Crowdsourced speedtest measurements are an important tool for studying internet performance from the end user perspective. Nevertheless, despite the accuracy of individual measurements, simplistic aggregation of these data points is problematic due to their intrinsic sampling bias. In this work, we utilize a dataset of nearly 1 million individual Ookla Speedtest measurements, correlate each datapoint with 2019 Census demographic data, and develop new methods to present a novel analysis to quantify regional sampling bias and the relationship of internet performance to demographic profile. We find that the crowdsourced Ookla Speedtest data points contain significant sampling bias across different census block groups based on a statistical test of homogeneity. We introduce two methods to correct the regional bias by the population of each census block group. Whereas the sampling bias leads to a small discrepancy in the overall cumulative distribution function of internet speed in a city between estimation from original samples and bias-corrected estimation, the discrepancy is much smaller compared to the size of the sampling heterogeneity across regions. Further, we show that the sampling bias is strongly associated with a few demographic variables, such as income, education level, age, and ethnic distribution. Through regression analysis, we find that regions with higher income, younger populations, and lower representation of Hispanic residents tend to measure faster internet speeds along with substantial collinearity amongst socioeconomic attributes and ethnic composition. Finally, we find that average internet speed increases over time based on both linear and nonlinear analysis from state space models, though the regional sampling bias may result in a small overestimation of the temporal increase of internet speed.
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The IoT's vulnerability to network attacks has motivated the design of intrusion detection schemes (IDS) using Machine Learning (ML), with a low computational cost for online detection but intensive offline learning. Such IDS can have high attack detection accuracy and are easily installed on servers that communicate with IoT devices. However, they are seldom evaluated in realistic operational conditions where IDS processing may be held up by the system overload created by attacks. Thus we first present an experimental study of UDP Flood Attacks on a Local Area Network Test-Bed, where the first line of defence is an accurate IDS using an Auto-Associative Dense Random Neural Network. The experiments reveal that during severe attacks, the packet and protocol management software overloads the multi-core server, and paralyses IDS detection. We therefore propose and experimentally evaluate an IDS design where decisions are made from a very small number of incoming packets, so that attacking traffic is dropped within milli-seconds after an attack begins and the paralysing effect of congestion is avoided.
Search result snippets are crucial in modern search engines, providing users with a quick overview of a website's content. Snippets help users determine the relevance of a document to their information needs, and in certain scenarios even enable them to satisfy those needs without visiting web documents. Hence, it is crucial for snippets to reliably represent the content of their corresponding documents. While this may be a straightforward requirement for some queries, it can become challenging in the complex domain of healthcare, and can lead to misinformation. This paper aims to examine snippets' reliability in representing their corresponding documents, specifically in the health domain. To achieve this, we conduct a series of user studies using Google's search results, where participants are asked to infer viewpoints of search results pertaining to queries about the effectiveness of a medical intervention for a medical condition, based solely on their titles and snippets. Our findings reveal that a considerable portion of Google's snippets (28%) failed to present any viewpoint on the intervention's effectiveness, and that 35% were interpreted by participants as having a different viewpoint compared to their corresponding documents. To address this issue, we propose a snippet extraction solution tailored directly to users' information needs, i.e., extracting snippets that summarize documents' viewpoints regarding the intervention and condition that appear in the query. User study demonstrates that our information need-focused solution outperforms the mainstream query-based approach. With only 19.67% of snippets generated by our solution reported as not presenting a viewpoint and a mere 20.33% misinterpreted by participants. These results strongly suggest that an information need-focused approach can significantly improve the reliability of extracted snippets in online health search.
The ability for users to access quantum computers through the cloud has increased rapidly in recent years. Despite still being Noisy Intermediate-Scale Quantum (NISQ) machines, modern quantum computers are now being actively employed for research and by numerous startups. Quantum algorithms typically produce probabilistic results, necessitating repeated execution to produce the desired outcomes. In order for the execution to begin from the specified ground state each time and for the results of the prior execution not to interfere with the results of the subsequent execution, the reset mechanism must be performed between each iteration to effectively reset the qubits. However, due to noise and errors in quantum computers and specifically these reset mechanisms, a noisy reset operation may lead to systematic errors in the overall computation, as well as potential security and privacy vulnerabilities of information leakage. To counter this issue, we thoroughly examine the state leakage problem in quantum computing, and then propose a solution by employing the classical and quantum one-time pads before the reset mechanism to prevent the state leakage, which works by randomly applying simple gates for each execution of the circuit. In addition, this work explores conditions under which the classical one-time pad, which uses fewer resources, is sufficient to protect state leakage. Finally, we study the role of various errors in state leakage, by evaluating the degrees of leakage under different error levels of gate, measurement, and sampling errors. Our findings offer new perspectives on the design of reset mechanisms and secure quantum computing systems.
