Strong Verification Research Articles

Practical and Robust Federated Learning With Highly Scalable Regression Training.

Privacy-preserving federated learning, as one of the privacy-preserving computation techniques, is a promising distributed and privacy-preserving machine learning (ML) approach for Internet of Medical Things (IoMT), due to its ability to train a regression model without collecting raw data of data owners (DOs). However, traditional interactive federated regression training (IFRT) schemes rely on multiple rounds of communication to train a global model and are still under various privacy and security threats. To overcome these problems, several noninteractive federated regression training (NFRT) schemes have been proposed and applied in a variety of scenarios. However, there are still several challenges: 1) how to protect the privacy of DOs' local dataset; 2) how to realize highly scalable regression training without linear dependence on sample dimension; 3) how to tolerate DOs' dropout; and 4) how to enable DOs to verify the correctness of aggregated results returned from the cloud service provider (CSP). In this article, we propose two practical noninteractive federated learning schemes with privacy-preserving for IoMT, named homomorphic encryption based NFRT (HE-NFRT) and double-masking protocol based NFRT (Mask-NFRT), respectively, which are based on a comprehensive consideration of NFRT, privacy concerns, high-efficiency, robustness, and verification mechanism. The security analyses display that our proposed schemes are able to protect the privacy of DOs' local training data, resist collusion attack, and support strong verification to each DO. The performance evaluation results demonstrate that our proposed HE-NFRT scheme is desirable for a high-dimensional and high-security IoMT application while Mask-NFRT scheme is desirable for a high-dimensional and large-scale IoMT application.

Data-driven economic MPC with asymptotic stability and strong duality verification using Hankel matrix

We consider the problem of dynamic regulation with an economic cost function to control unknown linear systems, in which improving the economic performance and guaranteeing the stability of economical optimal equilibrium point are control objectives. A data-driven economic MPC scheme is presented using measured input-output trajectories without a prior system identification step. Our method uses Hankel matrices which include one input-output data trajectory for prediction in economic MPC, while persistently exciting of the input generating the data is needed. One of the novelties of the presented framework is directly verifying the strong duality property from input-output trajectory with the general cost function, considered as the supply rate. This is used to find a Lyapunov function for data-driven economic MPC. Under the strong duality assumption, asymptotic stability of the economical optimal equilibrium point for the closed-loop system with terminal equality constraint is guaranteed. The proposed data-driven economic MPC approach needs only persistently exciting data trajectory along with an upper bound on the system order and need no model description and no online parameter estimation. The proposed scheme applicability compared to the existing model-based economic MPC and data-driven MPC is illustrated for continuous stirred tank reactor (CSTR) and a numerical example and the robustness of the proposed scheme is evaluated in the case of measurement noise, as well as nonlinear model for CSTR system.

LabourCentre - Bridging the Gap Between Clients and Skilled Workers

This research paper presents a web application that aims to simplify the hiring and contract assignment of labourers, promoting a more effective and transparent relationship between employers and qualified workers. The application guarantees thorough verification, makes labour registration easier, and makes contract assignments easier. It has an extensive labourer registration module that enables employees to set up profiles with thorough backgrounds and skill sets. Prospective clients are reassured that labourer profiles are authentic by a strong verification mechanism. Customers can peruse an array of au- thenticated workers, making knowledgeable selections predicated on competencies, background, and additional factors. Clients can create, modify, and assign contracts to qualified labourers using an easy-to-use portal that streamlines the contract assignment process. The research explores the impact of the platform on efficiency and transparency, focusing on user satisfaction and adoption rates. Index Terms—labourer, customer, MERN, open source, User friendly, Interface.

