The artificial intelligence (AI) community has recently made tremendous progress in developing self-supervised learning (SSL) algorithms that can learn high-quality data representations from massive amounts of unlabeled data. These methods brought great results even to the fields outside of AI. Due to the joint efforts of researchers in various areas, new SSL methods come out daily. However, such a sheer number of publications make it difficult for beginners to see clearly how the subject progresses. This survey bridges this gap by carefully selecting a small portion of papers that we believe are milestones or essential work. We see these researches as the "dots" of SSL and connect them through how they evolve. Hopefully, by viewing the connections of these dots, readers will have a high-level picture of the development of SSL across multiple disciplines including natural language processing, computer vision, graph learning, audio processing, and protein learning.
The development of IP-based Internet of Things (IoT) networks would facilitate more effective end-to-end IP network architectures, but it remains a challenge. Network routing needs to be effectively addressed in the IoT environments of scarce computational and energy resources. Accordingly, the Internet Engineering Task Force (IETF) has specified the IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to provide a bespoke IPv6-based routing framework for IoT networks. However, RPL comes with no Quality of Service (QoS) support which is an essential requirement for many IoT applications. The network research community has introduced a number of research proposals enhancing RPL with different QoS solutions. This paper presents a review of these proposed solutions and aims to establish a firm understanding of recent QoS developments for RPL and possible areas for future IoT routing research. The focus is on comprehending the protocol and networking properties that can affect QoS performance in RPL networks. Consideration is also given to different objective functions developed for addressing varying QoS aspects such as throughput, delay, and packet loss. RPL is also extended in a number of QoS solutions following different approaches at the MAC, network, and application layers. However, there is still a need for further developments to address effective QoS support, particularly for dynamic RPL networks.
Oblivious polynomial evaluation (OPE) is a two-party protocol that allows a receiver, R to learn an evaluation f(α), of a sender, S's polynomial f(x), whilst keeping both α and f(x) private. This protocol has attracted a lot of attention recently, as it has wide ranging applications in the field of cryptography.
In this article we review some of these applications and, additionally, take an in-depth look at the special case of information theoretic OPE. Specifically, we provide a current and critical review of the existing information theoretic OPE protocols in the literature. We divide these protocols into two distinct cases (three-party and distributed OPE) allowing for the easy distinction and classification of future information theoretic OPE protocols. In addition to this work, we also develop several modifications and extensions to existing schemes, resulting in increased security, flexibility and efficiency. Lastly, we also identify a security flaw in a previously published OPE scheme.