We present a technique for controlling physically simulated characters using user inputs from an off-the-shelf depth camera. Our controller takes a real-time stream of user poses as input, and simulates a stream of target poses of a biped based on it. The simulated biped mimics the user's actions while moving forward at a modest speed and maintaining balance. The controller is parameterized over a set of modulated reference motions that aims to cover the range of possible user actions. For real-time simulation, the best set of control parameters for the current input pose is chosen from the parameterized sets of pre-computed control parameters via a regression method. By applying the chosen parameters at each moment, the simulated biped can imitate a range of user actions while walking in various interactive scenarios.
Video quality can suffer from limited internet speed while being streamed by users. Compression artifacts start to appear when the bitrate decreases to match the available bandwidth. Existing algorithms either focus on removing the compression artifacts at the same video resolution, or on upscaling the video resolution but not removing the artifacts. Super resolution-only approaches will amplify the artifacts along with the details by default. We propose a lightweight convolutional neural network (CNN)-based algorithm which simultaneously performs artifacts reduction and super resolution (ARSR) by enhancing the feature extraction layers and designing a custom training dataset. The output of this neural network is evaluated for test streams compressed at low bitrates using variable bitrate (VBR) encoding. The output video quality shows a 4-6 increase in video multi-method assessment fusion (VMAF) score compared to traditional interpolation upscaling approaches such as Lanczos or Bicubic.
Additive spatial statistical models with weakly stationary process assumptions have become standard in spatial statistics. However, one disadvantage of such models is the computation time, which rapidly increases with the number of data points. The goal of this article is to apply an existing subsampling strategy to standard spatial additive models and to derive the spatial statistical properties. We call this strategy the "spatial data subset model" (SDSM) approach, which can be applied to big datasets in a computationally feasible way. Our approach has the advantage that one does not require any additional restrictive model assumptions. That is, computational gains increase as model assumptions are removed when using our model framework. This provides one solution to the computational bottlenecks that occur when applying methods such as Kriging to "big data". We provide several properties of this new spatial data subset model approach in terms of moments, sill, nugget, and range under several sampling designs. An advantage of our approach is that it subsamples without throwing away data, and can be implemented using datasets of any size that can be stored. We present the results of the spatial data subset model approach on simulated datasets, and on a large dataset consists of 150,000 observations of daytime land surface temperatures measured by the MODIS instrument onboard the Terra satellite.
As the cost of training ever larger language models has grown, so has the interest in reusing previously learnt knowledge. Transfer learning methods have shown how reusing non-task-specific knowledge can help in subsequent task-specific learning. In this paper, we investigate the inverse: porting whole functional modules that encode task-specific knowledge from one model to another. We designed a study comprising 1,440 training/testing runs to test the portability of modules trained by parameter-efficient finetuning (PEFT) techniques, using sentiment analysis as an example task. We test portability in a wide range of scenarios, involving different PEFT techniques and different pretrained host models, among other dimensions. We compare the performance of ported modules with that of equivalent modules trained (i) from scratch, and (ii) from parameters sampled from the same distribution as the ported module. We find that the ported modules far outperform the two alternatives tested, but that there are interesting performance differences between the four PEFT techniques. We conclude that task-specific knowledge in the form of structurally modular sets of parameters as produced by PEFT techniques is highly portable, but that degree of success depends on type of PEFT and on differences between originating and receiving pretrained models.
In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors/uncertainties and external disturbances. However, MPC's sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100\% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics.
Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing more effective models in various scenarios, the exploration on the explainability of recommender systems is running behind. Explanations could help improve user experience and discover system defects. In this paper, after formally introducing the elements that are related to model explainability, we propose a novel explainable recommendation model through improving the transparency of the representation learning process. Specifically, to overcome the representation entangling problem in traditional models, we revise traditional graph convolution to discriminate information from different layers. Also, each representation vector is factorized into several segments, where each segment relates to one semantic aspect in data. Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge. In this way, the proposed model can learn interpretable and meaningful representations for users and items. Unlike traditional methods that need to make a trade-off between explainability and effectiveness, the performance of our proposed explainable model is not negatively affected after considering explainability. Finally, comprehensive experiments are conducted to validate the performance of our model as well as explanation faithfulness.
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research.
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