Verification Time Research Articles

Accelerating NoC Verification Using a Complete Model and Active Window

This work presents formal modeling of Network-on-Chip (NoC) considering detailed functional units of NoC. The intricate modeling of NoC router components like buffer, switch, and arbiter is accomplished using Finite State Machine (FSM). As in the case of a real NoC, parallel execution of these functional units is carried out by maintaining the synchronization between these functional units within a router and between each of the adjacent routers in the presented formal model. Important properties for the correctness of the proposed model are verified using a model checker. Implementing a detailed and a complete NoC model is a memory extensive operation while verifying with a model checker. We have introduced a concept of active windows to verify each router and its communication with the adjacent routers. The correctness of the model is checked by verifying the synchronization between NoC functional units and NoC routers, verifying progress in functional units, and verifying the successful transfer of packets. Verification of starvation freedom in an NoC router is also performed for round-robin arbiter and fixed-priority arbiter. Parallel threads are used in the experiments to reduce the verification time.

Blockchain-Based Dashcam Video Management Method for Data Sharing and Integrity in V2V Network

A dashboard camera (Dashcam) is attached to the front or rear of a vehicle to record images and video. As it is effective in crime prevention and accident management and it can also be used as learning data for Traffic Accident Detection technology for Autonomous Vehicles the use of Dashcam is increasing. However, the video data is stored on a memory card or a cloud server, so there is a high possibility of data loss and forgery. Although it is possible to prevent forgery and falsification using distributed storage based on blockchain technology, the authenticity and privacy of the image data stored in the blockchain cannot be guaranteed. In this study, to solve this problem, we propose a multi-signature-based access control method by grouping and storing video data of multiple vehicles based on GPS (Global Positioning System) data. To ensure the privacy of the video data stored in the blockchain, only users uploading video belonging to the relevant GPS can access nearby Dashcam videos. Through these experimental results, it show that Experimental results proposed method can maintain low latency in large-scale request environment to the privacy and reach data management efficiently in a distributed file system. According to our experiments, the dashcam video data distributed storage latency was fast enough, with an average of 25 ms for uploads and less than 15 ms for downloads. The signature generation time is 10ms, and the verification time required for access control is also less than 100 ms, which is the same even if the number of nodes increases, so scalability is not affected. The blockchain transaction processing took about 2 seconds, but it does not affect the V2V network. Also, client registration time does not affect performance.

Integrating Threshold Opening With Threshold Issuance of Anonymous Credentials Over Blockchains for a Multi-Certifier Communication Model

Authentication while maintaining anonymity when availing a service over the internet is a significant privacy challenge. Anonymous credentials (AC) address this by providing the user with a credential issued by a trusted entity that convinces the service provider ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SP</i> ) that the user is authenticated but reveals no other information. The existing AC schemes assume a single trusted authority (certifier) that validates all the user attributes. In practice, however, a user may require different attributes to be attested by different certifiers. This means that the user has to get multiple credentials, increasing the burden on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SP</i> who has to verify each one of them. Moreover, complete anonymity can be misused. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decentralized threshold revocable anonymous credential (DTRAC)</i> scheme over blockchains that supports – a) attestation of attributes by multiple certifiers, and b) anonymity revocation through a set of distributed openers, by integrating threshold opening to the state-of-the-art threshold anonymous credential issuance scheme, Coconut [34]. DTRAC generates a single credential on attributes that are attested by multiple certifiers, freeing the SP from the hassle of verifying multiple credentials. We analyze the security of DTRAC formally in the universal composability (UC) framework. We also implement a prototype on Ethereum using smart contracts and give a detailed analysis of its performance.We compare the verification time for credentials with attributes attested by multiple certifiers in both DTRAC and Coconut and see that in terms of execution time and gas consumption, DTRAC performs significantly better than Coconut. It also scales better, with the performance gain of DTRAC over Coconut increasing linearly with the number of certifiers.

