Line Code Research Articles

Prime Inner Product Encoding for Effective Wildcard-Based Multi-Keyword Fuzzy Search

With the prevalence of cloud computing, a growing number of users are delegating clouds to host their sensitive data. To preserve user privacy, it is suggested that data is encrypted before outsourcing. However, data encryption makes keyword-based searches over ciphertexts extremely difficult. This is even challenging for <i>fuzzy search</i> that allows uncertainties or misspellings of keywords in a query. In this article, we propose a prime inner product encoding (PIPE) scheme, which makes use of the <i>indecomposable</i> property of prime numbers to provide efficient, highly accurate, and flexible multi-keyword fuzzy search. Our main idea is to encode either a query keyword or an index keyword into a vector filled with primes or reciprocals of primes, such that the result of vectors’ inner product is an integer only when two keywords are similar. Specifically, we first construct <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{0}$</tex-math></inline-formula> that is secure in the known ciphertext model. Unlike existing works that have difficulty supporting AND and OR semantics simultaneously, <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{0}$</tex-math></inline-formula> gives users the flexibility to specify different search semantics in their queries. Then, we construct <inline-formula><tex-math notation="LaTeX">$\text{PIPE}_{\text{S}}$</tex-math></inline-formula> that subtly adds random noises to a query vector to resist linear analyses. Both theoretical analyses and experiment results demonstrate the effectiveness of our scheme.

PYLON: A PyTorch Framework for Learning with Constraints

Deep learning excels at learning task information from large amounts of data, but struggles with learning from declarative high-level knowledge that can be more succinctly expressed directly. In this work, we introduce PYLON, a neuro-symbolic training framework that builds on PyTorch to augment procedurally trained models with declaratively specified knowledge. PYLON lets users programmatically specify constraints as Python functions and compiles them into a differentiable loss, thus training predictive models that fit the data whilst satisfying the specified constraints. PYLON includes both exact as well as approximate compilers to efficiently compute the loss, employing fuzzy logic, sampling methods, and circuits, ensuring scalability even to complex models and constraints. Crucially, a guiding principle in designing PYLON is the ease with which any existing deep learning codebase can be extended to learn from constraints in a few lines code: a function that expresses the constraint, and a single line to compile it into a loss. Our demo comprises of models in NLP, computer vision, logical games, and knowledge graphs that can be interactively trained using constraints as supervision.

DEVELOPMENT OF A NEW OILSEED RAPE VARIETY NIFA SARSON-T20 THROUGH PHYSICAL MUTAGENESIS

Induced mutagenesis has been proven as one of non-conventional approaches to create genetic variability. In line with the objective to develop a high seed and oil yielding oilseed rape cultivar; 4000 healthy seeds of an advanced rapeseed (Brassica napus L.) line NR-23 having 8 percent moisture were irradiated @ 1, 1.2 and 1.4 kGy in 2010 using cobalt-60 source. Single plant selections were executed based on yield and yield contributing traits following pedigree method of selection from M2-M4 generations. Homozygous progenies of the pedigree number 011-K-16-3 selected from 1 kGy were bulked during 2013. The mutant with line code 011-K-16-3 was evaluated at station and outstation seed yield trials from 2013-14 to 2017-18. The mutant performed better than checks in Preliminary, Advanced, Zonal and National Uniform Rapeseed Yield Trials (NURYT). It has out yielded Hyola-401 and Faisal Canola by 26% and 23%, respectively in the multi-location adaptation seed yield trials conducted at selected sites in the Khyber Pakhtunkhwa (KP) and Punjab. It has also exceeded the national check Faisal Canola by 5% on location mean basis in the NURYT (2016-17 and 2017-18). It maintained its superiority in oil yields over the check Faisal Canola at station and in NURYTs. It is medium maturing, tall (190-205cm) featured with ramification at middle low, tolerant to alternaria blight, high seed yield (3000-3300 kg ha-1); high oil contents (43-46%) and low erucic acid (&lt; 5%). The Provincial Seed Council approved it with name NIFA Sarson-T20 for growing in the Khyber Pakhtunkhwa (KP).

