Voting Systems with Trust Mechanisms in Cyberspace: Vulnerabilities and Defenses

With the popularity of voting systems in cyberspace, there is growing evidence that current voting systems can be manipulated by fake votes. This problem has attracted many researchers working on guarding voting systems in two areas: relieving the effect of dishonest votes by evaluating the trust of voters, and limiting the resources that can be used by attackers, such as the number of voters and the number of votes. In this paper, we argue that powering voting systems with trust and limiting attack resources are not enough. We present a novel attack named as Reputation Trap (RepTrap). Our case study and experiments show that this new attack needs much less resources to manipulate the voting systems and has a much higher success rate compared with existing attacks. We further identify the reasons behind this attack and propose two defense schemes accordingly. In the first scheme, we hide correlation knowledge from attackers to reduce their chance to affect the honest voters. In the second scheme, we introduce robustness-of-evidence, a new metric, in trust calculation to reduce their effect on honest voters. We conduct extensive experiments to validate our approach. The results show that our defense schemes not only can reduce the success rate of attacks but also significantly increase the amount of resources an adversary needs to launch a successful attack

Sparse Anatomical Prompt Semi-Supervised Learning with Masked Image Modeling for CBCT Tooth Segmentation

Accurate tooth identification and segmentation in Cone Beam Computed Tomography (CBCT) dental images can significantly enhance the efficiency and precision of manual diagnoses performed by dentists. However, existing segmentation methods are mainly developed based on large data volumes training, on which their annotations are extremely time-consuming. Meanwhile, the teeth of each class in CBCT dental images being closely positioned, coupled with subtle inter-class differences, gives rise to the challenge of indistinct boundaries when training model with limited data. To address these challenges, this study aims to propose a tasked-oriented Masked Auto-Encoder paradigm to effectively utilize large amounts of unlabeled data to achieve accurate tooth segmentation with limited labeled data. Specifically, we first construct a self-supervised pre-training framework of masked auto encoder to efficiently utilize unlabeled data to enhance the network performance. Subsequently, we introduce a sparse masked prompt mechanism based on graph attention to incorporate boundary information of the teeth, aiding the network in learning the anatomical structural features of teeth. To the best of our knowledge, we are pioneering the integration of the mask pre-training paradigm into the CBCT tooth segmentation task. Extensive experiments demonstrate both the feasibility of our proposed method and the potential of the boundary prompt mechanism

Exploring the Cost-Availability Tradeoff in P2P Storage Systems.

Abstract-P2P storage systems use replication to provide a certain level of availability. While the system must generate new replicas to replace replicas lost to permanent failures, it can save significant replication cost by not replicating following transient failures. However, in real systems, it is impossible to reliably distinguish permanent and transients failures, resulting in a tradeoff between high recovery cost and low data availability. In this paper, we analyze the use of timeouts as a mechanism to navigate this tradeoff. We address the challenging problem of how to choose a timeout to walk the fine line between causing unnecessary replication due to detection inaccuracy, and reducing availability due to detection delay. We conduct simulations based both on synthetic and real traces, and show that the performance of our selected timeout closely approximates the optimal performance that can be achieved by timeouts, and even that of an "oracle" failure detector

A Measurement Study of the Structured Overlay Network in P2P File-Sharing Systems

The architecture of P2P file-sharing applications has been developing to meet the needs of large scale demands. The structured overlay network, also known as DHT, has been used in these applications to improve the scalability, and robustness of the system, and to make it free from single-point failure. We believe that the measurement study of the overlay network used in the real file-sharing P2P systems can provide guidance for the designing of such systems, and improve the performance of the system. In this paper, we perform the measurement in two different aspects. First, a modified client is designed to provide view to the overlay network from a single-user vision. Second, the instances of crawler programs deployed in many nodes managed to crawl the user information of the overlay network as much as possible. We also find a vulnerability in the overlay network, combined with the character of the DNS service, a more serious DDoS attack can be launched

