Reviewerly/exHarmony huggingface dataset:
Terms for Efficient Proof Checking and Parsing

Proofs automatically generated by interactive or automated theorem provers are often several orders of magnitude larger than proofs written by hand. This implies significant challenges for processing such proofs efficiently. It turns out that the data structures used to encode terms have a high impact on performance. This article proposes several term data structures; in particular, heterogeneous terms for proof checking that distinguish long- and short-lived terms, and abstract terms for proof parsing. Both term data structures are implemented in the proof checker Kontroli, enabling it to parse and check proofs both sequentially and concurrently without overhead. The evaluation on three datasets exported from interactive theorem provers shows that the new term data structures significantly improve proof checking performance.
Efficient Server-Aided Personalized Treatment Recommendation with Privacy Preservation

With AI-derived knowledge graph (KG), medical centers can recommend appropriate treatment options to physicians as references based on their patients' personal healthcare information (PHI). However, the treatment recommendation services may also cause serious privacy concerns. In this paper, we propose an efficient and privacy-preserving personalized treatment recommendation scheme with the aid of a third-party server. Specifically, a medical center denotes the KG of each disease by a directed graph with conditional edges and vertices that describe the treatment options and costs in different states. To prevent privacy leakage while reducing computational and management cost, the graphs are encrypted and then delegated to an honest-but-curious server. With the assistance of the server, physicians can set proper illness states and cost requirements according to patients' PHI, and correspondingly generate personalized ciphertexts to retrieve appropriate treatment options. The key component of the proposed scheme is a novel designed secure and flexible path comparison protocol, by tailoring a symmetric homomorphic encryption algorithm and combining it with a secure hash function. The protocol can enable the server to compare the uploaded ciphertexts with encrypted graphs in a secure and efficient way. Comprehensive security analysis indicates that the proposed scheme can meet desirable privacy requirements, and extensive experimental results demonstrate its practicality.
MCVAE: Multi-channel Variational Autoencoder for Anomaly Detection

Unsupervised anomaly detection is a very important problem due to its wide applications in many systems such as the network communication system, the Internet of Things, and the ICS system. Interpretable modeling of heterogeneous data channels is also essential in anomaly detection, due to the intrinsic multi-modality in multi-channel multi-dimension data. Some existing works use variational autoencoder (VAE) for anomaly detection with a single Gaussian distribution model. However, these VAE can not learn the complex distribution between features, and hence cannot make accurate detections. To tackle this challenge, in this paper, we propose a multi-channel VAE model to jointly account for latent relationships across multiple channels. Moreover, we explore the training loss function combining the reconstruction loss of each channel. We also explore the test loss combining the reconstruction probability and reconstruction ratio between global channel information and each channel information. The proposed detector reports an anomaly when the test loss is below a certain threshold. We conduct extensive simulations on a real world dataset and find that our proposed scheme outperforms the state-of-the-art anomaly detection schemes compared with existing methods.
Progressive Differentiable Architecture Search Based Automatic Modulation Classification Method

Automatic modulation classification (AMC) is a key step of signal demodulation that determines whether the receiver can correctly receive the modulation type of the transmitted signal without prior knowledge. Deep learning (DL) based AMC methods have achieved excellent performances. However, these methods highly rely on expert experience to design network structures. These hand-designed networks have fixed structures and lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) is a vital direction for automatic machine learning (AutoML) which can solve the shortcomings of hand-designed networks. In this paper, we propose a lightweight progressive differentiable architecture search-based AMC (PDARTS-AMC) method to search for a very lightweight network with good performance. Experimental results show that the proposed PDARTS-AMC method both improves the accuracy and reduces the computational cost when compared with existing methods.
Exploiting Playback Device's Effect on Multi-channel Audio to Secure Voice Assistants