The First Robust Evidence Showing a Dark Matter Density Spike Around the Supermassive Black Hole in OJ 287

Black hole dynamics suggests that dark matter would redistribute near a supermassive black hole (SMBH) to form a density spike. However, no direct evidence of a dark matter density spike around an SMBH has been identified. In this Letter, we present the first robust evidence showing a dark matter density spike around an SMBH. We revisit the data of the well-known SMBH binary OJ 287 and show that the inclusion of the dynamical friction due to a dark matter density spike around the SMBH can satisfactorily account for the observed orbital decay rate. The derived spike index γsp=2.351−0.045+0.032 gives an excellent agreement with the value γ sp = 2.333 predicted by the benchmark model assuming an adiabatically growing SMBH. This provides a strong verification of the canonical theory suggested two decades ago modeling the gravitational interaction between collisionless dark matter and SMBHs.

ARE YOU A REAL PARTICIPANT? EXPERIENCES WITH FRAUDULENT PARTICIPATION ACROSS TWO FUNDED STUDIES

Abstract While offering financial incentives for participation in research studies is common, less discussed is the potential impact of fraudulent participation for financial gain. Survey and data management software provide safeguards for fraud detection but enabling detection does not automatically weed out fraudulent responses. Few resources provide guidelines for identifying or managing fraudulent participation. Thoughtful processes are important, while also being mindful of normal research behaviors which might get flagged as suspicious, for example due to low literacy skills. This presentation discusses experiences across two studies with adults living with serious illness and/or their family members and two different types of fraudulent participation: online surveys and studies requiring personal interaction. Study one is a pilot instrument validation study collecting online survey data. Recruitment occurred locally through listservs and a research participation database from a large university hospital. Study two is a mixed-methods longitudinal design where survey and interview data are collected via phone or video calls. Participants for this study were recruited nationwide using multiple strategies, including social media. Both offer time-saving insights for dealing with fraudulent participation in research. For example, study one provides tips for planning how to manage spam/bots prior to data collection, including setting up filters to automatically weed out identified spam, creating automatic messaging included in the survey in the event it needs to be paused, and identifying potential problematic patterns in data that bypasses fraud detection. Study 2 emphasizes the importance of creating strong verification procedures for study eligibility when relying on participant reporting.

An Extra Security Measurement for Android Mobile Applications Using the Fingerprint Authentication Methodology

This research discusses the development of an Android application through the integration of fingerprint recognition technology for user authentication as a way of improving security in the system. The app serves as an additional security layer, requiring users to verify their identity before gaining access to any installed applications on their Android device. The use of technologically developed devices with fingerprint scanners that have the capacity for analyzing biological traits facilitates a strong user verification process. The process makes use of a fingerprint previously registered by the user to authenticate user legitimacy, hence providing an efficient authentication process. A set of metrics was introduced and verified against a legacy system, proving that the proposed system surpasses the legacy system. This new approach offers the advantage of serving as an alternative to using PIN numbers or pattern unlocking on Android smartphones.

Secure Enrollment Token Delivery Mechanism for Zero Trust Networks Using Blockchain

Zero Trust Networking (ZTN) is a security model where no default trust is given to entities in a network infrastructure. The first bastion of security for achieving ZTN is strong identity verification. Several standard methods for assuring a robust identity exist (E.g., OAuth2.0, OpenID Connect). These standards employ JSON Web Tokens (JWT) during the authentication process. However, the use of JWT for One Time Token (OTT) enrollment has a latent security issue. A third party can intercept a JWT, and the payload information can be exposed, revealing the details of the enrollment server. Furthermore, an intercepted JWT could be used for enrollment by an impersonator as long as the JWT remains active. Our proposed mechanism aims to secure the ownership of the OTT by including the JWT as encrypted metadata into a Non-Fungible Token (NFT). The mechanism uses the blockchain Public Key of the intended owner for encrypting the JWT. The blockchain assures the JWT ownership by mapping it to the intended owner's blockchain public address. Our proposed mechanism is applied to an emerging Zero Trust framework (OpenZiti) alongside a permissioned Ethereum blockchain using Hyperledger Besu. The Zero Trust Framework provides enrollment functionality. At the same time, our proposed mechanism based on blockchain and NFT assures the secure distribution of OTTs that is used for the enrollment of identities.