Initial Propulsion System Study for the Futuristic Hyperloop Transportation System: Design, Modeling, and Hardware in the Loop Verification

Recently, the proposal for a futuristic mode of transportation known as the Hyperloop has been popularized. Currently, there are only some reports regarding the design of the Hyperloop. More specifically, reports regarding the propulsion system design methodology for Hyperloop is minimal. Thus, this paper provides an initial steppingstone to modeling and simulation study for the propulsion system in a Hyperloop. The main contribution of this study is to provide a relatively simple design methodology for any future Hyperloop endeavors. This is shown using state of the art simulators to aid in designing the propulsion system. The design revolves around the linear synchronous motor based on field-oriented control through a three – phase inverter. PSIM is used to develop the model and design the full power system and controller. This includes the DC-DC converter, battery system model, three – phase inverter, and the motor controller. The motor used for modelling is a rotary permanent magnet synchronous motor. Finally, hardware in the loop technology is used to verify and validate the design. The controller design is tested through a Texas Instrument digital signal processor. The real time verification shows matching results with the offline simulation model.

Hand Gesture Recognition of Methods-Time Measurement-1 Motions in Manual Assembly Tasks Using Graph Convolutional Networks

ABSTARCT Gesture recognition is gaining popularity in many fields, including gesture control, robotics, or medical applications. However, the technology is barely used in industrial manufacturing processes due to high costs, a time-consuming configuration, and changes in the workflow. This paper proposes a minimally invasive approach to recognize workers' hand motions in manual assembly tasks. The novelty of this approach is the use of only one camera instead of any other sensors and the application of state-of-the-art graph neural networks. The method relies on monocular RGB video data to predict the basic motions of the industry standard motion-time system Methods-Time Measurement-1. Our two-stage neural network composed of hand key point extraction and adaptive graph convolution delivers accurate classification results in real-time. To train and validate the model, we created a dataset containing 22,000 frames of real-world assembly tasks. The data produced by this method in a production line can be used for motion time verification, assembly-line design, or assembly cost estimation. In a use-case study, we show that the proposed approach can generate Methods-Time Measurement analysis tables. These have so far only been accurately created by human experts. Source code: https://github.com/alexriedel1/Hand-Gesture-Recognition-in-manual-assembly-tasks-using-GCN

A Feature-driven Method for Automating the Assessment of OSINT Cyber Threat Sources

Global malware campaigns and large-scale data breaches show how everyday life can be impacted when the defensive measures fail to protect computer systems from cyber threats. Understanding the threat landscape and the adversaries’ attack tactics to perform it represent key factors for enabling an efficient defense against threats over the time. Of particular importance is the acquisition of timely and accurate information from threats intelligence sources available on the web which can provide additional intelligence on emerging threats even before they can be observed as actual attacks. Currently, specific indicators of compromise (e.g. IP addresses, domains, hashsums of malicious files) are shared in a semi-automated and structured way via so-called threat feeds. Unfortunately, current systems have to deal with the trade-off between the timeliness of such an alert (i.e. warning at the first mention of a threat) and the need to wait for verification by other sources (i.e. warning after multiple sources have verified the threat). In addition, due to the increasing number of open sources, it is challenging to find the right balance between feasibility and costs in order to identify a relatively small subset of valuable sources. In this paper, a method to automate the assessment of cyber threat intelligence sources and predict a relevance score for each source is proposed. Specifically, a model based on meta-data and word embedding is defined and experimented by training regression models to predict the relevance score of sources on Twitter. The results evaluation show that the assigned score allows to reduce the waiting time for intelligence verification, on the basis of its relevance, thus improving the time advantage of early threat detection.

Privacy-Protection Scheme of a Credit-Investigation System Based on Blockchain.

In response to the rapid growth of credit-investigation data, data redundancy among credit-investigation agencies, privacy leakages of credit-investigation data subjects, and data security risks have been reported. This study proposes a privacy-protection scheme for a credit-investigation system based on blockchain technology, which realizes the secure sharing of credit-investigation data among multiple entities such as credit-investigation users, credit-investigation agencies, and cloud service providers. This scheme is based on blockchain technology to solve the problem of islanding of credit-investigation data and is based on zero-knowledge-proof technology, which works by submitting a proof to the smart contract to achieve anonymous identity authentication, ensuring that the identity privacy of credit-investigation users is not disclosed; this scheme is also based on searchable-symmetric-encryption technology to realize the retrieval of the ciphertext of the credit-investigation data. A security analysis showed that this scheme guarantees the confidentiality, the availability, the tamper-proofability, and the ciphertext searchability of credit-investigation data, as well as the fairness and anonymity of identity authentication in the credit-investigation data query. An efficiency analysis showed that, compared with similar identity-authentication schemes, the proof key of this scheme is smaller, and the verification time is shorter. Compared with similar ciphertext-retrieval schemes, the time for this scheme to generate indexes and trapdoors and return search results is significantly shorter.