Improved PBL Hybrid with LBL is Benificial to Fundamental Knowledge Acquisition in a Large Class Prior to Medical Internship

Since pre-internship medical students appeared inefficient in acquiring fundamental knowledge in large classes, a hybrid instructional method of problem-and-lecture-based learning (PLBL) was designed to leverage the complementary strengths of PBL in reasoning under minimal guidance and LBL in immediate knowledge retention. We improved PBL (IPBL) in its instructional process and grading in a way that’s feasible in large classes, divided in IPBL almost 50 students into 7-10 squads as a figure simulating student counts in classic PBL class to strive for each squad member to achieve the same level of knowledge, and applied IPBL to about half of the instructional contents while LBL to another half for their complementary strengths. In this case, PLBL led to more number of test questions correctly answered by all students in a class, more students in higher test score buckets, and higher student perception scores on the methodology. PLBL facilitates fundamental knowledge acquisition in large classes within 50 students prior to medical internships

Votetrust: Leveraging friend invitation graph to defend against social network sybils

Online social networks (OSNs) suffer from the creation of fake accounts that introduce fake product reviews, malware and spam. Existing defenses focus on using the social graph structure to isolate fakes. However, our work shows that Sybils could befriend a large number of real users, invalidating the assumption behind social-graph-based detection. In this paper, we present VoteTrust, a scalable defense system that further leverages user-level activities. VoteTrust models the friend invitation interactions among users as a directed, signed graph, and uses two key mechanisms to detect Sybils over the graph: a voting-based Sybil detection to find Sybils that users vote to reject, and a Sybil community detection to find other colluding Sybils around identified Sybils. Through evaluating on Renren social network, we show that VoteTrust is able to prevent Sybils from generating many unsolicited friend requests. We also deploy VoteTrust in Renen, and our real experience demonstrates that VoteTrust can detect large-scale collusion among Sybils

Exosomes from embryonic mesenchymal stem cells alleviate osteoarthritis through balancing synthesis and degradation of cartilage extracellular matrix

Abstract Background Mesenchymal stem cell therapy for osteoarthritis (OA) has been widely investigated, but the mechanisms are still unclear. Exosomes that serve as carriers of genetic information have been implicated in many diseases and are known to participate in many physiological processes. Here, we investigate the therapeutic potential of exosomes from human embryonic stem cell-induced mesenchymal stem cells (ESC-MSCs) in alleviating osteoarthritis (OA). Methods Exosomes were harvested from conditioned culture media of ESC-MSCs by a sequential centrifugation process. Primary mouse chondrocytes treated with interleukin 1 beta (IL-1β) were used as an in vitro model to evaluate the effects of the conditioned medium with or without exosomes and titrated doses of isolated exosomes for 48 hours, prior to immunocytochemistry or western blot analysis. Destabilization of the medial meniscus (DMM) surgery was performed on the knee joints of C57BL/6 J mice as an OA model. This was followed by intra-articular injection of either ESC-MSCs or their exosomes. Cartilage destruction and matrix degradation were evaluated with histological staining and OARSI scores at the post-surgery 8 weeks. Results We found that intra-articular injection of ESC-MSCs alleviated cartilage destruction and matrix degradation in the DMM model. Further in vitro studies illustrated that this effect was exerted through ESC-MSC-derived exosomes. These exosomes maintained the chondrocyte phenotype by increasing collagen type II synthesis and decreasing ADAMTS5 expression in the presence of IL-1β. Immunocytochemistry revealed colocalization of the exosomes and collagen type II-positive chondrocytes. Subsequent intra-articular injection of exosomes derived from ESC-MSCs successfully impeded cartilage destruction in the DMM model. Conclusions The exosomes from ESC-MSCs exert a beneficial therapeutic effect on OA by balancing the synthesis and degradation of chondrocyte extracellular matrix (ECM), which in turn provides a new target for OA drug and drug-delivery system development