An Improved Integrity-Based Hybrid Multi-User Data Access Control for Cloud Heterogeneous Supply Chain Databases

Cloud-based supply chain applications play a vital role in the multi-user data security framework for heterogeneous data types. The majority of the existing security models work effectively on small to medium-sized datasets with a homogenous data structure. In contrast, Supply Chain Management (SCM) systems in the real world utilize heterogeneous databases. The heterogeneous databases include a massive quantity of raw SCM data and a scanned image of a purchase quotation. In addition, as the size of the database grows, it becomes more challenging to provide data security on multi-user SCM databases. Multi-user datatypes are heterogeneous in structure, and it is complex to apply integrity and confidentiality models due to high computational time and resources. Traditional multi-user integrity algorithms are difficult to process heterogeneous datatypes due to computational time and variation in hash bit size. Conventional attribute-based encryption models such as "Key-policy attribute-based encryption" (KP-ABE), "Ciphertext-Policy Attribute-Based Encryption" (CP-ABE) etc., are used to provide strong data confidentiality on large textual data. Providing security for heterogeneous databases in a multi-user SCM system requires a significant computational runtime for these conventional models. An enhanced integrity-based multi-user access control security model is created for heterogeneous databases in the cloud infrastructure to address the problems with heterogeneous SCM databases. A non-linear integrity model is developed to provide strong integrity verification in the multi-user communication process. A multi-user based access control model is implemented by integrating the multi-user hash values in the encoding and decoding process. Practical results proved that the multi-user non-linear integrity-based multi-access control framework has better runtime and hash bit variation compared to the conventional models on large cloud-based SCM databases.

The citrus flavonoid “Nobiletin” impedes STZ-induced Alzheimer’s disease in a mouse model through regulating autophagy mastered by SIRT1/FoxO3a mechanism

The prominence of autophagy in the modulation of neurodegenerative disorders has sparked interest to investigate its stimulation in Alzheimer's disease (AD). Nobiletin possesses several bioactivities such as anti-inflammation, antioxidation, and neuroprotection. Consequently, the study's aim was to inspect the possible neurotherapeutic impact of Nobiletin in damping AD through autophagy regulation. Mice were randomly assigned into: Group I which received DMSO, Groups II, III, and IV obtained STZ (3 mg/kg) intracerebroventricularly once with Nobiletin (50 mg/kg/day; i.p.) in Group III and Nobiletin with EX-527 (2 mg/kg, i.p.) in Group IV. Interestingly, Nobiletin ameliorated STZ-induced AD through enhancing the motor performance and repressing memory defects. Moreover, Nobiletin de-escalated hippocampal acetylcholinesterase (AChE) activity and enhanced acetylcholine level while halting BACE1 and amyloid-β levels. Meanwhile, Nobiletin stimulated the autophagy process through activating the SIRT1/FoxO3a, LC3B-II, and ATG7 pathway. Additionally, Nobiletin inhibited Akt pathway and controlled the level of NF-κB and TNF-α. Nobiletin amended the oxidative stress through enhancing GSH and cutting down MDA levels. However, EX527, SIRT1 inhibitor, counteracted the neurotherapeutic effects of Nobiletin. Therefore, the present study provides a strong verification for the therapeutic influence of Nobiletin in AD. This outcome may be assigned to autophagy stimulation through SIRT1/FoxO3a, inhibiting AChE activity, reducing neuroinflammation and oxidative stress.Graphical abstract

