Recovery Of Information Research Articles

Retracted: Identity-Based Designated-Verifier Proxy Signature Scheme with Information Recovery in Telemedicine System

Retracted: Identity-Based Designated-Verifier Proxy Signature Scheme with Information Recovery in Telemedicine System

Tax Data Processing System

The expense information handling framework at the Shibgah Islam Nusantara Amanah Foundation is as yet done physically, according to the ongoing framework, information filing and duty information recovery have not been completed as expected and on time, where away it is constantly put away haphazardly, causing harm or harm. lost information, so looking for information for data requirements of benefactors consumes most of the day since they need to every year find and match documents. This framework is broke down and planned utilizing spellbinding investigation research strategies and OOAD (Object Oriented Analysis Design) framework improvement methods. The programming language utilized by this framework is PHP with XAMPP web server and PHPMyAdmin information base. This expense information handling data framework that has been made can assist establishment with staffing to find, and recap charge information that is given consequently and can decrease the degree of document harm and blunders in recording charge reports.

Semantic Information Recovery in Wireless Networks.

Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and, thus, enables savings in information rate. In this work, we extend the fundamental approach from Basu et al. for modeling semantics to the complete communications Markov chain. Thus, we model semantics by means of hidden random variables and define the semantic communication task as the data-reduced and reliable transmission of messages over a communication channel such that semantics is best preserved. We consider this task as an end-to-end Information Bottleneck problem, enabling compression while preserving relevant information. As a solution approach, we propose the ML-based semantic communication system SINFONY and use it for a distributed multipoint scenario; SINFONY communicates the meaning behind multiple messages that are observed at different senders to a single receiver for semantic recovery. We analyze SINFONY by processing images as message examples. Numerical results reveal a tremendous rate-normalized SNR shift up to 20 dB compared to classically designed communication systems.

Monocular polarized three-dimensional absolute depth reconstruction technology for multi-target scenes.

The traditional polarization three-dimensional (3D) imaging technology has limited applications in the field of vision because it can only obtain the relative depth information of the target. Based on the principle of polarization stereo vision, this study combines camera calibration with a monocular ranging model to achieve high-precision recovery of the target's absolute depth information in multi-target scenes. Meanwhile, an adaptive camera intrinsic matrix prediction method is proposed to overcome changes in the camera intrinsic matrix caused by focusing on fuzzy targets outside the depth of field in multi-target scenes, thereby realizing monocular polarized 3D absolute depth reconstruction under dynamic focusing of targets at different depths. Experimental results indicate that the recovery error of monocular polarized 3D absolute depth information for the clear target is less than 10%, and the detail error is only 0.19mm. Also, the precision of absolute depth reconstruction remains above 90% after dynamic focusing on the blurred target. The proposed monocular polarized 3D absolute depth reconstruction technology for multi-target scenes can broaden application scenarios of the polarization 3D imaging technology in the field of vision.

A deep learning-based adaptive receiver for full-duplex systems

To accommodate the growth of data traffic of 6G and beyond networks, achieving a significant improvement in spectrum efficiency is inevitable. Full-duplex systems are very promising since they have the potential of increasing the spectral efficiency compared to half-duplex systems. The main challenge facing the deployment of full-duplex systems is self-interference. Conventional receivers include series of processing blocks that recover the desired signal after removing the effect of self-interference. In the presence of noise and fading channels, the received signal becomes distorted and the recovery is thus challenging. Deep learning algorithms have shown great success in efficient parameter estimation, as well as adaptive decision making. In this study, a deep learning-based full-duplex receiver, namely FDDR, is proposed to replace the receiver’s entire information recovery process of the full-duplex system rather than optimizing each processing module of the receiver separately. Simulation results show that FDDR approaches the same Bit Error Rate performance as a conventional receiver that uses a Kalman Filter as a channel estimator with 80% less complexity. The Bit Error Rate performance of FDDR is also tested in the case of Doppler frequency mismatch between the training and testing phases, multiple self-interference reflections and cyclic prefix free scenarios and shows superior performance.