A Fully Parallel Approach of Model Checking Via Probe Machine

Model checking is a verification technique that explores all possible system states in a brute-force manner. However, the state space can be extremely large for many practical systems and the verification time grows exponentially with the size of systems. It is a major limitation for state-space search algorithms of model checking. This paper presents a novel approach to perform Linear Temporal Logic (LTL) and Computation Tree Logic (CTL) model checking by using the connective probe machine, which is a fully parallel computing model. Our state-space search algorithm is based on the semantics of CTL properties and we design transformation algorithms to transform the model of a system into the structure that can run on the existing probe machine. We propose another approach to find multiple accepting cycles in linear time, which greatly shortens the verification time of LTL model checking. Compared to the traditional model checker, our approach can find multiple counterexamples according to the given property, which can trace as many system defects as possible. Simultaneously, it can greatly reduce the verification time for systems with large state spaces. We develop a model checker called MC2PROBE based on our approach and prove the feasibility and efficiency of our checker by experiments.

Automatization through Practice: The Opportunistic‐Stopping Phenomenon Called into Question

As a theory of skill acquisition, the instance theory of automatization posits that, after a period of training, algorithm‐based performance is replaced by retrieval‐based performance. This theory has been tested using alphabet‐arithmetic verification tasks (e.g., is A + 4 = E?), in which the equations are necessarily solved by counting at the beginning of practice but can be solved by memory retrieval after practice. A way to infer individuals’ strategies in this task was supposedly provided by the opportunistic‐stopping phenomenon, according to which, if individuals use counting, they can take the opportunity to stop counting when a false equation associated with a letter preceding the true answer has to be verified (e.g., A + 4 = D). In this case, such within‐count equations would be rejected faster than false equations associated with letters following the true answers (e.g., A + 4 = F, i.e., outside‐of‐count equations). Conversely, the absence of opportunistic stopping would be the sign of retrieval. However, through a training experiment involving 19 adults, we show that opportunistic stopping is not a phenomenon that can be observed in the context of an alphabet‐arithmetic verification task. Moreover, we provide an explanation of how and why it was wrongly inferred in the past. These results and conclusions have important implications for learning theories because they demonstrate that a shift from counting to retrieval over training cannot be deduced from verification time differences between outside and within‐count equations in an alphabet‐arithmetic task.

A Logic Verification Framework for SFQ and AQFP Superconducting Circuits

Traditional logical equivalence checking (LEC), which plays a major role in the entire chip design process, faces challenges of meeting the requirements demanded by the many emerging technologies that are based on logic models different from the standard complementary metal oxide semiconductor (CMOS). In this article, we propose an LEC framework to be employed in the verification process of superconducting electronics (SCE). Our LEC framework is compatible with the existing CMOS technologies and can also check features and capabilities that are unique to SCE. For instance, the performance of nonresistively biased single-flux quantum (SFQ) circuits benefits from ultradeep pipelining, and verification of such circuits requires new models and algorithms. We, therefore, present the multicycle input dependency circuit model which is a novel model representation of design to explicitly capture the dependency of primary outputs of the circuit on sequences of internal signals and inputs. Embedding the proposed circuit model and several structural checking modules, the process of verification can be independent of the underlying technology and signaling. We benchmark the proposed framework on postsynthesis SFQ and 4-phase adiabatic quantum flux parametron netlists. Results show a comparative verification time of SFQ circuit benchmark, including 16-bit integer divider and ISCAS’85 circuits with respect to the ABC tool for similar CMOS circuits.

