specifically uses low-power transmitters as a means to maximize throughput. This design co-locates and networks together many low-powered mode-I devices to act as a multiple-antenna array. We estimate the size of the array required to meet typical rate targets, and show that the array design significantly outperforms traditional designs in terms of throughput for a given cost. We then turn our attention to spatial variability and study its impact on the problem of locating base stations in a whitespaces network. Here, we propose spectrum-aware placement algorithms for whitespaces, which account for this spatial variability along with key parameters like user density. We show that such algorithms clearly outperform traditional placement algorithms and improve network coverage in this band.

Mobile browsing habits are characteristically different from browsing on traditional devices. Mobile users often look for information snippets instead of complete web pages. Also, mobile devices are often constrained in terms of resource availability, such as battery, data plan limits and network bandwidth. Under such constraints, partial-loading of a web page, by loading the most relevant content snippets early, can satisfy the user and consume less resources. Mobile content adaptation middleware has traditionally focused on user factors, such as user feedbacks and user context. We believe that the content creator, with complete knowledge of the importance of each item in the web page, is well-suited to guide the adaptation process. Combining user choice, and ratings assigned by the content creator to different web page elements (items), enables delivering the most relevant items in an ordered manner in response to a page request. We present ScoDA, a cooperative content adaptation middleware framework, under resource constraints. We evaluate the effectiveness of page loading using ScoDA, on simulated complex web pages as well as real web pages.

We conduct an extensive measurement study in an operational cellular network in dense urban settings and identify two key sources of inefficiencies in standard 3GPP handoff technique: wrong timing of handoff and sub-optimal base station association at a point. Based on our study, we propose a novel handoff technique that leverages (1) historical geo-tagged signal strength data from visible base stations at typical mobile user locations and (2) knowledge of the user trajectory. First, we formulate the handoff problem as a bi-criteria optimization problem and present a dynamic programming formulation that provides pareto-optimal solutions that maximize overall signal strength along user trajectory for a range of handoffs. Using an efficient utility based selection technique, we select a solution that balances the tradeoff between reducing number of handoffs and increasing the overall signal strength experienced by the user. Next, we present a much faster greedy heuristic that augments the solution obtained from the dynamic programming formulation when optimizing user performance in the presence of dynamic network load at each base station. A key contribution of our work is to show that striking a tradeoff between the two objectives is much harder in dense macro cellular base station deployments and an effective handoff technique should balance both these objectives well. We show that our technique takes a well balanced approach and achieves more than 25% reduction in the number of handoffs over the standard 3GPP handoff technique in an operational cellular network without incurring reduction in average signal strength, and even improving it by 1dB/sec in some cases. Finally, we propose a novel 3GPP standards compliant trajectory-aware location based handoff protocol and discuss the practical implementation aspects in cellular networks.

In this paper, we study the problem of efficient video delivery over the cellular downlink. The key objective is to maximize the Quality of Experience (QoE) of the user, as measured by application level metrics such as the buffering ratio and low bit rate ratio. We present a two-tiered solution with a standard base-station scheduler that works on a per-packet basis and a Video Management System (VMS) that works at the granularity of thousands of video frames. The video management system uses knowledge of the video playout curves and future channel states to develop a scheduling policy that is feasibility optimal. The algorithms are simple and leverage recent results on real-time scheduling in wireless networks. We evaluate the performance of our algorithms using real video traces and a standard channel model. The VMS ensures that the per-user QoE guarantees are maintained, as compared with a standard PF scheduler that is oblivious to application level QoE requirements.

The Wireless Network Cloud (WNC) [1] is a novel network architecture where wireless base-stations are implemented as software modules and multiple base-stations are consolidated to a single centralized computing platform. Due to the time-varying and random nature of base-station traffic, consolidation leads to multiplexing of statistically-varying basestation loads on a common hardware platform. In turn, this can lead to significant hardware reduction in the consolidated platform when compared to the distributed network. This paper represents the first analysis of such consolidation gain. Through traffic simulation experiments, we quantify the extent and variation of this multiplexing gain in a WiMAX base-station network in different traffic conditions. We show experimentally, that the obtained gain increases linearly with network size (number of base-stations). Further, we also show that the consolidation gain is higher when the consolidated base-stations face higher traffic intensity.

Managing the Quality-of-Experience (QoE) of video streaming for wireless clients is becoming increasingly important due to the rapid growth of video traffic on wireless networks. The inherent variability of the wireless channel as well as the Variable Bit Rate (VBR) of the compressed video streams make QoE management a challenging problem. Prior work has studied this problem in the context of transmitting a single video stream. In this paper, we investigate multiplexing schemes to transmit multiple video streams from a base station to mobile clients that use number of playout stalls as a performance metric. In this context, we present an epoch-by-epoch framework to fairly allocate wireless transmission slots to streaming videos. In each epoch our scheme essentially reduces the vulnerability to stalling by allocating slots to videos in a way that maximizes the minimum `playout lead' across all videos. Next, we show that the problem of allocating slots fairly is NP-complete even for a constant number of videos. We then present a fast lead-aware greedy algorithm for the problem. Our choice of greedy algorithm is motivated by the fact that this algorithm is optimal when the channel quality of a user remains unchanged within an epoch (but different users may experience different channel quality). Moreover, our experimental results based on public MPEG-4 video traces and wireless channel traces that we collected from a WiMAX test-bed show that the lead-aware greedy approach performs a fair distribution of stalls across the clients when compared to other algorithms, while still maintaining similar or lower average number of stalls per client.

