Verification Time Research Articles

Fraud Detection in Bank Loans with KYC Verification System

In today's technologically-driven financial landscape, bank loan fraud poses a significant threat to both financial institutions and their customers. While traditional methods of fraud detection have been moderately effective, the increasing sophistication of fraudsters necessitates the implementation of more advanced measures. This research proposes an integrated system for bank loan fraud detection that leverages the robustness of Know Your Customer (KYC) verification. By combining traditional KYC processes with advanced machine learning algorithms, this system seeks to provide a more comprehensive approach to detecting and preventing fraudulent loan applications. Initial results indicate a marked reduction in successful fraud attempts, as well as a decrease in false positives compared to conventional systems. Furthermore, the integrated system offers enhanced customer experience by streamlining the loan application process, reducing verification times, and ensuring greater security of personal data. As financial institutions continue to grapple with the challenges of fraud, this research underscores the importance of integrating traditional verification methods with cutting-edge technological solutions for optimal results

Abstraction and Refinement: Towards Scalable and Exact Verification of Neural Networks

As a new programming paradigm, deep neural networks (DNNs) have been increasingly deployed in practice, but the lack of robustness hinders their applications in safety-critical domains. While there are techniques for verifying DNNs with formal guarantees, they are limited in scalability and accuracy. In this article, we present a novel counterexample-guided abstraction refinement (CEGAR) approach for scalable and exact verification of DNNs. Specifically, we propose a novel abstraction to break down the size of DNNs by over-approximation. The result of verifying the abstract DNN is conclusive if no spurious counterexample is reported. To eliminate each spurious counterexample introduced by abstraction, we propose a novel counterexample-guided refinement that refines the abstract DNN to exclude the spurious counterexample while still over-approximating the original one, leading to a sound, complete yet efficient CEGAR approach. Our approach is orthogonal to and can be integrated with many existing verification techniques. For demonstration, we implement our approach using two promising tools, Marabou and Planet , as the underlying verification engines, and evaluate on widely used benchmarks for three datasets ACAS , Xu , MNIST , and CIFAR-10 . The results show that our approach can boost their performance by solving more problems in the same time limit, reducing on average 13.4%–86.3% verification time of Marabou on almost all the verification tasks, and reducing on average 8.3%–78.0% verification time of Planet on all the verification tasks. Compared to the most relevant CEGAR-based approach, our approach is 11.6–26.6 times faster.

Isolating the impact of a visual search template's color and form information on search guidance and verification times.

Visual search can be guided by biasing one's attention towards features associated with a target. Prior work has shown that high-fidelity, picture-based cues are more beneficial to search than text-based cues. However, typically picture cues provide both detailed form information and color information that is absent from text-based cues. Given that visual resolution deteriorates with eccentricity, it is not clear that high-fidelity form information would benefit guidance to peripheral objects - much of the picture benefit could be due to color information alone. To address this, we conducted a search task with eye-tracking that had four types of cues that comprised a 2 (text/pictorial cue) × 2 (no color/color) design. We hypothesized that color information would be important for efficient search guidance while high-fidelity form information would be important for efficient verification times. In Experiment 1 cues were a colored picture of the target, a gray-scaled picture of the target, a text-based cue that included color (e.g., "blue shoe"), or a text-based cue without color (e.g., "shoe"). Experiment 2 was a replication of Experiment 1, except that the color word in the text-based cue was presented in the precise color that was the dominant color in the target. Our results show that high-fidelity form information is important for efficient verifications times (with color playing less of a role) and color is important for efficient guidance, but form information also benefits guidance. These results suggest that different features of the cue independently contribute to different aspects of the search process.

Prospective comparison of two methods for correct ureteral stent placement in pediatric laparoscopic pyeloplasty

