Reviewerly/exHarmony huggingface dataset:
Quorum-based model learning on a blockchain hierarchical clinical research network using smart contracts

Collaborative privacy-preserving modeling across several healthcare institutions allows for the construction of more generalizable predictive models while protecting patient privacy. We aim at addressing the site availability issue on a hierarchical network by designing an immutable/transparent/source-verifiable quorum mechanism. We developed an approach to combine a hierarchical learning algorithm, a novel Proof-of-Quorum (PoQ) consensus protocol, and a design of blockchain smart contracts. We constructed QuorumChain as an example and evaluated the scenarios of site-unavailability during the initialization and/or iteration phases of the modeling process on three healthcare/genomic datasets. When one or more sites would become unavailable, HierarchicalChain could not function, whereas QuorumChain improved predictive correctness significantly (the full Area Under the receiver operating characteristic Curve, or AUC, improved from 0.068 to 0.441, all with p-values < 0.001). By constructing a quorum to continue the modeling process, QuorumChain possesses the capability to tackle the situation of sites being unavailable. It inherits the capability of learning on network-of-networks, improves learning continuity, and provides data/software immutability, transparency, and provenance, which can be important in expediting clinical research.
Improving mitotic cell counting accuracy and efficiency using <scp>phosphohistone‐H3</scp> (<scp>PHH3</scp>) antibody counterstained with haematoxylin and eosin as part of breast cancer grading

Mitotic count in breast cancer is an important prognostic marker. Unfortunately, substantial inter- and intraobserver variation exists when pathologists manually count mitotic figures. To alleviate this problem, we developed a new technique incorporating both haematoxylin and eosin (H&E) and phosphorylated histone H3 (PHH3), a marker highly specific to mitotic figures, and compared it to visual scoring of mitotic figures using H&E only.Two full-face sections from 97 cases were cut, one stained with H&E only, and the other was stained with PHH3 and counterstained with H&E (PHH3-H&E). Counting mitoses using PHH3-H&E was compared to traditional mitoses scoring using H&E in terms of reproducibility, scoring time, and the ability to detect mitosis hotspots. We assessed the agreement between manual and image analysis-assisted scoring of mitotic figures using H&E and PHH3-H&E-stained cells. The diagnostic performance of PHH3 in detecting mitotic figures in terms of sensitivity and specificity was measured. Finally, PHH3 replaced the mitosis score in a multivariate analysis to assess its significance.Pathologists detected significantly higher mitotic figures using the PHH3-H&E (median ± SD, 20 ± 33) compared with H&E alone (median ± SD, 16 ± 25), P < 0.001. The concordance between pathologists in identifying mitotic figures was highest when using the dual PHH3-H&E technique; in addition, it highlighted mitotic figures at low power, allowing better agreement on choosing the hotspot area (k = 0.842) in comparison with standard H&E (k = 0.625). A better agreement between image analysis-assisted software and the human eye was observed for PHH3-stained mitotic figures. When the mitosis score was replaced with PHH3 in a Cox regression model with other grade components, PHH3 was an independent predictor of survival (hazard ratio [HR] 5.66, 95% confidence interval [CI] 1.92-16.69; P = 0.002), and even showed a more significant association with breast cancer-specific survival (BCSS) than mitosis (HR 3.63, 95% CI 1.49-8.86; P = 0.005) and Ki67 (P = 0.27).Using PHH3-H&E-stained slides can reliably be used in routine scoring of mitotic figures and integrating both techniques will compensate for each other's limitations and improve diagnostic accuracy, quality, and precision.
Understand-Before-Talk (UBT): A Semantic Communication Approach to 6G Networks

In Shannon theory, semantic aspects of communication were identified but considered irrelevant to the technical communication problems. Semantic communication (SC) techniques have recently attracted renewed research interests in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm 6^{th}$</tex-math></inline-formula> generation (6G) wireless, because they have the capability to support an efficient interpretation of the significance and meaning intended by a sender (or accomplishment of the goal) when dealing with multi-modal data such as videos, images, audio, text messages, and so on, which would be the case for various applications such as intelligent transportation systems where each autonomous vehicle needs to deal with real-time videos and data from a number of sensors including radars. To this end, most of the emerging SC works focus on specific data types and employ sophisticated machine learning models including deep learning and neural networks. However, they could be impractical for multi-modal data possibly within a real-time constraint, relative to the purpose of the communication. A notable difficulty of existing SC frameworks lies in handling the discrete constraints imposed on the pursued semantic coding and its interaction with the independent knowledge-base, which makes reliable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantic extraction</i> extremely challenging. Therefore, we develop a new hashing-based semantic extraction approach to SC framework, where our learning objective is to generate one time signatures (hash codes) using supervised learning for low latency, secure and efficient management of the SC dynamics. We first evaluate the proposed semantic extraction framework over large image data sets, extend it with domain adaptive hashing and then demonstrate the effectiveness of “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semantics signature</i> ” in bulk transmission and multi-modal data.
Exposing the Rat in the Tunnel