Collaborative networks of transformers and convolutional neural networks are powerful and versatile learners for accurate 3D medical image segmentation

Integrating transformers and convolutional neural networks represents a crucial and cutting-edge approach for tackling medical image segmentation problems. Nonetheless, the existing hybrid methods fail to fully leverage the strengths of both operators. During the Patch Embedding, the patch projection method ignores the two-dimensional structure and local spatial information within each patch, while the fixed patch size cannot capture features with rich representation effectively. Moreover, the calculation of self-attention results in attention diffusion, hindering the provision of precise details to the decoder while maintaining feature consistency. Lastly, none of the existing methods establish an efficient multi-scale modeling concept. To address these issues, we design the Collaborative Networks of Transformers and Convolutional neural networks (TC-CoNet), which is generally used for accurate 3D medical image segmentation. First, we elaborately design precise patch embedding to generate 3D features with accurate spatial position information, laying a solid foundation for subsequent learning. The encoder–decoder backbone network is then constructed by TC-CoNet in an interlaced combination to properly incorporate long-range dependencies and hierarchical object concepts at various scales. Furthermore, we employ the constricted attention bridge to constrict attention to local features, allowing us to accurately guide the recovery of detailed information while maintaining feature consistency. Finally, atrous spatial pyramid pooling is applied to high-level feature map to establish the concept of multi-scale objects. On five challenging datasets, including Synapse, ACDC, brain tumor segmentation, cardiac left atrium segmentation, and lung tumor segmentation, the extensive experiments demonstrate that TC-CoNet outperforms state-of-the-art approaches in terms of superiority, migration, and strong generalization. These illustrate in full the efficacy of the proposed transformers and convolutional neural networks combination for medical image segmentation. Our code is freely available at: https://github.com/YongChen-Exact/TC-CoNet.

A framework for information recovery of cementitious materials under long-term use

The lifetime of concrete and concrete structure is significantly longer compared to the ordinary industrial materials and products. During the lifecycle stages, various stakeholders take place and makes interventions to the structures. It is important to store all information necessary for such interventions. This information includes design documents and construction records at newly construction as well as the remedial activity records at the use stage. Although the importance of such information storage is emphasized in many studies and recommendations, the loss of such information is frequently observed for the concrete structures after long term use. Even the most basic information related to concrete material design, such as water-to-cement ratio, is frequently lost after long-term use of the concrete structures.Therefore, the framework and method to recover materials information is necessary to appropriately operate and maintain the structure for the long-term period. This paper presents a novel framework for realizing such information recovery of cementitious material using the multi-dimensional data-driven approach. First, the essence and advantage of obtaining the multi-dimensional data in comparison with single value approach was presented. First, different dependency of AC impedance |Z| and theta at different measurement frequencies was compared and use of this tendency for information recovery was discussed. Second, the accuracy of water-to-cement ratio estimation using the proposed system was compared between different available information. The proposed system exhibited some consistent estimation even when all information is not available, and exhibited good estimation accuracy when age is assumed known.

Spacetime Structure, Asymptotic Radiation, and Information Recovery for a Quantum Hybrid State.

A hybrid quantum state is a combination of the Hartle-Hawking state for the physical particles and the Boulware state for the nonphysical ones (such as ghosts), as was introduced in our earlier work [Y. Potaux et al., Phys. Rev. D 105, 025015 (2022).PRVDAQ2470-001010.1103/PhysRevD.105.025015]. We present a two-dimensional example, based on the Russo-Susskind-Thorlacius model, when the corresponding backreacted spacetime is a causal diamond, geodesically complete and free of the curvature singularities. In the static case it shows no presence of the horizon while it has a wormhole structure mimicking the black hole. In the dynamical case, perturbed by a pulse of classical matter, there appears an apparent horizon while the spacetime remains to be a regular causal diamond. We compute the asymptotic radiation both in the static and dynamical case. We define entropy of the asymptotic radiation and demonstrate that as a function of the retarded time it shows the behavior typical for the Page curve. We suggest interpretation of our findings.