Voice Assistant Devices (VADs) such as Alexa, Google Now, and Siri have become increasingly popular because of their various voice-enabled features, including online shopping, controlling smart home appliances, accessing banking services, and more. However, it also brings unique security issues like voice replay attacks, where an attacker can generate a malicious voice command via a compromised playback device near the VAD. The usage of VADs in users' daily tasks makes such attack detection more important. To detect such attacks, we propose a defense system leveraging the impact of playback devices on the bass frequency region (0 to 500 Hz) of multi-channel audios. Currently, no prior work has exploited the playback device's impact on the bass frequency region of the multi-channel audios to prevent voice replay attacks. Specifically, our system divides the bass region of each channel into ten sub-bands and computes the total percentage of the overall signal's power presented in each sub-band. To make the system more robust against advanced audio attacks, we also extract the Modified Group Delay Function (MODGDF) cepstral coefficients of the bass area for phase features. Then, our proposed system applies a support vector machine (SVM) classifier to infer whether a human or a compromised playback device initiates the voice command. The system is then tested against a public multi-channel replay attack dataset. The system performance is checked in four different environmental conditions, achieving a maximum of 1.21 % Equal Error Rate (EER). Our experimental results also show that incorporating more audio channels improves the attack detection performance.
Transfer Learning-Based Radio Frequency Fingerprint Identification Using ConvMixer Network

Radio frequency fingerprint (RFF) identification is an emerging physical layer security technique, which provokes many promising applications in the internet of things (IoT). However, traditional machine learning-based RFF identification methods rely on complex manual feature extraction, while it is difficult for methods based on deep learning to deal with RFF identification under different channel environments. To solve these problems, we propose three different transfer learning-based RFF identification methods based on ConvMixer network, which is a mixture of different convolutional layers, using pre-trained model in the previous channel environment to assist in training under the new channel environment. Experimental results show that, compared with the previous retraining method, our proposed method reduces the number of training parameters and improves the identification performance at low SNR. Moreover, the proposed method can still have a certain performance guarantee with less training data.
Structuring Meaningful Code Changes in Developer Community

The rapid development of Open-Source Software (OSS) has resulted in a significant demand for code changes to maintain OSS. Symptoms of poor design and implementation choices in code changes often occur, thus heavily hindering code reviewers to verify correctness and soundness of code changes. Researchers have investigated how to learn meaningful code changes to assist developers in anticipating changes that code reviewers may suggest for the submitted code. However, there are two main limitations to be addressed, including the limitation of long-range dependencies of the source code and the missing syntactic structural information of the source code. To solve these limitations, we propose a novel method named GTCT. GTCT comprises two components: code graph embedding and code transformation learning. To address the missing syntactic structural information, we encoding the source code into a code graph structure from the lexical and syntactic representations of the source code. Subsequently, we uses the multi-head attention mechanism and positional encoding mechanism to address the long-range dependencies limitation. Extensive experiments are conducted to evaluate the performance of GTCT by both quantitative and qualitative analyses. For the quantitative analysis, GTCT relatively outperforms the baseline on six datasets by 210%, 342.86%, 135%, 29.41%, 109.09%, and 91.67% in terms of perfect prediction. Meanwhile, the qualitative analysis shows that each type of code change by GTCT outperforms that of the baseline method in terms of bug fixed, refactoring code, and others’ taxonomy of code changes.
Formalising Decentralised Exchanges in Coq

The number of attacks and accidents leading to significant losses of crypto-assets is growing. According to Chainalysis, in 2021, approx. $14 billion has been lost due to various incidents, and this number is dominated by Decentralized Finance (DeFi) applications. To address these issues, one can use a collection of tools ranging from auditing to formal methods. We use formal verification and provide the first formalisation of a DeFi contract in a foundational proof assistant capturing contract interactions.
The Geometry of Causality: Multi-token Geometry of Interaction and Its Causal Unfolding

We introduce a multi-token machine for Idealized Parallel Algol (IPA), a higher-order concurrent programming language with shared state and semaphores. Our machine takes the shape of a compositional interpretation of terms as Petri structures, certain coloured Petri nets. For the purely functional fragment of IPA, our machine is conceptually close to Geometry of Interaction token machines, originating from Linear Logic and presenting higher-order computation as the low-level process of a token walking through a graph (a proof net) representing the term. We combine here these ideas with folklore ideas on the representation of first-order imperative concurrent programs as coloured Petri nets. To prove our machine computationally adequate with respect to the reference operational semantics, we follow game semantics and represent types as certain games specifying dependencies and conflict between computational events. Petri strategies are those Petri structures obeying the rules of the game extracted from the type. We show how Petri strategies unfold to concurrent strategies in the sense of concurrent games on event structures. This link with concurrent strategies not only allows us to prove adequacy of our machine, but also lets us generate operationally a causal description of the behaviour of programs at higher-order types, which is shown to coincide with that given denotationally by the interpretation in concurrent games.
ASN1*: Provably Correct, Non-malleable Parsing for ASN.1 DER