Tor~\citetor is the most widely used anonymous communication network with millions of daily users~\citetormetrics. Since Tor provides server and client anonymity, hundreds of malware binaries found in the wild rely on it to hide their presence and hinder Command & Control (C&C) takedown operations. We believe Tor is a paramount tool enabling online freedom and privacy, and blocking it to defend against such malware is infeasible for both users and organizations. In this work, we present effective traffic analysis approaches that can accurately identify Tor-based malware communication. We collect hundreds of Tor-based malware binaries, execute and examine more than 47,000 active encrypted malware connections and compare them with benign browsing traffic. In addition to traditional traffic analysis features (which work at the connection level), we propose global host-level network features to capture peculiar malware communication fingerprints across host logs. Our experiments confirm that our models are able to detect "zero-day'' malware connections with 0.7% FPR even when malware connections constitute less than 5% of Tor traces in the test set. Using multi-labeling approaches, we are able to accurately detect the malware behavior-based classes (grayware, ransomware, etc). Finally, we evaluate the robustness of our models on real-world enterprise logs and show that the classifiers can identify infected hosts even with missing features.
A Knowledge-Guided Framework for Fine-Grained Classification of Liver Lesions Based on Multi-Phase CT Images

Automatic and accurate differentiation of liver lesions from multi-phase computed tomography imaging is critical for the early detection of liver cancer. Multi-phase data can provide more diagnostic information than single-phase data, and the effective use of multi-phase data can significantly improve diagnostic accuracy. Current fusion methods usually fuse multi-phase information at the image level or feature level, ignoring the specificity of each modality, therefore, the information integration capacity is always limited. In this paper, we propose a Knowledge-guided framework, named MCCNet, which adaptively integrates multi-phase liver lesion information from three different stages to fully utilize and fuse multi-phase liver information. Specifically, 1) a multi-phase self-attention module was designed to adaptively combine and integrate complementary information from three phases using multi-level phase features; 2) a cross-feature interaction module was proposed to further integrate multi-phase fine-grained features from a global perspective; 3) a cross-lesion correlation module was proposed for the first time to imitate the clinical diagnosis process by exploiting inter-lesion correlation in the same patient. By integrating the above three modules into a 3D backbone, we constructed a lesion classification network. The proposed lesion classification network was validated on an in-house dataset containing 3,683 lesions from 2,333 patients in 9 hospitals. Extensive experimental results and evaluations on real-world clinical applications demonstrate the effectiveness of the proposed modules in exploiting and fusing multi-phase information.
Squirrel

The focus of this work are multi-signatures schemes in the synchronized setting. A multi-signature scheme allows multiple signatures for the same message but from independent signers to be compressed into one short aggregated signature, which allows verifying all of the signatures simultaneously. In the synchronized setting, the signing algorithm takes the current time step as an additional input. It is assumed that no signer signs more than one message per time step and we aim to aggregate signatures for the same message and same time step. This setting is particularly useful in the context of blockchains, where validators are naturally synchronized by the blocks they sign.
L-SRR

Location-based services (LBS) have been significantly developed and widely deployed in mobile devices. It is also well-known that LBS applications may result in severe privacy concerns by collecting sensitive locations. A strong privacy model ''local differential privacy'' (LDP) has been recently deployed in many different applications (e.g., Google RAPPOR, iOS, and Microsoft Telemetry) but not effective for LBS applications due to the low utility of existing LDP mechanisms. To address such deficiency, we propose the first LDP framework for a variety of location-based services (namely ''L-SRR''), which privately collects and analyzes user locations with high utility. Specifically, we design a novel randomization mechanism ''Staircase Randomized Response'' (SRR) and extend the empirical estimation to significantly boost the utility for SRR in different LBS applications (e.g., traffic density estimation, and k-nearest neighbors). We have conducted extensive experiments on four real LBS datasets by benchmarking with other LDP schemes in practical applications. The experimental results demonstrate that L-SRR significantly outperforms them.
RedShift