Ureteral stent placement during laparoscopic pyeloplasty is a common procedure in pediatric patients. Although an apparently safe maneuver, ascending placement of the stent can lead to complex removal or repositioning reinterventions. In this study we compare two methods for intraoperative verification of correct positioning. Prospective observational study collecting data on laparoscopic pyeloplasties in pediatric patients in our center over three years. We carried out descriptive and univariate comparative analyses. Data were compared between ultrasound and reflux visualized by the catheter after intraoperative salineinjection into the bladder through the urethral catheter. We recorded time to catheter visualization in both ultrasonography and in reflux from the start of bladder instillation, as well as bladder volume at the time of placement verification with each method. Data were collected from 20 patients (15 male and 5 female) with a median age of 48 months. Pyeloplasty was successful in 100% of the sample (as observed by ultrasound and MAG-3), while one patient had postoperative leak requiring nephrostomy placement. Correct distal positioning of the ureteral stent could be verified by intraoperative ultrasound and reflux in all cases. Using reflux, the bladder volume needed to verify correct positioning exceeded the age-related maximum in half the cohort, while on ultrasound, the stent was visualized in the bladder without reaching the maximum bladder capacity for age in any case (p=0.02 comparing percentages). Likewise, mean time to verification was lower with ultrasound than with reflux (61.8s versus 115s), but without these differences reaching statistical significance (p=0.14). The present study is the first to compare two methods to verify the correct positioning of the ureteral stent in laparoscopic pyeloplasties in pediatric patients. Our results show that both intraoperative ultrasound and visualization of reflux are useful methods, although ultrasound requires a lower volume of saline instilled through the bladder catheter for verification. This work can be very useful for the daily clinical practice of urologists and pediatric surgeons. Both intraoperative ultrasound and visualization of reflux are useful methods to verify the correct positioning of the ureteral stent in laparoscopic pyeloplasty of pediatric patients. With ultrasound, a smaller volume is required to check for reflux. Although ultrasound is faster for verification, there are no differences in procedural times.

Big data-assisted student’s English learning ability appraisal model using fuzzy logic system

The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice.

Towards Building a Faster and Incentive Enabled Privacy-Preserving Proof of Location Scheme from GTOTP

In recent years, there has been significant growth in location-based services (LBSs) and applications. These services empower users to transmit their location data to location service providers, thereby facilitating the provisioning of pertinent resources and services. However, in order to prevent malicious users from sending fake location data, users must attest to their location for service providers, namely, through a proof of location (PoL). Such a proof should additionally prevent attackers from being able to obtain users’ identity and location information through it. In this paper, we propose an efficient privacy-preserving proof of location (PPPoL) scheme. The scheme is based on the standard cryptographic primitives, including Group Time-based One-Time Password (GTOTP) and public key encryption, which achieves entity privacy, location privacy, and traceability. Unlike the previous GTOTP-based PPPoL scheme, our scheme enables instant location verification with additional hash operations. To encourage the active participation of witnesses in location proofs, we propose an incentive mechanism based on smart contracts. Additionally, we implement a proof of concept of our PPPoL scheme on an Android device. Our experimental results show that proof generation and verification time are on the order of milliseconds. Meanwhile, the total overhead for the incentive mechanism amounts to 0.0011 ETH. This result is practical for mobile device-based LBSs.

Efficient and secure confidential transaction scheme based on commitment and aggregated zero-knowledge proofs

ABSTRACT As a distributed ledger technology, blockchain has broad applications in many areas such as finance, agriculture, and contract signing due to its advantages of being tamperproof and difficult to forge. However, the open nature of blockchain also introduces severe privacy issues, currently cryptocurrency privacy protection solutions under account-based models cannot balance the internal verification time and confidentiality of transactions. In order to improve the confidentiality and efficiency of transactions under the account-based model, this paper proposes an access control anonymous payment scheme based on homomorphic commitment and aggregated zero-knowledge proofs, focusing on the realization of a one-to-many anonymous transfer function, one-to-one transfer function, deposit function and withdraw function, to ensure the privacy of a transaction data while reducing verification time and Gas costs. We evaluated our approach on a proof-of-concept implementation by generating Solidity contracts and implemented some interesting contracts. The experimental results show that this scheme not only consumes lower gas, but also reduces the internal time during transaction generation. In addition, our solution has established a one to many transfer transaction scheme, and the proof verification time after aggregation is constant. Thus the efficiency of this scheme is particularly well suited for the digital finance scenarios.

Smart Grids Data Aggregation Method on Paillier Homomorphic Encryption

ABSTRACT Smart grid (SGs) is a highly integrated power system, it is gradually replacing the traditional power grid, but at present there are problems in the SGs data aggregation, such as user privacy leakage, grid data query inflexibly and data leakage. In order to solve these problems, this paper studied the SGs data aggregation method based on Paillier homomorphic encryption (HE). In this paper, the Paillier HE algorithm was used to study the power grid data, and the encrypted data was numerically calculated under different encryption states. It used the cloud computing center in the blockchain to directly aggregate the ciphertext data generated by users, and used the hash function to set the secret key to prevent the ciphertext data from being tampered with, and obtained the aggregate result of the original data after decryption by the power grid management center. When the number of security parameters was 30,000, the encryption time required by this method was 21.71 seconds. When the number of smart meters (SMs) was 80, the signature verification time required by this method was 2.31 seconds.