Compactified extra dimension and entanglement island as clues to quantum gravity

We show that the compactified extra dimension and the emergence of the island can provide clues about quantum gravity because their combination can solve the deepest puzzles of black hole physics. Suppose that the time dimension and the extra dimension compactified on a circle are symmetric under double Wick rotation, the curvature singularity would be removed due to the end of spacetime as a smooth bubble hidden behind the event horizon. The smooth bubble geometries can also be interpreted as microstates leading to the Bekenstein–Hawking entropy because the smooth bubble geometries live in the same region of mass and charge as the black string. In addition, by applying the quantum extremal surface prescription, we show the emergence of the island at late times of the black string evaporation where it is located slightly outside the event horizon. Due to the dominant contribution of the island configuration, the entanglement entropy of the radiation grows no longer linearly in time but it reaches a finite value that is twice the Bekenstein–Hawking entropy at the leading order. This transition shows the information preservation during the black string evaporation. Furthermore, we calculate the Page time which determines the moment of the transition between the linearly growing and constant behaviors of the entanglement entropy as well as the scrambling time corresponding to the information recovery time of the signal falling into the black string.

Assessment of the readiness of a computer system for timely servicing of requests when combined with information recovery of memory after failures

Assessment of the readiness of a computer system for timely servicing of requests when combined with information recovery of memory after failures

Frequency-domain Hong-Ou-Mandel interference in cold atoms with induced atomic coherence

Hong-Ou-Mandel (HOM) interference with two independent photons of different wavelengths is a primary tool for controlled transfer of quantum information across a hybrid quantum network such as the quantum internet, in which the diverse material nodes operate at different resonant frequencies. The key challenge of realizing two-photon interference in the frequency domain is the need for linear frequency conversion between spectrally distinct photons, which, in contrast to frequency mixing in nonlinear optical processes, is free from multiphoton noise arising in strong laser fields. Here, we present a linear HOM scheme based on a lossless three-wave mixing between two photons of different frequencies in the laser-controlled tripod-type atoms, where the parametric interaction between photons is triggered by the induced coherence between two metastable states of the atoms [S. Petrosyan et al., Phys. Rev. A 105, 052606 (2022)]. We give the general derivation of the HOM effect and examine it for two single-pulse photons and in the important case of time-bin encoded single photons, which are well protected from decoherence in optical fibers. We show that two-photon coincidence measurements display the HOM-type ``dip and peak'' fringes according to the number of time bins in the incident photon state, allowing full recovery of the original quantum information. An essential feature of our scheme is the neutrality of the HOM effect implementation to the polarization indistinguishability of photons, which makes our model flexible for use in hybrid quantum networks.

Quantification of gallium cryo-FIB milling damage in biological lamellae

Cryogenic electron microscopy (cryo-EM) can reveal the molecular details of biological processes in their native, cellular environment at atomic resolution. However, few cells are sufficiently thin to permit imaging with cryo-EM. Thinning of frozen cells to <500nm lamellae by focused-ion-beam (FIB) milling has enabled visualization of cellular structures with cryo-EM. FIB milling represents a significant advance over prior approaches because of its ease of use, scalability, and lack of large-scale sample distortions. However, the amount of damage it causes to a thinned cell section has not yet been determined. We recently described an approach for detecting and identifying single molecules in cryo-EM images of cells using 2D template matching (2DTM). 2DTM is sensitive to small differences between a molecular model (template) and the detected structure (target). Here, we use 2DTM to demonstrate that under the standard conditions used for machining lamellae of biological samples, FIB milling introduces a layer of variable damage that extends to a depth of 60nm from each lamella surface. This layer of damage limits the recovery of information for insitu structural biology. We find that the mechanism of FIB milling damage is distinct from radiation damage during cryo-EM imaging. By accounting for both electron scattering and FIB milling damage, we estimate that FIB milling damage with current protocols will negate the potential improvements from lamella thinning beyond 90nm.