TV Whitespaces, recently opened up by the FCC for unlicensed use by wireless devices, are seen as a potential cellular offload solution, especially in dense metros. However, under the new database-driven guidelines, there are typically very few whitespace bands available in such dense metros to a high-powered fixed device, which plays the role of a cellular base station in whitespaces. To address the lack of degrees-of-freedom (DoF) with this traditional architecture of one high-powered serving device, we propose a novel base station design that co-locates and networks together many low-powered devices to act as a multiple-antenna array. Lower-powered whitespace devices have access to more spectral DoF, a property that is unique to whitespaces. In the first part of the paper, we solve an array design problem where we estimate the size of the array required to meet long-term (worst-case) throughput targets. Using extensive simulations, we show that by effectively exploiting both spatial and spectral DoF, the array design outperforms the traditional design in most network conditions. Specifically, the proposed design can support throughputs of the order of a WiMAX cell running applications such as high-definition television. In the second part of the paper, we turn our attention to the operational aspects of such a design. Recognizing that the proposed array can potentially contain hundreds of elements, we propose a dynamic ON-OFF power control algorithm that operates in conjunction with the MaxWeight data scheduling algorithm and responds to the current network state - queues and channels - of the system, thus making the system power-efficient.

Video objects are much larger in size than traditional web objects and tend not to be viewed in entirety. Hence, caching them partially is a promising approach. Also, the projected growth in video traffic over wireless cellular networks calls for resource-efficient caching mechanisms in the wireless edge to lower traffic over the cellular backhaul and peering links and their associated costs. An evaluation of traditional partial caching solutions proposed in the literature shows that known solutions are not robust to video viewing patterns, increasing object pool size, changing object popularity, or limitation in the resources available for caching at the wireless network elements. In this paper, to overcome the limitations, we propose a novel approach that adopts a flexible segmentation policy and generalizes both LRU and LFU when applied to segmented accesses, and in our simulations, is shown to significantly lower wireless backhaul traffic (by around 20-30% and in some cases even higher).

We consider lifetime quality-of-experience (QoE) management for video-on-demand (VoD) users in WiMAX networks. For efficient resource utilization while enhancing user experience, we propose run-time load balancing through joint scheduling among multiple base stations (BSs), referred to as macro scheduling. Macro scheduling employs a utility-maximization approach. To achieve long-term proportional fairness (PF) and manage lifetime QoE, user and flow utilities are modeled as functions of their past service rates, in addition to current channel conditions and bandwidth needs. We show that scheduling flows across multiple BSs jointly to achieve PF is NP-hard in the strong sense. Since approximation algorithms proposed in prior work are computationally expensive for online use, we design efficient heuristics that perform as well as the approximation algorithms. A simulation-based evaluation shows that our overall macro scheduling scheme can improve the number of satisfied users by up to 35% in comparison to other approaches, while only minimally sacrificing on throughput.

Abstract Time Warp distributed simulations can enter a catastrophic state of cascading rollbacks where out of order (premature) event executions propagate faster than the corrective measures (anti‐messages) designed to terminate them. In this paper, a distributed, proactive cancellation mechanism designed to avoid this situation is presented. We also present a version of total clocks calledPlausible Total Clocks(PTC). PTC employs a compact time vector ofevent counter rangeto address the scalability issues typically suffered by total clock solutions. That is, PTC consists of constant size vectors and are independent of the number of simulation objects in the simulation. The events generated due to an out of order execution are proactively cancelled by determining its causality relation with the already annihilated events. We present a proof of correctness of the distributed cancellation mechanism using PTC and also show that the catastrophic states are avoided with this proactive cancellation mechanism. This cancellation mechanism assumes first‐input–first‐output communication layer, static inter‐connection topology, and logical processes consisting of multiple simulation objects. Copyright © 2009 John Wiley & Sons, Ltd.

Optimistic time warp simulators should stop the rapid propagation of incorrect events to avoid reaching a catastrophic state (a state where out of order event execution is always a step ahead of its corrective measures). A distributed cancellation mechanism using total clocks was proposed earlier to avoid such catastrophic states. In this paper, we present a proactive cancellation mechanism using a vector of event counter range to address the scalability issues with the previously defined solution. As opposed to total clocks, this timestamp mechanism (a.k.a. plausible total clocks) consists of constant size vectors and are independent of the number of simulation objects in the simulation. The events generated due to an out of order execution are pro-actively canceled by determining its causality relation with the already annihilated events. We present a proof of correctness of the distributed cancellation mechanism and also show that catastrophic states are avoided with this proactive cancellation mechanism. This cancellation mechanism assumes FIFO communication layer, static inter-connection topology, and logical processes consisting of several simulation objects

In time warp simulations, the overheads associated with rollbacks, state-saving and the communication induced by rollbacks are the chief contributors to the cost of the simulation; thus, these aspects of the simulation have been primary targets for optimizations. Unfortunately, the behavior of the time warp simulation is highly dynamic and greatly influenced by the application being simulated. Thus, the suggested optimizations are only effective for certain intervals of the simulation. This paper argues that the performance of time warp simulators benefits from a dynamic on-line decision process that selects and configures the sub-algorithms implementing the different aspects of the simulator to best match the current behavior of the simulation. In particular we study control strategies to dynamically: (i) adjust the checkpointing (or state-saving) interval (ii) select the cancellation strategy (lazy or aggressive), and (iii) determine the policy for aggregating the application messages (an optimization that significantly improves the performance in message passing environments). The strategies have been implemented in the WARPED time warp simulation kernel and the performance obtained via the dynamically controlled optimizations is shown to surpass that of their best performing static counterparts.