When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming

AI powered code-recommendation systems, such as Copilot and CodeWhisperer, provide code suggestions inside a programmer's environment (e.g., an IDE) with the aim of improving productivity. We pursue mechanisms for leveraging signals about programmers' acceptance and rejection of code suggestions to guide recommendations. We harness data drawn from interactions with GitHub Copilot, a system used by millions of programmers, to develop interventions that can save time for programmers. We introduce a utility-theoretic framework to drive decisions about suggestions to display versus withhold. The approach, conditional suggestion display from human feedback (CDHF), relies on a cascade of models that provide the likelihood that recommended code will be accepted. These likelihoods are used to selectively hide suggestions, reducing both latency and programmer verification time. Using data from 535 programmers, we perform a retrospective evaluation of CDHF and show that we can avoid displaying a significant fraction of suggestions that would have been rejected. We further demonstrate the importance of incorporating the programmer's latent unobserved state in decisions about when to display suggestions through an ablation study. Finally, we showcase how using suggestion acceptance as a reward signal for guiding the display of suggestions can lead to suggestions of reduced quality, indicating an unexpected pitfall.

Sequentially Triggering “Time-Bomb” Trojan into Hardware Wired Microsequencer

This paper presents an analysis of a sequential Triggered “Time Bomb” hardware Trojan (HT). Major security concerns have been rising up since malicious modification of hardware or fabrication IC can lead to an altered functional behavior, potentially with disastrous consequences in safety-critical applications. Due to the stealthy nature of the hardware Trojans the conventional design and time verification and post manufacturing testing cannot be readily extended. There are a large number of possible instances and operating modes for the hardware Trojans in a digital system. Since the hardware Trojan insertion can modify the functionality of the digital integrated circuit (IC), alter its behavior, generate denial of service (DoS), the HT threats should be analyzed with maximum importance through the entire lifecycle of the IC.

Evaluation of a remote hybrid staffing model for investigational drug service pharmacists.

This project compared the time to complete investigational prescription drug order verification by investigational drug service (IDS) pharmacists in an onsite vs offsite (hybrid) staffing model, evaluating the impact of remote work on verification time. Parenteral IDS drug orders from August 2019 through June 2022 were analyzed. Electronic medical records were timestamped for each order, first verification, second verification, and medication administration. The project was divided into four 6-month time periods to allow for 2 independent comparisons of onsite vs hybrid staffing models. A survey was given retrospectively to IDS pharmacists for the last hybrid timeframe to assess work satisfaction and burnout. A total of 4,398 orders were evaluated. The time from order entry to first verification was 14 minutes (n = 1,248) during the first onsite time period compared to 13 minutes (n = 1,042) during the first hybrid period (P = 0.003). The time for order verification was similar between the second onsite and hybrid periods (12 minutes [n = 1,041] vs 10 minutes [n = 1,067], respectively; P < 0.0001). The staff satisfaction survey for the hybrid model showed high levels of job satisfaction and no self-reported burnout. The remote hybrid staffing model did not result in a meaningful change in the time from order placement to medication administration within the IDS pharmacy. Hybrid staffing also resulted in greater job satisfaction among IDS pharmacists. This study highlights the benefits of implementing remote work practices in the pharmacy practice setting.

Performance evaluation of a quantum-resistant Blockchain: a comparative study with Secp256k1 and Schnorr

Popular Secp256k1 and Schnorr algorithms offer strong security in current Blockchains. However, they are vulnerable to quantum attacks. To solve this problem, several quantum-resistant algorithms have been proposed. However, the performance evaluations and tangible analyses of these algorithms on current Blockchains have not been studied yet. In this context, a performance analysis of quantum-resistant algorithms on a Blockchain can provide valuable insight into the efficiency of quantum-resistant algorithms in real-world scenarios. To address this need, we prototyped and analyzed a quantum-resistant Blockchain using the Falcon algorithm. Falcon is selected because it provides smaller signature and key size compared to Crystals-Dilithium and Sphincs+. We then measured in real-time the key size, transaction signature size, and transaction verification time. The paper also discusses the potential scalability limitations of the proposed quantum-resistant Blockchain and suggests an approach to select quantum-resistant algorithms based on different Blockchain use cases. Our approach and benchmark results have implications for the future development and adoption of quantum-resistant Blockchains.

