我们审查当前的解决方案和技术挑战，以实现自动语音识别，关键字发现，设备仲裁，语音增强和在多边形家庭环境中的来源本地化，以为Interspeech 2022特别会议提供背景，“信号处理和机器学习的挑战和机器，用于多个智能设备”。我们还确定了支持这些研究领域所需的数据集。根据评论和我们在多设备领域的研究经验，我们以对未来进化的前景结论

我们认为具有非正度运动学的代理/机器人的问题避免了许多动态障碍。机器人和障碍物的状态和速度噪声以及机器人的控制噪声被建模为非参数分布，因为噪声模型的高斯假设被侵犯在现实世界中。在这些假设下，我们制定了一种强大的MPC，其以使机器人对准目标状态的方式有效地样本机器人控制，同时避免这种非参数噪声的胁迫下的障碍物。特别地，MPC包括分布匹配成本，其有效地将当前碰撞锥的分布对准到某个所需的分布，其样本是无碰撞的。这种成本在希尔伯特空间中作为距离功能构成，其最小化通常导致碰撞锥样品变得无碰撞。我们通过线性化原始非参数状态和障碍物分布的高斯近似来对比较和显示有形性能增益。我们还通过对非参数噪声的高斯近似构成的方法来表现出卓越的性能，而不会对进一步的线性提出进行这种近似的非参数噪声的高斯近似。性能增益在轨迹长度和控制成本方面都显示，其遵守所提出的方法的功效。据我们所知，这是在存在非参数状态，速度和致动器噪声模型存在下的运动障碍的第一次呈现。

准确的负载预测对于电力系统的电力市场运营以及电力系统中的其他实时决策任务至关重要。本文认为社区内的住宅客户的短期负荷预测（STLF）问题。现有的STLF工作主要侧重于预测馈线系统或单一客户的汇总负荷，但是在预测单个设备水平的负荷上，已经努力。在这项工作中，我们介绍了一种用于有效预测各个电器的功耗的STLF算法。所提出的方法在深度学习中强大的经常性神经网络（RNN）架构，称为长短短期记忆（LSTM）。当每个设备具有唯一重复的消耗模式时，将跟踪预测误差的模式，使得过去的预测误差可用于提高最终预测性能。实际负载数据集的数值测试证明了在现有的基于LSTM的方法和其他基准方法上提高了所提出的方法。

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.

Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69$

Periocular refers to the region of the face that surrounds the eye socket. This is a feature-rich area that can be used by itself to determine the identity of an individual. It is especially useful when the iris or the face cannot be reliably acquired. This can be the case of unconstrained or uncooperative scenarios, where the face may appear partially occluded, or the subject-to-camera distance may be high. However, it has received revived attention during the pandemic due to masked faces, leaving the ocular region as the only visible facial area, even in controlled scenarios. This paper discusses the state-of-the-art of periocular biometrics, giving an overall framework of its most significant research aspects.

Recently, Smart Video Surveillance (SVS) systems have been receiving more attention among scholars and developers as a substitute for the current passive surveillance systems. These systems are used to make the policing and monitoring systems more efficient and improve public safety. However, the nature of these systems in monitoring the public's daily activities brings different ethical challenges. There are different approaches for addressing privacy issues in implementing the SVS. In this paper, we are focusing on the role of design considering ethical and privacy challenges in SVS. Reviewing four policy protection regulations that generate an overview of best practices for privacy protection, we argue that ethical and privacy concerns could be addressed through four lenses: algorithm, system, model, and data. As an case study, we describe our proposed system and illustrate how our system can create a baseline for designing a privacy perseverance system to deliver safety to society. We used several Artificial Intelligence algorithms, such as object detection, single and multi camera re-identification, action recognition, and anomaly detection, to provide a basic functional system. We also use cloud-native services to implement a smartphone application in order to deliver the outputs to the end users.

As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire passage is either human-written or machine-generated. In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. We show that, while annotators often struggle at this task, there is substantial variance in annotator skill and that given proper incentives, annotators can improve at this task over time. Furthermore, we conduct a detailed comparison study and analyze how a variety of variables (model size, decoding strategy, fine-tuning, prompt genre, etc.) affect human detection performance. Finally, we collect error annotations from our participants and use them to show that certain textual genres influence models to make different types of errors and that certain sentence-level features correlate highly with annotator selection. We release the RoFT dataset: a collection of over 21,000 human annotations paired with error classifications to encourage future work in human detection and evaluation of generated text.

Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks. In this paper, we propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization - that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. We further collect human annotations of STRUDEL summaries over 400 dialogues and introduce a new STRUDEL dialogue comprehension modeling framework that integrates STRUDEL into a graph-neural-network-based dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension abilities. In our empirical experiments on two important downstream dialogue comprehension tasks - dialogue question answering and dialogue response prediction - we show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.

This thesis considers sequential decision problems, where the loss/reward incurred by selecting an action may not be inferred from observed feedback. A major part of this thesis focuses on the unsupervised sequential selection problem, where one can not infer the loss incurred for selecting an action from observed feedback. We also introduce a new setup named Censored Semi Bandits, where the loss incurred for selecting an action can be observed under certain conditions. Finally, we study the channel selection problem in the communication networks, where the reward for an action is only observed when no other player selects that action to play in the round. These problems find applications in many fields like healthcare, crowd-sourcing, security, adaptive resource allocation, among many others. This thesis aims to address the above-described sequential decision problems by exploiting specific structures these problems exhibit. We develop provably optimal algorithms for each of these setups with weak feedback and validate their empirical performance on different problem instances derived from synthetic and real datasets.