Safety helmet detection method based on semantic guidance and feature selection fusion

Safety helmet detection is a hot topic of research in the field of industrial safety for object detection technology. Existing object detection methods still face great challenges for the detection of small-scale safety helmet object. In this paper, we propose a safety helmet detection method based on the fusion of semantic guidance and feature selection. The method is able to consider the balance between detection performance and efficiency. First, a multi-scale non-local module is proposed to establish internal correlations between different scales of deep image features as well as to aggregate semantic context information to guide the information recovery of decoder network features. Then the feature selection fusion structure is proposed to adaptively select deep features and underlying key features for fusion to make up for the missing semantic and spatial detail information of the decoding network and improve the spatial location expression capability of the decoding network. Experimental analysis shows that the method in this paper has good detection performance on the expanded safety helmet wearing dataset with 5.12% improvement in mAP compared to the baseline method CenterNet, and 6.11% improvement in AP for the safety helmet object.

Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM

Accurate and efficient short-term forecasting of multiple loads is of great significance to the operation control and scheduling of integrated energy distribution systems. In order to improve the effect of load forecasting, a mogrifier-quantum weighted memory enhancement long short-term memory (Mogrifier-QWMELSTM) neural network forecasting model is proposed. Compared with the conventional LSTM neural network model, the model proposed in this paper has three improvements in model structure and model composition. First, the mogrifier is added to make the data fully interact with each other. This addition can help enhance the correlation between the front and rear data and improve generalization, which is the main disadvantage of LSTM neural network. Second, the memory enhancement mechanism is added on the forget gate to realize the extraction and recovery of forgotten information. The addition can help improve the gradient transmission ability in the learning process of the neural network, make the neural network remain sensitive to distant data information, and enhance the memory ability. Third, the model is composed of quantum weighted neurons. Compared with conventional neurons, quantum weighted neurons have significant advantages in nonlinear data processing and parallel computing, which help to improve the accuracy of load forecasting. The simulation results show that the weighted mean accuracy of the proposed model can reach more than 97.5% in summer and winter. Moreover, the proposed model has good forecasting effect on seven typical days in winter, which shows that the model has good stability.

An Efficient Privacy - Aware Authentication Framework for Mobile Cloud Computing

The approach of distributed computing, information proprietors are inspired to re-appropriate their intricate information the board frameworks from neighbourhood locales to business public cloud for extraordinary adaptability and financial investment funds. Yet, for safeguarding information security, delicate information must be scrambled prior to re-appropriating, which obsoletes customary information use in view of plaintext catchphrase search. In this way, empowering an encoded cloud information search administration is of fundamental significance. Taking into account the enormous number of information clients and reports in cloud, it is pivotal for the pursuit administration to permit multi-watchword question and give result similitude positioning to meet the compelling information recovery need. Related works on accessible encryption centre around single catchphrase search or Boolean watchword search, and seldom separate the indexed lists. In this paper, interestingly, we characterize and tackle the difficult issue of protection safeguarding multi-watchword positioned metaphysics catchphrase planning and search over encoded cloud information (EARM), and lay out a bunch of severe protection prerequisites for such a solid cloud information use framework to turn into a reality. Content-based distribute/buy in gives an inexactly coupled and expressive type of correspondence for enormous scope circulated frameworks. Secrecy is difficult for distribute/buy in middleware conveyed over various regulatory areas. This component that lessens the expense of encoded coordinating, as a profiteering administrator utilizing Blossom channels and straightforward randomization procedures. Propose regulation obscurity procedures and give a thorough security examination of the data spilled by Blossom channels for this situation. Among different multi-watchword semantics, we pick the effective guideline of "Improved Affiliation Rule Mining coordinate coordinating", i.e., whatever number matches as could reasonably be expected, to catch the closeness between search inquiry and information records, and further use "inward item likeness" to formalize such standard for comparability estimation quantitatively. We initially propose an essential EARM conspire utilizing secure inward item calculation, and afterward fundamentally further develop it to meet different protection prerequisites in two degrees of danger models.