We introduce an efficient transformation from univariate polynomial commitment based zk-SNARKs to their transparent counterparts. The transformation is achieved with the help of a new IOP primitive which we call a list polynomial commitment. This primitive is applicable for preprocessing zk-SNARKs over both prime and binary fields. We present the primitive itself along with a soundness analysis of the transformation and instantiate it with an existing universal proof system. We also present benchmarks for a proof of concept implementation alongside a comparison with the current non-transparent state-of-the-art. Our results show competitive efficiency both in terms of proof size and generation times. At the 80-bit security level, our benchmarks provide proof generation times of about a minute and proof sizes of around 515 KB for a circuit with one million gates.
Caulk

We present position-hiding linkability for vector commitment schemes: one can prove in zero knowledge that one or m values that comprise commitment \cm all belong to the vector of size N committed to in \com. Our construction \textsfCaulk can be used for membership proofs and lookup arguments and outperforms all existing alternatives in prover time by orders of magnitude.
Foundations of Coin Mixing Services

Coin mixing services allow users to mix their cryptocurrency coins and thus enable unlinkable payments in a way that prevents tracking of honest users' coins by both the service provider and the users themselves. The easy bootstrapping of new users and backwards compatibility with cryptocurrencies (such as Bitcoin) with limited support for scripts are attractive features of this architecture, which has recently gained considerable attention in both academia and industry.
i-TiRE

Timed-release encryption can encrypt a message to a future time such that it can only be decrypted after that time. Potential applications include sealed bid auctions, scheduled confidential transactions, and digital time capsules. To enable such applications as decentralized smart contracts, we explore how to use timed-release encryption on blockchains.
Post Quantum Noise

We introduce PQNoise, a post-quantum variant of the Noise framework. We demonstrate that it is possible to replace the Diffie-Hellman key-exchanges in Noise with KEMs in a secure way. A challenge is the inability to combine key pairs of KEMs, which can be resolved by certain forms of randomness-hardening for which we introduce a formal abstraction. We provide a generic recipe to turn classical Noise patterns into PQNoise patterns. We prove that the resulting PQNoise patterns achieve confidentiality and authenticity in the fACCE model. Moreover we show that for those classical Noise-patterns that have been conjectured or proven secure in the fACCE model our matching PQNoise patterns eventually achieve the same security. Our security proof is generic and applies to any valid PQNoise pattern. This is made possible by another abstraction, called a hash-object, which hides the exact workings of how keying material is processed in an abstract stateful object that outputs pseudorandom keys under different corruption patterns. We also show that the hash chains used in Noise are a secure hash-object. Finally, we demonstrate the practicality of PQNoise delivering benchmarks for several base patterns.
PolyFax: a toolkit for characterizing multi-language software

Today's software systems are mostly developed in multiple languages (i.e., multi-language software), yet tool support for understanding and assuring these systems is rare. To facilitate future research on multi-language software engineering, this paper presents PolyFax, a toolkit that offers automated means for dataset collection from GitHub and two analysis utilities--a vulnerability-fixing commit categorization tool (VCC) and a language interfacing mechanism identification/categorization tool (LIC). The VCC tool immediately assists with assessing the vulnerability proneness of a given multi-language project based on its version histories, while the LIC tool enables dissection of the most important aspect of the construction of multi-language systems. Application of PolyFax to 7,113 multi-language projects with 12.6 million commits showed its practical usefulness in terms of promising efficiency and accuracy for studying multi-language software.
On the vulnerability proneness of multilingual code

Software construction using multiple languages has long been a norm, yet it is still unclear if multilingual code construction has significant security implications and real security consequences. This paper aims to address this question with a large-scale study of popular multi-language projects on GitHub and their evolution histories, enabled by our novel techniques for multilingual code characterization. We found statistically significant associations between the proneness of multilingual code to vulnerabilities (in general and of specific categories) and its language selection. We also found this association is correlated with that of the language interfacing mechanism, not that of individual languages. We validated our statistical findings with in-depth case studies on actual vulnerabilities, explained via the mechanism and language selection. Our results call for immediate actions to assess and defend against multilingual vulnerabilities, for which we provide practical recommendations.
Practical Volume-Hiding Encrypted Multi-Maps with Optimal Overhead and Beyond