Statistical model checking for variability-intensive systems: applications to bug detection and minimization

We propose a new Statistical Model Checking (SMC) method toidentify bugs in variability-intensive systems (VIS). Thestate-space of such systems is exponential in the number ofvariants, which makes the verification problem harder than forclassical systems. To reduce verification time, we propose tocombine SMC with featured transition systems (FTS)—amodel that represents jointly the state spaces of all variants. Ournew methods allow the sampling of executions from one or more(potentially all) variants. We investigate their utility in twocomplementary use cases. The first case considers the problem offinding all variants that violate a given property expressed inLinear-Time Logic (LTL) within a given simulation budget. To achievethis, we perform random walks in the featured transition systemseeking accepting lassos. We show that our method allows us to findbugs much faster (up to 16 times according to our experiments) thanexhaustive methods. As any simulation-based approach, however, therisk of Type-1 error exists. We provide a lower bound and an upperbound for the number of simulations to perform to achieve thedesired level of confidence. Our empirical study involving 59properties over three case studies reveals that our method managesto discover all variants violating 41 of the properties.This indicates that SMC can act as a coarse-grainedanalysis method to quickly identify the set of buggy variants.The second case complements the first one. In case thecoarse-grained analysis reveals that no variant can guarantee tosatisfy an intended property in all their executions, one shouldidentify the variant that minimizes the probability of violatingthis property. Thus, we propose a fine-grained SMC method thatquickly identifies promising variants and accurately estimates theirviolation probability. We evaluate different selection strategiesand reveal that a genetic algorithm combined with elitist selectionyields the best results.

Predicting Formal Verification Resource Needs (Computation Time and Memory) through Machine Learning

This paper presents an application of Machine Learning (ML) for estimating the resource requirements (time and memory) for formal verification of digital systems. Formal Verification has become the main bottleneck in the digital design process. As designs grow in size, the verification cost exponentially increases. Some verification efforts fail when the cost of the verification is too large. The most common failures are due to state explosion problem when memory runs out or when verification does not meet time constraints and cannot complete within the given time. For this reason, having an estimation of the computation resources beforehand can be valuable. In this work, a neural network is used to predict the formal verification cost for a diverse collection of designs. Experiments with a large variety of Verilog benchmarks have been conducted. In each experiment, the specific features of a given design are extracted, the design is synthesized and formally verified, and the verification time and memory usage are recorded. The neural network then uses this data to learn the correlation between design features and verification cost. A genetic algorithm experimented with different neural network designs to determine an appropriate framework. The experimental results confirm that the developed neural network can predict the resource requirements and memory usage with reasonable accuracy.

How to Delegate Computations: The Power of No-Signaling Proofs

We construct a 1-round delegation scheme (i.e., argument-system) for every language computable in time t = t ( n ), where the running time of the prover is poly ( t ) and the running time of the verifier is n · polylog ( t ). In particular, for every language in P we obtain a delegation scheme with almost linear time verification. Our construction relies on the existence of a computational sub-exponentially secure private information retrieval ( PIR ) scheme. The proof exploits a curious connection between the problem of computation delegation and the model of multi-prover interactive proofs that are sound against no-signaling (cheating) strategies , a model that was studied in the context of multi-prover interactive proofs with provers that share quantum entanglement, and is motivated by the physical principle that information cannot travel faster than light. For any language computable in time t = t ( n ), we construct a multi-prover interactive proof ( MIP ), that is, sound against no-signaling strategies, where the running time of the provers is poly ( t ), the number of provers is polylog ( t ), and the running time of the verifier is n · polylog ( t ). In particular, this shows that the class of languages that have polynomial-time MIP s that are sound against no-signaling strategies, is exactly EXP . Previously, this class was only known to contain PSPACE . To convert our MIP into a 1-round delegation scheme, we use the method suggested by Aiello et al. (ICALP, 2000), which makes use of a PIR scheme. This method lacked a proof of security. We prove that this method is secure assuming the underlying MIP is secure against no-signaling provers.

Polaris: Transparent Succinct Zero-Knowledge Arguments for R1CS with Efficient Verifier

AbstractWe present a new zero-knowledge succinct argument of knowledge (zkSNARK) scheme for Rank-1 Constraint Satisfaction (RICS), a widely deployed NP-complete language that generalizes arithmetic circuit satisfiability. By instantiating with different commitment schemes, we obtain several zkSNARKs where the verifier’s costs and the proof size range fromO(log2N) toO(N)O\left( {\sqrt N } \right)depending on the underlying polynomial commitment schemes when applied to anN-gate arithmetic circuit. All these schemes do not require a trusted setup. It is plausibly post-quantum secure when instantiated with a secure collision-resistant hash function. We report on experiments for evaluating the performance of our proposed system. For instance, for verifying a SHA-256 preimage (less than 23k AND gates) in zero-knowledge with 128 bits security, the proof size is less than 150kB and the verification time is less than 11ms, both competitive to existing systems.