Central European Agroclimate over the Past 2000 Years

Abstract Central Europe has experienced a sequence of unprecedented summer droughts since 2015, which had considerable effects on the functioning and productivity of natural and agricultural systems. Placing these recent extremes in a long-term context of natural climate variability is, however, constrained by the limited length of observational records. Here, we use tree-ring stable oxygen and carbon isotopes to develop annually resolved reconstructions of growing season temperature and summer moisture variability for central Europe during the past 2000 years. Both records are independently interpolated across the southern Czech Republic and northeastern Austria to produce explicit estimates of the optimum agroclimatic zones, based on modern references of climatic forcing. Historical documentation of agricultural productivity and climate variability since 1090 CE provides strong quantitative verification of our new reconstructions. Our isotope records not only contain clear expressions of the medieval (920–1000 CE) and Renaissance (early sixteenth century) droughts, but also the relative influence of temperature and moisture on hydroclimatic conditions during the first millennium (including previously reported pluvials during the early third, fifth, and seventh centuries of the Common Era). We conclude that Czech agricultural production has experienced significant extremes over the past 2000 years, which includes periods for which there are no modern analogs. Significance Statement As temperatures increase, droughts are becoming a growing concern for European agriculture. Our study allows recent extremes to be contextualized and helps to better the understanding of potential drivers. Stable carbon and oxygen isotopes in oak tree rings were analyzed to reconstruct year-to-year and longer-term changes in both temperature and moisture over central Europe and the past 2000 years. We combine these proxy-based climate reconstructions to model how well crops were growing in the past. The early fifth and the early sixteenth centuries of the Common Era were most likely characterized by extreme conditions beyond what has been experienced in recent decades. Our reconstructions of natural variability might be used as a baseline in projections of future conditions.

Evolution of optical vortices in gradient media and curved spaces.

Light propagation in gradient media and curved spaces induce intriguing phenomena, such as focusing and self-imaging, thus delivering a wide range of applications. However, these systems are limited to excitations without orbital angular momentum, which may produce unforeseen results. Here, we demonstrate the reconstructions (or called imaging to some extent) of optical vortices (OVs) in two-dimensional (2D) gradient media and three-dimensional (3D) curved spaces. We present the evolution of OVs in two types of generalized Maxwell fisheye (GMFE) lenses from the perspective of geometrical and wave optics, and use coherent perfect absorbers (CPAs) to better recover the OVs in the converging position. Furthermore, we also demonstrate such phenomena in two types of 3D compact closed manifolds-sphere and spindle-which are also called geodesic lenses. Surprisingly, the results we obtained in 3D curved spaces can be seen as a strong verification of the Poincaré-Hopf theorem. Our work provides a new, to the best of our knowledge, platform to investigate the evolution of OVs on curved surfaces.

Speaker Identification Analysis Based on Long-Term Acoustic Characteristics with Minimal Performance

The identity of the speakers depends on the phonological properties acquired from the speech. The Mel-Frequency Cepstral Coefficients (MFCC) are better researched for derived the acoustic characteristic. This speaker model is based on a small representation and the characteristics of the acoustic features. These are derived from the speaker model and the cartographic representation by the MFCCs. The MFCC is used for independent monitoring of speaker text. There is a problem with the recognition of speakers by small representation, so proposed the Gaussian Mixture Model (GMM), mean super vector core for training. Unknown vector modules are cleared using rarity and experiments based on the TMIT database. The I-vector algorithm is proposed for the effective improvement of ASR (Automatic Speaker Recognition). The Atom Aligned Sparse Representation (AASR) is used to describe the speaker-based model. The Short Representation Classification (SRC) is used to describe the speaker recognition report. A robust short coding is based on the Maximum Likelihood Estimation (MIE) to clarify the problem in small representation. Strong speaker verification based on a small representation of GMM super vectors. Strong speaker verification based on a small representation of GMM super vectors.

Quantitative magnetic resonance spectroscopy of depression: The value of short-term metabolite changes in predicting treatment response.

