Misinformation spread over social media has become an undeniable infodemic. However, not all spreading claims are made equal. If propagated, some claims can be destructive, not only on the individual level, but to organizations and even countries. Detecting claims that should be prioritized for fact-checking is considered the first step to fight against spread of fake news. With training data limited to a handful of languages, developing supervised models to tackle the problem over lower-resource languages is currently infeasible. Therefore, our work aims to investigate whether we can use existing datasets to train models for predicting worthiness of verification of claims in tweets in other languages. We present a systematic comparative study of six approaches for cross-lingual check-worthiness estimation across pairs of five diverse languages with the help of Multilingual BERT (mBERT) model. We run our experiments using a state-of-the-art multilingual Twitter dataset. Our results show that for some language pairs, zero-shot cross-lingual transfer is possible and can perform as good as monolingual models that are trained on the target language. We also show that in some languages, this approach outperforms (or at least is comparable to) state-of-the-art models.

自然语言处理的最新进展在文本分析和语言理解模型中产生了许多令人兴奋的发展。但是，这些模型也可以用于跟踪人们，引起严重的隐私问题。在这项工作中，我们调查了个人可以在使用社交媒体平台时避免被这些模型检测到的事情。我们将调查在两项曝光危险任务，立场检测和地理标记中进行。我们探索了各种用于修改文本的简单技术，例如用显着词，​​释义和添加虚拟社交媒体帖子插入错别字。我们的实验表明，基于BERT的模型的性能因错别字而被罚款以进行立场检测，但不受释义的影响。此外，我们发现错别字对最先进的地理参考模型的影响最小，因为它们对社交网络的依赖增加了。但是，我们表明用户可以通过与不同的用户互动来欺骗这些模型，从而将其绩效降低了近50％。

The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human `receptors' and therefore blurs the difference of virtual and real environments. We commence by highlighting the compelling use cases empowered by the IoS and also the key network requirements. We then elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms along with 6G technologies may satisfy the requirements of IoS use cases. On one hand, semantic communications can be applied for extracting meaningful and significant information and hence efficiently exploit the resources and for harnessing a priori information at the receiver to satisfy IoS requirements. On the other hand, AI/ML facilitates frugal network resource management by making use of the enormous amount of data generated in IoS edge nodes and devices, as well as by optimizing the IoS performance via intelligent agents. However, the intelligent agents deployed at the edge are not completely aware of each others' decisions and the environments of each other, hence they operate in a partially rather than fully observable environment. Therefore, we present a case study of Partially Observable Markov Decision Processes (POMDP) for improving the User Equipment (UE) throughput and energy consumption, as they are imperative for IoS use cases, using Reinforcement Learning for astutely activating and deactivating the component carriers in carrier aggregation. Finally, we outline the challenges and open issues of IoS implementations and employing semantic communications, edge intelligence as well as learning under partial observability in the IoS context.

Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly expand the functionality and provide seamless dialogue experience. The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs. The model also manages the dialogue policy and interact with the user through generating appropriate natural language responses. By allowing generating free-form programs, Dialog2API supports composite goals by combining different APIs, whereas unrestricted program revision provides natural and robust dialogue experience. To facilitate Dialog2API, the core model is provided with API documents, an execution environment and optionally some example dialogues annotated with programs. We propose an approach tailored for the Dialog2API, where the dialogue states are represented by a stack of programs, with most recently mentioned program on the top of the stack. Dialog2API can work with many application scenarios such as software automation and customer service. In this paper, we construct a dataset for AWS S3 APIs and present evaluation results of in-context learning baselines.

Privacy protection and nonconvexity are two challenging problems in decentralized optimization and learning involving sensitive data. Despite some recent advances addressing each of the two problems separately, no results have been reported that have theoretical guarantees on both privacy protection and saddle/maximum avoidance in decentralized nonconvex optimization. We propose a new algorithm for decentralized nonconvex optimization that can enable both rigorous differential privacy and saddle/maximum avoiding performance. The new algorithm allows the incorporation of persistent additive noise to enable rigorous differential privacy for data samples, gradients, and intermediate optimization variables without losing provable convergence, and thus circumventing the dilemma of trading accuracy for privacy in differential privacy design. More interestingly, the algorithm is theoretically proven to be able to efficiently { guarantee accuracy by avoiding} convergence to local maxima and saddle points, which has not been reported before in the literature on decentralized nonconvex optimization. The algorithm is efficient in both communication (it only shares one variable in each iteration) and computation (it is encryption-free), and hence is promising for large-scale nonconvex optimization and learning involving high-dimensional optimization parameters. Numerical experiments for both a decentralized estimation problem and an Independent Component Analysis (ICA) problem confirm the effectiveness of the proposed approach.