Exploiting the microarchitectural leakage of prefetching activities for side-channel attacks

Microarchitectural optimizations are designed to maximize CPU usage from all aspects of instruction execution. While they effectively shorten the overall timing of execution by executing instructions or load data speculatively, observable traces, which can be used to infer sensitive information about programs on the fly, are left behind. The Instruction Pointer (IP) based stride prefetcher is implemented by Intel since Sandy Bridge, which uses sequential load history to determine whether to fetch additional lines in advance. Shin et al. (2018) discovered that lines near the lookup table are prefetched by the IP-based stride prefetcher, and they conducted side-channel attack on ECDH in face of constant-time algorithm. Their exploitation of prefetching leakage leverage secret bits which directly relate to prefetching themselves, hence such attacks cannot be applied to algorithms without bit-dependent control/data flow, e.g., AES. @We believe the potential of prefetching leakage is not fully explored as many details of prefetcher implementation are unrevealed. Understanding how the prefetching works will help us to construct more powerful attacks. Motivated by this, we reverse-engineer three prefetching rules of IP-based stride prefetcher. A novel side-channel attack to recover the secret key of AES-128 is proposed to exemplify the exploitation, in which 3 or 4 consecutive bytes of the secret key are inferred according to observed prefetching activities combined with our concluded rules. Other unknown bytes can be complemented one at a time iteratively with our complement method. Besides, we verify that little interference is introduced by delaying the probing phase. In that case, it is rather reasonable and practical to schedule probing after the entire encryption is completed, instead of the first round where attackers need to interrupt the execution of encryption. Based on the inference, our side-channel attack successfully recovered all bytes of the secret key of AES-128. The microarchitectural leakage of prefetching activities is proved to contribute in terms of direct sensitive information recovery when detailed prefetching mechanisms are offered.

Issue Information

EpilepsiaVolume 64, Issue 4 p. i-viii ISSUE INFORMATIONFree Access Issue Information First published: 12 April 2023 https://doi.org/10.1111/epi.16940AboutRelatedInformationPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessClose modalShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Volume64, Issue4April 2023Pages i-viii RelatedInformation RecommendedPeer Review at the Health Information and Libraries JournalMaria J. Grant, Health Information & Libraries JournalTHE RECOVERY OF INTER‐BLOCK INFORMATION IN VARIETY TRIALS ARRANGED IN THREE‐DIMENSIONAL LATTICESF. YATES, Annals of EugenicsCORRELATION BETWEEN PROGNOSIS BASED ON CONDITION OF THE TUBERCULOUS PATIENT AT ENTRY TO A SANATORIUM AND THE ISSUEB. PEROTT M.D., ETHEL M. ELDERTON, Annals of Eugenics

Black hole information recovery from gravitational waves

We study the classical and quantum black hole information in gravitational waves from a black hole’s history. We review the necessary concepts regarding quantum information in many-body systems to motivate information retrieval and content in gravitational waves. We then show the first step in an optimal information retrieval strategy is to search for information in gravitational waves, compared to searching for correlations in Hawking radiation. We argue a large portion of the information of the initial collapsing state may be in the gravitational waves. Using the Zerilli equation for particles falling radially into Schwarzschild black holes, we then describe a method to retrieve full classical information about infalling sources, including masses, infall times and angles.

IR$$^2$$Net: information restriction and information recovery for accurate binary neural networks

IR$$^2$$Net: information restriction and information recovery for accurate binary neural networks

A Signal processing method of OFDM communication receiver based on CNN

Orthogonal frequency division multiplexing (OFDM) the signal processing is a key issue in wireless communication research. The multipath effect and Doppler shift of wireless communication channels can lead to distortion of the transmitted signal, which poses a considerable challenge to the information recovery of communication receivers. This paper presents the signal processing method of OFDM communication based on convolutional neural network (CNN). The method replaces all signal processing modules of the OFDM communication receiver with CNN, and the information is recovered by the CNN. In order to adapt to the processing of communication signals, we designed a one-dimensional convolutional neural network (1D-CONV-CNN) model as the neural network structures by this method. Simulation results indicate that the signal processing method effectively reduces the bit error rate (BER) and improves its performance compared with the conventional reception method under different channel conditions.