Abstract Syntax Notation One (ASN.1) is a language for structured data exchange between computers, standardized by both ITU-T and ISO/IEC since 1984. The Distinguished Encoding Rules (DER) specify its non-malleable binary format: for a given ASN.1 data type, every value has a distinct, unique binary representation. ASN.1 DER is used in many security-critical interfaces for telecommunications and networking, such as the X.509 public key infrastructure, where non-malleability is essential. However, due to the expressiveness and flexibility of the general-purpose ASN.1 language, correctly parsing ASN.1 DER data formats is still considered a serious security challenge in practice. We present ASN1*, the first formalization of ASN.1 DER with a mechanized proof of non-malleability. Our development provides a shallow embedding of ASN.1 in the F* proof assistant and formalizes its DER semantics within the EverParse parser generator framework. It guarantees that any ASN.1 data encoded using our DER semantics is non-malleable. It yields verified code that parses valid binary representations into values of the corresponding ASN.1 data type while rejecting invalid ones. We empirically confirm that our semantics models ASN.1 DER usage in practice by evaluating ASN1* parsers extracted to OCaml on both positive and negative test cases involving X.509 certificates and Certificate Revocation Lists (CRLs).
Witnessability of Undecidable Problems

Many problems in programming language theory and formal methods are undecidable, so they cannot be solved precisely. Practical techniques for dealing with undecidable problems are often based on decidable approximations. Undecidability implies that those approximations are always imprecise. Typically, practitioners use heuristics and ad hoc reasoning to identify imprecision issues and improve approximations, but there is a lack of computability-theoretic foundations about whether those efforts can succeed. This paper shows a surprising interplay between undecidability and decidable approximations: there exists a class of undecidable problems, such that it is computable to transform any decidable approximation to a witness input demonstrating its imprecision. We call those undecidable problems witnessable problems . For example, if a program property P is witnessable, then there exists a computable function f P , such that f P takes as input the code of any program analyzer targeting P and produces an input program w on which the program analyzer is imprecise. An even more surprising fact is that the class of witnessable problems includes almost all undecidable problems in programming language theory and formal methods. Specifically, we prove the diagonal halting problem K is witnessable, and the class of witnessable problems is closed under complements and many-one reductions. In particular, all “non-trivial semantic properties of programs” mentioned in Rice’s theorem are witnessable. We also explicitly construct a problem in the non-witnessable (and undecidable) class and show that both classes have cardinality 2 ℵ 0 . Our results offer a new perspective on the understanding of undecidability: for witnessable problems, although it is impossible to solve them precisely, it is always possible to improve any decidable approximation to make it closer to the precise solution. This fact formally demonstrates that research efforts on such approximations are promising and shows there exist universal ways to identify precision issues of program analyzers, program verifiers, SMT solvers, etc., because their essences are decidable approximations of witnessable problems.
Structural Equivalence in Subgraph Matching

Symmetry plays a major role in subgraph matching both in the description of the graphs in question and in how it confounds the search process. This work addresses how to quantify these effects and how to use symmetries to increase the efficiency of subgraph isomorphism algorithms. We introduce rigorous definitions of structural equivalence and establish conditions for when it can be safely used to generate more solutions. We illustrate how to adapt standard search routines to utilize these symmetries to accelerate search and compactly describe the solution space. We then adapt a state-of-the-art solver and perform a comprehensive series of tests to demonstrate these methods' efficacy on a standard benchmark set. We extend these methods to multiplex graphs and present results on large multiplex networks drawn from transportation systems, social media, adversarial attacks, and knowledge graphs.
Bayesian Methods for Estimation the Parameters of Finite Mixture of Inverse Rayleigh Distribution

Methods of estimating statistical distribution have attracted many researchers when it comes to fitting a specific distribution to data. However, when the data belong to more than one component, a popular distribution cannot be fitted to such data. To tackle this issue, mixture models are fitted by choosing the correct number of components that represent the data. This can be obvious in lifetime processes that are involved in a wide range of engineering applications as well as biological systems. In this paper, we introduce an application of estimating a finite mixture of Inverse Rayleigh distribution by the use of the Bayesian framework when considering the model as Markov chain Monte Carlo (MCMC). We employed the Gibbs sampler and Metropolis – Hastings algorithms. The proposed techniques are applied to simulated data following several scenarios. The accuracy of estimation has been examined by the average mean square error (AMSE) and the average classification success rate (ACSR). The results showed that the method was well performed in all simulation scenarios with respect to different sample sizes.
StructureSense