A Compact and High-Performance Hardware Architecture for CRYSTALS-Dilithium

The lattice-based CRYSTALS-Dilithium scheme is one of the three thirdround digital signature finalists in the National Institute of Standards and Technology Post-Quantum Cryptography Standardization Process. Due to the complex calculations and highly individualized functions in Dilithium, its hardware implementations face the problems of large area requirements and low efficiency. This paper proposes several optimization methods to achieve a compact and high-performance hardware architecture for round 3 Dilithium. Specifically, a segmented pipelined processing method is proposed to reduce both the storage requirements and the processing time. Moreover, several optimized modules are designed to improve the efficiency of the proposed architecture, including a pipelined number theoretic transform module, a SampleInBall module, a Decompose module, and three modular reduction modules. Compared with state-of-the-art designs for Dilithium on similar platforms, our implementation requires 1.4×/1.4×/3.0×/4.5× fewer LUTs/FFs/BRAMs/DSPs, respectively, and 4.4×/1.7×/1.4× less time for key generation, signature generation, and signature verification, respectively, for NIST security level 5.

AI-enabled privacy-preservation phrase with multi-keyword ranked searching for sustainable edge-cloud networks in the era of industrial IoT

The integration of sensing technologies and cloud computing signifies the design perspectives of electronic healthcare systems. It has its own application domain to upload the clinical data of the patients and treatment procedures to the cloud server. Moreover, the data user may process the queries with suitable sensing parameters to obtain an appropriate medical record. As a result, the development of the Industrial Internet of Things (I-IoT) demands practical insights, trustworthiness, and reliability of intelligent automation to prevent the occurrence of potential risks in the process of production. In the past, multi-keyword searching (MKS) over encrypted cloud data has attracted researchers' attention. As cloud computing is highly practicing, data owners may easily outsource any kind of system data to commercial sites using the Industrial Internet of Things (I-IoT). However, data privacy and protection should be ensured using encryption techniques before any sensitive data is outsourced over insecure public networks. Providing cloud data encryption and secure keyword searching still exist as challenging issues. In I-IoT, cloud computing deals with a large amount of data users and documents, thus a technique like MKS is highly necessitated to process the search request and secure query processing. Thus, this paper presents a privacy-preservation phrase with multi-keyword ranked searching (PPP-MKRS) that introduces optimized filtering, binary tree index structure, and conjunctive keyword search to achieve secure searching efficiency. The experimental analysis shows that the proposed PPP-MKRS scheme consumes less computation, storage, and verification time in comparison with other searching encryption techniques.

Energy Efficient Multiprocessing Solo Mining Algorithms for Public Blockchain Systems

Blockchain as a decentralized distributed ledger is revolutionizing the world with a secure design data storage mechanism. In the case of Bitcoin, mining involves a process of packing transactions in a block by calculating a random number termed as a nonce. The nonce calculation is done by special nodes called miners, and all the miners follow the Proof of Work (PoW) mining mechanism to perform the mining task. The transaction verification time in PoW-based blockchain systems, i.e., Bitcoin, is much slower than other digital transaction systems such as PayPal. It needs to be quicker if a system adapts PoW-based blockchain solutions, where there are thousands of transactions being computed at a time. Besides this, PoW mining also consumes a lot of energy to calculate the nonce of a block. Mining pools resulting into aggregated hashpower have been a popular solution to speed up the PoW mining, but they can be attacked by using different types of attacks. Parallel computing can be used to speed up the solo mining methods by utilizing the multiple processes of the contributing processors. In this research, we analyze various consensus mechanisms and see that the PoW-based blockchain systems have the limitations of low transaction confirmation time and high energy consumption. We also analyze various types of consensus layer attacks and their effects on miners and mining pools. To tackle these issues, we propose parallel PoW nonce calculation methods to accelerate the transaction verification process especially in solo mining. We have tested our techniques on different difficulty levels, and our proposed techniques yield better results than the traditional nonce computation mechanisms.