Encrypted multi-map (EMM), as a special case of structured encryption, has attracted extensive attention recently. However, most of EMM constructions reveal the real volumes of queried keys, which can be leveraged to launch leakage-abuse attacks, as demonstrated by Kellaris et al. in CCS 2016 and Kornaropoulos et al. in S&P 2021.
Constant Latency in Sleepy Consensus

Dynamic participation support is an important feature of Bitcoin's longest-chain protocol and its variants. But these protocols suffer from long latency as a fundamental trade-off. Specifically, the latency depends at least on the following two factors: 1) the desired security level of the protocol, and 2) the actual participation level of the network. Classic BFT protocols, on the other hand, can achieve constant latency but cannot make progress under dynamic participation. In this work, we present a protocol that simultaneously supports dynamic participation and achieves constant latency. Our core technique is to extend the classic BFT approach from static quorum size to dynamic quorum size, i.e., according to the current participation level, while preserving important properties of static quorum. We also present a recovery mechanism for rejoining nodes that is efficient in terms of both communication and storage. Our experimental evaluation shows our protocol has much lower latency than a longest-chain protocol, especially when there is a sudden decrease of participation.
Incorporating domain knowledge through task augmentation for front-end JavaScript code generation

Code generation aims to generate a code snippet automatically from natural language descriptions. Generally, the mainstream code generation methods rely on a large amount of paired training data, including both the natural language description and the code. However, in some domain-specific scenarios, building such a large paired corpus for code generation is difficult because there is no directly available pairing data, and a lot of effort is required to manually write the code descriptions to construct a high-quality training dataset. Due to the limited training data, the generation model cannot be well trained and is likely to be overfitting, making the model's performance unsatisfactory for real-world use. To this end, in this paper, we propose a task augmentation method that incorporates domain knowledge into code generation models through auxiliary tasks and a Subtoken-TranX model by extending the original TranX model to support subtoken-level code generation. To verify our proposed approach, we collect a real-world code generation dataset and conduct experiments on it. Our experimental results demonstrate that the subtoken-level TranX model outperforms the original TranX model and the Transformer model on our dataset, and the exact match accuracy of Subtoken-TranX improves significantly by 12.75% with the help of our task augmentation method. The model performance on several code categories has satisfied the requirements for application in industrial systems. Our proposed approach has been adopted by Alibaba's BizCook platform. To the best of our knowledge, this is the first domain code generation system adopted in industrial development environments.
Language-agnostic dynamic analysis of multilingual code: promises, pitfalls, and prospects

Analyzing multilingual code holistically is key to systematic quality assurance of real-world software which is mostly developed in multiple computer languages. Toward such analyses, state-of-the-art approaches propose an almost-fully language-agnostic methodology and apply it to dynamic dependence analysis/slicing of multilingual code, showing great promises. We investigated this methodology through a technical analysis followed by a replication study applying it to 10 real-world multilingual projects of diverse language combinations. Our results revealed critical practicality (i.e., having the levels of efficiency/scalability, precision, and extensibility to various language combinations for practical use) challenges to the methodology. Based on the results, we reflect on the underlying pitfalls of the language-agnostic design that leads to such challenges. Finally, looking forward to the prospects of dynamic analysis for multilingual code, we identify a new research direction towards better practicality and precision while not sacrificing extensibility much, as supported by preliminary results. The key takeaway is that pursuing fully language-agnostic analysis may be both impractical and unnecessary, and striving for a better balance between language independence and practicality may be more fruitful.
Trusted-auditing chain: A security blockchain prototype used in agriculture traceability

Traceability systems have changed the way food safety is managed and data is stored. Blockchain tracking services now provide customers with an infrastructure that allows them to easily access data online. However, there are limitations to these new capabilities, such as a lack of transparency and the existence of privacy and security challenges. Additionally, as the need for more agile, private, and traceability secure data solutions continues to grow exponentially, rethinking the current structure of blockchain agricultural traceability is mission-critical for a country. By leveraging and building upon blockchain's unique attributes, including tamper-evident, security hash crypto-data, and distributed ledger, we have proposed a prototype that allows traceability data to be reliably stored via blockchain while simultaneously being secured, with completeness auditing to enhance credibility. The result, the trusted auditing chain (TA chain), is a flexible solution that assures data security and solves challenges such as scalability and privacy-preserving. The TA chain works through Schnorr-style non-interactive Zero-knowledge proof to support security automatical choose privacy augmented. In addition, The TA chain can audit more than 1000 transactions within 1ms, and its error stabilizes below the 250 μs, which proves a security and fair traceability system to assure that data is distributed and reliably, and provably audited.