Recent advancements in object-tracking technologies can turn mundane constructive assemblies into Tangible User Interfaces (TUI) media. Users rely on instructions or their own creativity to build both permanent and temporary structures out of such objects. However, most existing object-tracking technologies focus on tracking structures as monoliths, making it impossible to infer and track the user's assembly process and the resulting structures. Technologies that can track the assembly process often rely on specially fabricated assemblies, limiting the types of objects and structures they can track. Here, we present StructureSense, a tracking system based on passive UHF-RFID sensing that infers constructive assembly structures from object motion. We illustrated StructureSense in two use cases (as guided instructions and authoring tool) on two different constructive sets (wooden lamp and Jumbo Blocks), and evaluated system performance and usability. Our results showed the feasibility of using StructureSense to track mundane constructive assembly structures.
$D^{3}K$: Dynastic Data-Free Knowledge Distillation

Data-free knowledge distillation further broadens the applications of the distillation model. Nevertheless, the problem of providing diverse data with rich expression patterns needs to be further explored. In this paper, a novel dynastic data-free knowledge distillation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$D^{3}K$</tex-math></inline-formula> ) model is proposed to alleviate this problem. In this model, a dynastic supernet generator (D-SG) with a flexible network structure is proposed to generate diverse data. The D-SG can adaptively alter architectural configurations and activate different subnet generators in different sequential iteration spaces. The variable network structure increases the complexity and capacity of the generator, and strengthens its ability to generate diversified data. In addition, a novel additive constraint based on the differentiable dhash (D-Dhash) is designed to guide the structure parameter selection of the D-SG. This constraint forces the D-SG to constantly jump out of the fixed generation mode and generate diverse data in semantics and instance. The effectiveness of the proposed model is verified on the experimental benchmark datasets (MNIST, CIFAR-10, CIFAR-100, and SVHN).
Enhancing Protocol Privacy With Blind Calibration of Quantum Devices

To mitigate the noise in quantum channels, calibration is used to tune the devices to minimize error. Generally, calibration is performed by transmitting pre-agreed-upon calibration states and determining an error cost so the two parties can tune their devices accordingly. The calibration states can be the same ones used for the desired protocol, and so an untrusted party could potentially learn which protocol is being performed by gathering knowledge of the calibration states and cost function. Here, we assume privacy of the protocol is the goal and therefore the receiver should not be allowed to determine the protocol states. We limit the information that is revealed to the receiver, and in this regard, we propose a simple protocol that hides the calibration states and cost function from the receiver, but still allows for calibration to be performed efficiently, thereby increasing the privacy of the protocol. We show various numerical results demonstrating the ability of the protocol under various channel noise parameters and communication scenarios.
Admissible Types-to-PERs Relativization in Higher-Order Logic

Relativizing statements in Higher-Order Logic (HOL) from types to sets is useful for improving productivity when working with HOL-based interactive theorem provers such as HOL4, HOL Light and Isabelle/HOL. This paper provides the first comprehensive definition and study of types-to-sets relativization in HOL, done in the more general form of types-to-PERs (partial equivalence relations). We prove that, for a large practical fragment of HOL which includes container types such as datatypes and codatatypes, types-to-PERs relativization is admissible, in that the provability of the original, type-based statement implies the provability of its relativized, PER-based counterpart. Our results also imply the admissibility of a previously proposed axiomatic extension of HOL with local type definitions. We have implemented types-to-PERs relativization as an Isabelle tool that performs relativization of HOL theorems on demand.
Urban Ecological Planning Model Based on Information Entropy and Meteorological Suitability Evaluation Algorithm

In order to improve the effect of regional urban ecological planning and ecological restoration, aiming at the problem of low accuracy of ecological planning and poor matching with the current situation, taking Yucheng County, Shangqiu City, Henan Province as an example, an urban ecological planning model based on information entropy and meteorological suitability evaluation algorithm is designed. The geographical environment of Yucheng County, Shangqiu City, Henan Province is analyzed. Based on the current situation of regional ecological environment, the meteorological suitability evaluation algorithm is introduced to establish the urban ecological planning model. Comprehensively considering land planning factors and meteorological factors, calculate the information entropy of each influencing factor, determine the weight, and optimize the ecological planning model. The experimental results show that this method has achieved good results in the accuracy of ecological planning and the matching degree of the current situation in the urban ecological planning of Yucheng County, Shangqiu City, Henan Province.