ANNProof: Building a verifiable and efficient outsourced approximate nearest neighbor search system on blockchain

Data-as-a-service is increasingly prevalent, with outsourced K-approximate nearest neighbors search (K-ANNS) gaining popularity in applications like similar image retrieval and anti-money laundering. However, malicious search service providers and dataset providers in current outsourced query systems cause incorrect user query results. To address this, we propose ANNProof, a novel framework supporting verifiable outsourced K-ANNS on the blockchain. ANNProof utilizes two innovative authenticated data structures (ADS), the Merkle HNSW node tree, and the Merkle vector identifier tree, for efficient K-ANNS query verification. Additionally, we employ the Merkle sharding tree as an ADS optimization technique, reducing the overhead of delivering verifiable queries. We implement the ADS construction protocol based on blockchain smart contracts to ensure tamper-evident datasets and enhance execution efficiency via a contract state consistency checking scheme. Extensive evaluations show that ANNProof reduces VO generation time, result verification time, and VO size by 160, 120, and 28×, respectively, compared to the state-of-the-art systems. Moreover, ADS construction using ANNProof takes at most 2% of the index construction time, resulting in a negligible overhead for implementing verifiable queries. Meanwhile, the sharding optimization accelerates ADS updates by 53×.

Decentralized IoT Data Authentication with Signature Aggregation

The rapid expansion of the Internet of Things (IoT) has introduced significant challenges in data authentication, necessitating a balance between scalability and security. Traditional approaches often rely on third parties, while blockchain-based solutions face computational and storage bottlenecks. Our novel framework employs edge aggregating servers and Ethereum Layer 2 rollups, offering a scalable and secure IoT data authentication solution that reduces the need for continuous, direct interaction between IoT devices and the blockchain. We utilize and compare the Nova and Risc0 proving systems for authenticating batches of IoT data by verifying signatures, ensuring data integrity and privacy. Notably, the Nova prover significantly outperforms Risc0 in proving and verification times; for instance, with 10 signatures, Nova takes 3.62 s compared to Risc0’s 369 s, with this performance gap widening as the number of signatures in a batch increases. Our framework further enhances data verifiability and trust by recording essential information on L2 rollups, creating an immutable and transparent record of authentication. The use of Layer 2 rollups atop a permissionless blockchain like Ethereum effectively reduces on-chain storage costs by approximately 48 to 57 times compared to direct Ethereum use, addressing cost bottlenecks efficiently.

I2C master RTL development using Verilog HDL and verification using UVM

The objective of the project is to develop the I2C Master RTL using VHDL and verify it using UVM. I2C is a two-wire, bi-directional serial bus that provides a simple and efficient method of data exchange between devices, suitable for short-distance communication between multiple devices. The I2C Master facilitates communication between the Core and various slaves in the chip. The I2C Master IP implemented here is a subset of the full I2C protocol, as some features are not supported. The RTL design of I2C is open source, and its functional verification is performed using System Verilog and UVM. The Universal Verification Methodology was developed to provide a well-structured and reusable verification environment without interfering with the device under test (DUT). This paper highlights the reusability of I2C using UVM, details the construction of the verification environment, and describes the implementation of test cases for this protocol. Additionally, the performance metrics and verification coverage results are discussed, demonstrating the efficiency and robustness of the developed environment. The results show significant improvements in verification time and accuracy, validating the approach.

Abstract WP86: Data Collection Impacts on Study Costs: A Real-World Analysis

Introduction: Costs are a major barrier to conducting high-quality clinical trials, yet specific determinants of operational costs are understudied. Budgets are frequently planned assuming additional data fields increase cost in a proportional manner. We compared estimated costs for adding data fields to real-world costs as data fields increase. Methods: We studied the impact of data fields on operational costs for three Phase III acute stroke clinical trials of similar design, clinical complexity, and logistics. We retrospectively reviewed records of coordinator time logged for completion of data fields per study during 2021 and 2022. Study data management hours included time for data entry, query resolution, and data verification as requested by the study monitor. Our standard rate per hour was used to calculate actual cost for each clinical trial. We estimated the cost of a single data field by dividing the averaged cost per subject by the total data fields collected for the least expensive study. We used this value to estimate cost and compared that to the actual costs. Results: Study 1 was the least expensive study, and thus used to calculate the estimated cost of a single data field. There were 277 data fields and a cost of $513 per patient, the cost per data field was calculated as $1.85. Study 2 had 775 data fields collected per subject and should have an estimated cost of $1,436.40. The actual cost was $2,291.57 which is 1.6 times higher than expected. Study 3 had 2245 data fields collected per patient and should have an estimated cost of $4,155.30. The actual cost was $20,176.65 which is 4.86 times higher than expected. Results are summarized in Figure 1. Conclusion: Our analysis of real-world study data management costs demonstrate that additional data fields have a disproportionate increase in cost. Assuming a proportional increase could lead to inaccurate estimates with large budget and operational cost impacts when a study is implemented.