An Effective Deep Learning-Based Architecture for Prediction of N7-Methylguanosine Sites in Health Systems

N7-methylguanosine (m7G) is one of the most important epigenetic modifications found in rRNA, mRNA, and tRNA, and performs a promising role in gene expression regulation. Owing to its significance, well-equipped traditional laboratory-based techniques have been performed for the identification of N7-methylguanosine (m7G). Consequently, these approaches were found to be time-consuming and cost-ineffective. To move on from these traditional approaches to predict N7-methylguanosine sites with high precision, the concept of artificial intelligence has been adopted. In this study, an intelligent computational model called N7-methylguanosine-Long short-term memory (m7G-LSTM) is introduced for the prediction of N7-methylguanosine sites. One-hot encoding and word2vec feature schemes are used to express the biological sequences while the LSTM and CNN algorithms have been employed for classification. The proposed “m7G-LSTM” model obtained an accuracy value of 95.95%, a specificity value of 95.94%, a sensitivity value of 95.97%, and Matthew’s correlation coefficient (MCC) value of 0.919. The proposed predictive m7G-LSTM model has significantly achieved better outcomes than previous models in terms of all evaluation parameters. The proposed m7G-LSTM computational system aims to support the drug industry and help researchers in the fields of bioinformatics to enhance innovation for the prediction of the behavior of N7-methylguanosine sites.

How do electronic risk assessment tools affect the communication and understanding of diagnostic uncertainty in the primary care consultation? A systematic review and thematic synthesis

ObjectivesTo conduct a systematic review and synthesise qualitative research of electronic risk assessment tools (eRATs) in primary care, examining how they affect the communication and understanding of diagnostic risk and...

Modulation Scheme Analysis for Low-Power Leadless Pacemaker Synchronization Based on Conductive Intracardiac Communication.

Conductive intracardiac communication (CIC) has been demonstrated as a promising concept for the synchronization of multi-chamber leadless cardiac pacemakers (LLPMs). To meet the 2-5 μW power budget of a LLPM, highly specialized CIC-transceivers, which make optimal use of the cardiac communication channel, need to be developed. However, a detailed investigation of the optimal communication parameters for CIC-based LLPM synchronization is missing so far. This work analyzes the intracardiac communication performance of two low-power modulation techniques, namely On-Off-Keying (OOK) and Manchester-encoded baseband transmission (BB-MAN), as a function of the transmitted bit-energy. The bit error rate (BER) of a prototype dual-chamber LLPM was determined both in simulation and in-vitro experiments on porcine hearts. A BER of 1e -4 was achieved with a median bit-energy in the range of 3-16 pJ (interquartile range: 4-15 pJ) for data rates from 75-500 kbps and a receiver input noise density of 7 nV/ √{Hz}. Both modulation schemes showed comparable performance, with BB-MAN having a slight bit-energy advantage (1-2 dB at 150-500 kbps) under equalized transceiver characteristics. This study demonstrates that reliable CIC-based LLPM synchronization is feasible at transmitted power levels 10 nW under realistic channel conditions and receiver noise performance. Therefore, modulation techniques such, as BB-MAN or OOK, are preferable over recently proposed alternatives, such as pulse position modulation or conductive impulse signaling, since they can be realized with fewer hardware resources and smaller bandwidth requirements. Ultimately, a baseband communication approach might be favored over OOK, due to the more efficient cardiac signal transmission and reduced transceiver complexity.