Deep learning methods have gained increased attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems. Interpretation models enable the understanding of the inner workings of deep learning models and offer a sense of security in detecting the misuse of artifacts in the input data. Similar to prediction models, interpretation models are also susceptible to adversarial inputs. This work introduces two attacks, AdvEdge and AdvEdge$^{+}$, that deceive both the target deep learning model and the coupled interpretation model. We assess the effectiveness of proposed attacks against two deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models. Our experiments include the attack implementation using various attack frameworks. We also explore the potential countermeasures against such attacks. Our analysis shows the effectiveness of our attacks in terms of deceiving the deep learning models and their interpreters, and highlights insights to improve and circumvent the attacks.

大脑计算机界面（BCI）具有解决许多大脑信号分析局限性，精神障碍分辨率以及通过神经控制的植入物恢复缺失的肢体功能的巨大潜力。但是，尚无单一可用，并且存在安全的日常生活使用情况。大多数拟议的植入物都有多个实施问题，例如感染危害和散热，这限制了它们的可用性，并使通过法规和质量控制生产更具挑战性。无线植入物不需要颅骨慢性伤口。但是，当前植入物芯片内部的复杂聚类神经元识别算法消耗了大量功率和带宽，从而导致更高的散热问题并排出植入物的电池。尖峰分类是侵入性BCI芯片的核心单位，在功耗，准确性和区域中起着重要作用。因此，在这项研究中，我们提出了一个低功率自适应的简化VLSI体系结构，“ Zydeco风格”，用于BCI Spike Sorting，在最坏情况下，计算上的计算较差，其精度较高，高达93.5％。该体系结构使用带有外部物联网医疗ICU设备的低功率蓝牙无线通信模块。在Verilog中实现并模拟了所提出的架构。此外，我们正在提出植入概念设计。

了解人类的行为和监测心理健康对于维持社区和社会的安全至关重要。由于不受控制的心理健康，由于心理健康期间，由于心理健康的大流行期间的心理健康问题有所增加，因此对心理问题的早期发现至关重要。如今，智能虚拟个人助理（IVA）的使用已在全球范围内增加。个人使用声音来控制这些设备以满足请求并获得不同的服务。本文提出了一种基于封闭式复发性神经网络和卷积神经网络的新型深度学习模型，以了解人类的情感从语音中，以改善其IVA服务并监控其心理健康。

我们考虑在平均场比赛中在线加强学习。与现有作品相反，我们通过开发一种使用通用代理的单个样本路径来估算均值场和最佳策略的算法来减轻对均值甲骨文的需求。我们称此沙盒学习为其，因为它可以用作在多代理非合作环境中运行的任何代理商的温暖启动。我们采用了两种时间尺度的方法，在该方法中，平均场的在线固定点递归在较慢的时间表上运行，并与通用代理更快的时间范围内的控制策略更新同时进行。在足够的勘探条件下，我们提供有限的样本收敛保证，从平均场和控制策略融合到平均场平衡方面。沙盒学习算法的样本复杂性为$ \ Mathcal {o}（\ epsilon^{ - 4}）$。最后，我们从经验上证明了沙盒学习算法在交通拥堵游戏中的有效性。

行动检测和公共交通安全是安全社区和更好社会的关键方面。使用不同的监视摄像机监视智能城市中的交通流量可以在识别事故和提醒急救人员中发挥重要作用。计算机视觉任务中的动作识别（AR）的利用为视频监视，医学成像和数字信号处理中的高精度应用做出了贡献。本文提出了一项密集的审查，重点是智能城市的事故检测和自动运输系统中的行动识别。在本文中，我们专注于使用各种交通视频捕获来源的AR系统，例如交通交叉点上的静态监视摄像头，高速公路监控摄像头，无人机摄像头和仪表板。通过这篇综述，我们确定了AR中用于自动运输和事故检测的主要技术，分类法和算法。我们还检查了AR任务中使用的数据集，并识别数据集的数据集和功能的主要来源。本文提供了潜在的研究方向，以开发和整合为自动驾驶汽车和公共交通安全系统的事故检测系统，通过警告紧急人员和执法部门，如果道路事故发生道路事故，以最大程度地减少事故报告中的人为错误，并对受害者提供自发的反应。