Although various prediction models of the antidepressant response have been established, the results have not been effectively applied to heterogeneous depression populations, which has seriously limited their clinical value. This study tried to build a more specific and stable model to predict treatment response in depression based on short-term changes in hippocampal metabolites. Seventy-four major depressive disorder (MDD) patients and 20 healthy controls in the test set were prospectively collected and retrospectively analyzed. Subjects underwent magnetic resonance spectroscopy (MRS) once a week during 6 weeks of treatment. Hippocampal regions of interest (ROIs) were extracted by using a voxel iteration scheme combined with standard brain templates. The short-term differences in hippocampal metabolites between and within groups were screened. Then, the association between hippocampal metabolite changes and clinical response was analyzed, and a prediction model based on logistic regression was constructed. In addition, a validation set (n = 60) was collected from another medical center to validate the predictive abilities. After 2-3 weeks of antidepressant treatment, the differences in indicators (tCho wee0-2, tCho wee0-3 and NAA week0-3) were successfully screened. Then, the predictive abilities of these three indicators were revealed in the logistic regression model, and the optimal prediction effect was found in d(tCho) week0-3-d(NAA) week0-3 (AUC = 0.841, 95%CI = 0.736-0.946). In addition, their predictive abilities were further confirmed with the validation set. The small sample size and the need for multiple follow-ups limited the statistical ability to detect other findings. The predictive model in this study presented accurate prediction and strong verification effects, which may provide early guidance for adjusting the treatment regimens of depression and serve as a checkpoint at which the eventual treatment outcome can be predicted.

Bal Poshan Yojana: A Novel Approach to Facility-Based Severe Acute Malnutrition Management.

IntroductionSevere acute malnutrition (SAM) carries severe implications for a child’s development. The survival of a child suffering from severely acute malnourishment is majorly dependent on the management of SAM, and scaling up the framework for addressing malnutrition is one of its main components. A severely malnourished child has a higher risk of mortality as compared to an ideally nourished child. Bal Poshan Yojana under the aegis of Project Tushti, a public-private partnership (PPP) model, aims at strengthening the framework of treatment for SAM children by working towards building a sustainable eco-system through engaging the government body, private practitioners and implementing bodies.MethodsBal Poshan Yojana is a novel approach implemented for the first time in the Devbhumi Dwarka district of Gujarat for the management of SAM under a PPP model. The private practitioners and centres were empanelled and trained on the treatment of SAM. SAM children with medical complications were screened through anthropometric measurements and appetite tests carried out by Rashtriya Bal Swasthya Karyakram (or RBSK, a program for child health) medical team. High-risk cases were referred to the nearest empanelled Bal Poshan Centre in the area. Children were treated for 14 days at the Bal Poshan Centre as per the protocol of the National Rehabilitation Centre and then discharged if the children fulfilled the criteria. The treatment included a 14-day treatment protocol and three follow-ups in an interval of 15 days each. The data was analyzed with appropriate statistical tests.ResultsSince its inception, a total of 102 severely malnourished children under five years of age have completed their treatment successfully, including three follow-ups. Around 60.79% of SAM children have been found to become normal in their nutritional status. The mean weight gain upon discharge was 0.57 kg and after three follow-ups, it was 1.051 kg.ConclusionBal Poshan Yojana is a one of its kind initiative to tackle the growing burden of malnutrition among under-five age group children. The initiative has a focused approach. Strong referral and verification mechanisms ensure complete transparency and yielding of desired results.

Secure Authentication for Banking Using Face Recognition

With the increasing demand for online banking lack of security in the system has been felt due to a tremendous increase in fraudulent activities. Facial recognition is one of the numerous ways that banks can increase security and accessibility. This paper proposes to inspect the use of facial recognition for login and for banking purposes. The potency of our system is that it provides strong security, username and password verification, face recognition and pin for a successful transaction. Multilevel Security of this system will reduce problems of cybercrime and maintain the safety of the internet banking system. The end result is a strengthened authentication system that will escalate the confidence of customers in the banking sector.

An Efficient Mechanism to Prevent the Phishing Attacks

In the era of modern trends such as cloud computing, social media applications, emails, mobile applications, and URLs that lead to increased risks for defrauding authorized users, and then the attackers try to gain illegal access to accounts of users through a malicious attack. The phishing attack is one of the dangerous attacks caused to access of authorized account illegally way. The finances, business, banking, and other sensitive in states are faces by this type of attacks due to the important information they have. In this paper, we propose a secure verification scheme that can overcome the above-mentioned issues. Additionally, the proposed scheme can resist famous cyberattacks such as impersonate attacks, MITM attacks. Moreover, the proposed scheme has security features like strong verification, forward secrecy, user’s identity anomaly. The security analysis and the experimental results proved the strongest of the proposed scheme compared with other related works. Finally, our proposed scheme balanced between the performance and the security merits.