An Efficient Reduction of Timer Interrupts for Model Checking of Embedded Assembly Programs

In verifying programs for embedded systems, it is essential to reduce the verification time because state explosion may occur during model checking. One solution is to reduce the number of interrupt handler executions. In particular, when periodic interrupts such as timer interrupts are incorporated, it is necessary to know the physical time. In this paper, we define a control flow automaton (CFA) that can handle time and propose an algorithm based on interrupt handler execution reduction (IHER). The proposed method reduces the number of interrupt executions, including timer interrupts. A case study verified the effectiveness of this algorithm.

LoRe: A Programming Model for Verifiably Safe Local-first Software

Local-first software manages and processes private data locally while still enabling collaboration between multiple parties connected via partially unreliable networks. Such software typically involves interactions with users and the execution environment (the outside world). The unpredictability of such interactions paired with their decentralized nature make reasoning about the correctness of local-first software a challenging endeavor. Yet, existing solutions to develop local-first software do not provide support for automated safety guarantees and instead expect developers to reason about concurrent interactions in an environment with unreliable network conditions. We propose LoRe , a programming model and compiler that automatically verifies developer-supplied safety properties for local-first applications. LoRe combines the declarative data flow of reactive programming with static analysis and verification techniques to precisely determine concurrent interactions that violate safety invariants and to selectively employ strong consistency through coordination where required. We propose a formalized proof principle and demonstrate how to automate the process in a prototype implementation that outputs verified executable code. Our evaluation shows that LoRe simplifies the development of safe local-first software when compared to state-of-the-art approaches and that verification times are acceptable.

Blockchain based transparent and reliable framework for wheat crop supply chain.

The wheat crop that fulfills 35% of human food demand is facing several problems due to a lack of transparency, security, reliability, and traceability in the existing agriculture supply chain. Many systems have been developed for the agriculture supply chain to overcome such issues, however, monopolistic centralized control is the biggest hurdle to realizing the use of such systems. It has eventually gained consumers' trust in branded products and rejected other products due to the lack of traceable supply chain information. This study proposes a blockchain-based framework for supply chain traceability which provides trustable, transparent, secure, and reliable services for the wheat crop. A crypto token called wheat coin (WC) has been introduced to keep track of transactions among the stakeholders of the wheat supply chain. Moreover, an initial coin offering (ICO) of WC, crypto wallets, and an economic model are proposed. Furthermore, a smart contract-based transaction system has been devised for the transparency of wheat crop transactions and conversion of WC to fiat and vice versa. We have developed the interplanetary file system (IPFS) to improve data availability, security, and transparency which stores encrypted private data of farmers, businesses, and merchants. Lastly, the results of the experiments show that the proposed framework shows better performance as compared to previous crop supply chain solutions in terms of latency to add-blocks, per-minute transactions, average gas charge for the transaction, and transaction verification time. Performance analysis with Bitcoin and Ethereum shows the superior performance of the proposed system.

Islamic dogmatic texts in Pyotr Tolstoy’s translation of «The History of the Present State of the Ottoman Empire»: the first fixation of adhan in Russian literature

The article discusses supposedly the first fixation of the text of the adhan in Russian literature, which is found in the Russian translation of Paul Ricaut's treatise entitled «The History of the Present State of the Ottoman Empire». This translation was made from the Italian version by Pyotr Tolstoy during his stay in the Ottoman Empire in 1702–1714. Already in the English edition of the treatise of 1686 there are errors in the transliteration of the Arabic text of adhan, and its meaning is not fully conveyed. For the English reader, only takbir and shahada were translated, that are the most characteristic dogmatic formulae that Europeans could be familiar with from the other sources. The number of transliteration errors has increased in French and Italian translations. In the Polish translation, transliteration errors were corrected, but the gap in the transmission of content was not filled. Pyotr Tolstoy, not being an orientalist, did not make an attempt to critically comprehend the distorted transmission of Arabic fragments in the Italian translation of Historia and did not fill in the missing readings in the translation, for example, by attracting informants who knew the language and basic tenets of Islam. This is also not done during the re-preparation of the Russian translation for publication in 1741, and the Polish translation, which was partly used at that time for verification, was not referred in this case. Previous experiences of transmitting basic Islamic formulae also remain unfamiliar to Tolstoy, which is not surprising, given their fragmentation and occasional nature.