Practical SM2-Based Multisignature Scheme with Applications to Vehicular Networks

In vehicular networks, the increasing value of transportation data and scale of connectivity also brings many security and privacy concerns. Peer authentication and message integrity are two vital security requirements to ensure safe transportation system. Because of the constrained resources of the units performing the cryptographic components, the proposed security-enhancing schemes should be lightweight and scalable. In this paper, we present a multisignature scheme derived from the SM2 signature which enables a group of parties to collaboratively sign a message and generate a compact joint signature at the end. Our scheme requires no preprocessing or interactions among the parties before signing, and its performance matches or surpasses known ones in terms of signing time, verification time, and signature size. Therefore, our scheme is also suitable for vehicular networks, with the goal to enhance security with small computation and storage cost.

ZkrpChain: Towards multi-party privacy-preserving data auditing for consortium blockchains based on zero-knowledge range proofs

Consortium blockchain has been widely used in different scenarios, where blockchain members demand that their uploaded data could be audited based on data range under their identities without exposing the data themselves. However, so far, current solutions only support simple (e.g., standard-range) privacy-preserving data auditing, and can not be used in multi-party scenarios. To address the problem, we propose zkrpChain, which focuses on protection of the integrity and privacy of the data uploaded by blockchain members while leaving their identities public. In zkrpChain, which is based on Hyperledger Fabric and Bulletproofs, both standard-range and arbitrary-range zero-knowledge range proofs generation and verification are supported. To improve the efficiency, the aggregation of multiple proofs and batch verification are developed. Furthermore, to broaden the application scenarios, we also develop multi-party proofs generation and verification. To support further development, we provide chaincodes, related APIs together with the client codes. Finally, we conduct experiments to evaluate the performance of zkrpChain, and the results show that as the total data size grows exponentially, the proof and verification time only grow linearly while the total on-chain storage grows logarithmically.

The novel noninvasive method for prediction of long-term specific therapy efficacy in patients with IPAH and inoperable CTEPH

Abstract Introduction The main aim of the treatment in patients (pts) with idiopathic pulmonary arterial hypertension (IPAH) is the achivement of low risk of fatal events during the year, that reflecting the complex of nonivasive and invasive parameters presented in the risk stratification scale of ESC and ERS 2015 guidelines. However, in pts with chronic thromboembolic pulmonary hypertension (CTEPH) there is a lack of evidence to use this scale. Also there is some limitation to perform invasive diagnostics in every patient for complex risk assessment. Thus, the searching for the novel noninvasive markers for prediction of treatment efficacy need further investigation. Purpose To develop the novel approach for predicting of long-term specific therapy efficacy using noninvasive methods of diagnostic in IPAH and CTEPH pts. Methods The study included 88 IPAH and 38 inoperable CTEPH pts. At the time of diagnosis verification and after 1 year of treatment the complex assessment, including 6-minute walking test, cardiopulmonary exercise test, 2D and 3D Echo, right heart catheterization, NT-proBNP level assessment were made. The efficacy of treatment was determined as achievement of cardiac index (CI) equal or more than 2,5 l/min/m2 after 1 year of treatment. To create a model, predicting long-term specific therapy efficacy the classification trees with the full enumeration method for one-dimensional branches C&amp;RT were used. The impact of factors in prediction of the long-term specific therapy efficacy was demonstrated in the ranks of significance of predictors in units (U). Results At baseline all pts with IPAH/inoperable CTEPH were out of the low-risk area. Whereas, after 1 year of treatment 27% of IPAH/CTEPH pts achieved a low risk of fatal events. To develop a model for predicting of the long-term specific therapy efficacy (&amp;gt;1 year) in patients with IPAH/ CTEPH the most significant predictors of achievement of CI equal or more, than 2.5 l/min/m2 were: age (73 U), right ventricular end-diastolic/end-systolic volumes (85 U/84 U) at the time of diagnosis verification, and the dynamics of NT-proBNP (100 U.). According to the most valuble factors the model of long-term therapy efficacy was created: the value of dynamic (Δ) of NT-proBNP from &amp;gt;−376,4 to &amp;lt;30,4 pg/ml after 12 months of treatment with combination of right ventricular end-diastolic volume &amp;lt;150,5 ml at the time of diagnosis verification predict the therapy efficacy. Conclusion The implementation of novel noninvasive method, that predict specific therapy efficacy, allows making a timely decision of therapy escalation in IPAH and CTEPH pts by the first and subsequent years of treatment. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): National medical research center of Cardiology, Moscow