Dynamic Studies of the HTS Maglev Transit System

With the advantages of high-speed, low-noise and environment-friendly, magnetic levitation (maglev) train has the potential to become a high-speed transportation mode. High-temperature superconducting (HTS) maglev, which can remain stable levitation without any control system, is a competitive type of maglev rail transit. As a kind of high-speed transit, the vibration of the HTS maglev system is inevitable. Hence, studies about its dynamics are necessary and significant. The developments and researches for the HTS maglev vehicle dynamics in Applied Superconductivity Laboratory (ASCLab) are elaborated in detail in this paper. Based on the improved HTS maglev ring test line “Super-Maglev”, a series of research works have been carried out. First, the mathematical model of the pinning force was proposed. Second, motion stability and nonlinear vibration characteristics were studied. Then, the dynamic simulation of vehicle-bridge coupled system of the HTS maglev train was performed. Finally, certain vibration reduction optimizations were added. These dynamics researches give a strong verification for the future application prospect and feasibility of HTS maglev transportation, and the results can provide reference for the engineering application.

Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow

Herein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.

Microscopic characteristics of multiple droplets behaviors at the near-wall region during the quasi-steady state

It is well known that the liquid adhesion by impingement in the direct-injection spark-ignition (DISI) engine hinders the engine combustion efficiency and increases the particular matter (PM) emissions. Although numerous investigations were done on it, it is still blur for the scholars owing to the complicated droplets-wall dynamics. Due to the dense liquid near the wall, it is difficult to observe the impinging droplets in this region, let alone analyzing it on the micro level. Therefore, the “multiple droplets producer” was applied to cut the spray for clear observation in this study. Tslicer was finally determined at 40 μm to make the multiple droplets impacting on the wall at the near-wall region under various injection pressure from 10 to 30 MPa during the quasi-steady state. Four different locations of (15, 15), (17, 15), (20, 15), (22, 15) were selected along the jet development after impingement. Particle image analysis (PIA) technology was applied to capture the micro behaviors. The diameter and velocity of droplets were calculated and analyzed as well as the Weber number. Results show that droplet behaviors near the wall can be recorded at Tslicer = 40 μm during the injection. Droplet size becomes larger with spray propagation at the near-wall region. Observations demonstrate that the coalescence phenomenon of the secondary droplets as well as the splashing crown structures occurrence leads to the larger droplet and lower velocity. Moreover, non-dimensional parameter We was used to further prove the splashing transition occurring from (17, 15) to (20, 15). Additionally, the experimental results can provide strong evidence and verification basis for computational fluid dynamics (CFD) simulations.

A verifiable (t, n) threshold quantum state sharing scheme on IBM quantum cloud platform

In current verifiable quantum state sharing schemes, the dishonest behaviors of certain participants can be verified. However, these schemes have either high resource consumption, low verification efficiency or lack simulation implementations. To compensate for the shortcomings of current schemes, a new verifiable (t, n) threshold quantum state sharing scheme on the IBM quantum cloud platform is proposed. To reduce resource consumption, the proposed scheme prepares only one secret particle and then performs the Lagrange unitary operator on it, and the transformed secret particle is shared in the authorized subset of participants. To improve verification efficiency, the scheme performs the composite rotation unitary operator on the received message particle, such that it not only verifies the validity of the message particle but also reconstructs the original secret particle. The correctness of the proposed scheme is verified by not only mathematic proof but also experimental simulation on the IBM quantum cloud platform. Compared with the two other schemes based on the rotation unitary operator, the proposed scheme provides stronger verification security by using the private shadow key and rotation key. Compared with the two other schemes based on the verification mechanism, the proposed scheme has lower resource consumption and higher verification efficiency.