Qubit-efficient encoding scheme for quantum simulations of electronic structure

Simulating electronic structure on a quantum computer requires encoding of fermionic systems onto qubits. Common encoding methods transform a fermionic system of $N$ spin-orbitals into an $N$-qubit system, but many of the fermionic configurations do not respect the required conditions and symmetries of the system so the qubit Hilbert space in this case may have unphysical states and thus can not be fully utilized. We propose a generalized qubit-efficient encoding (QEE) scheme that requires the qubit number to be only logarithmic in the number of configurations that satisfy the required conditions and symmetries. For the case of considering only the particle-conserving and singlet configurations, we reduce the qubit count to an upper bound of $\mathcal O(m\log_2N)$, where $m$ is the number of particles. This QEE scheme is demonstrated on an H$_2$ molecule in the 6-31G basis set and a LiH molecule in the STO-3G basis set using fewer qubits than the common encoding methods. We calculate the ground-state energy surfaces using a variational quantum eigensolver algorithm with a hardware-efficient ansatz circuit. We choose to use a hardware-efficient ansatz since most of the Hilbert space in our scheme is spanned by desired configurations so a heuristic search for an eigenstate is sensible. The simulations are performed on IBM Quantum machines and the Qiskit simulator with a noise model implemented from a IBM Quantum machine. Using the methods of measurement error mitigation and error-free linear extrapolation, we demonstrate that most of the distributions of the extrapolated energies using our QEE scheme agree with the exact results obtained by Hamiltonian diagonalization in the given basis sets within chemical accuracy. Our proposed scheme and results show the feasibility of quantum simulations for larger molecular systems in the noisy intermediate-scale quantum (NISQ) era.

Impact of the ICD-11 on the accuracy of clinical coding in Korea.

Background: ICD-11 was officially released at the World Health Assembly on 25 May 2019. Objective: To find effective ways to increase the accuracy of coding for diagnostic terms in Korea for a stable transition from Korean modification of ICD-10 (7th Revision of Korean Classification of Disease, KCD-7) to ICD-11. Method: A total of 27 skilled Korean health information managers performed KCD-7 and ICD-11 coding simultaneously (line coding [56]; case coding [17]). Accuracy rates and percentage agreements were calculated, and granularity and difficulty of the ICD-11 were rated by participants. Results: The average accuracy rate of line coding was 71.6 % in ICD-11 and 80.2% in KCD-7, which was similar to results in other studies. The mean percentage agreements for ICD-11 and KCD-7 for line coding were 64.2% and 72.1%, respectively; while for case coding it was 15.3% and 26.6%. Selection criteria for the case scenarios may have influenced the low agreements in case coding. Cluster coding, changes of terms in ICD-11 and removal of codes used in ICD-10 contributed to low agreement in ICD-11 (46.6% of participants reported that granularity of ICD-11 was similar to ICD-10, while 36.9% reported that ICD-11 had finer granularity). In terms of difficulty, 15.3% of participants found line coding difficult and 10.9% found case coding difficult. Conclusion: Provision of more detailed reference guidelines and efficient training for coding professionals by the World Health Organization would enable ICD-11 to be an excellent tool for gathering relevant information about diseases in Korea.

Dynamic Deep Genomics Sequence Encoder for Managed File Transfer

The standard communication protocols transfer big data over the internet. However, the challenge lies while sharing the massive amount of genomics data quickly over limited bandwidth networks. Managed File Transfer (MFT) that uses File Transfer Protocol Secure (FTPS) or Secure Shell File Transfer Protocol (SFTP) is a scalable and secure alternative for the purpose. This paper proposes a framework consisting of a Variable-length Genomic Binary Encoding (VGBE) scheme and a hybrid Gated Recurrent Unit-Convolutional Neural Network model (GRU-CNN). The Method exploits Managed File Transfer to reduce the bit size before transmission and increases the bit rate during the transmission of genomics data. Consequently, the method ensures a minimum number of bits per word and less latency. The methodology is tested on various communication protocols. It is validated in terms of performance metrics viz. transfer rate and the size of transferred data using diverse benchmark datasets such as protein sequences and human reference genome taken from the University of California Santa Cruz (UCSC) and National Center for Biotechnology Information (NCBI), respectively. Furthermore, simulation and implementation results show that the model is 98% faster, has 96% less data size, and is more secure than standard communication protocols. Thus, the proposed method is an efficient and secure genomics data transfer method using deep learning techniques.

DL-SMILES#: A Novel Encoding Scheme for Predicting Compound Protein Affinity Using Deep Learning.

Drug repositioning aims to screen drugs and therapeutic goals from approved drugs and abandoned compounds that have been identified as safe. This trend is changing the landscape of drug development and creating a model of drug repositioning for new drug development. In the recent decade, machine learning methods have been applied to predict the binding affinity of compound proteins, while deep learning is recently becoming prominent and achieving significant performances. Among the models, the way of representing the compounds is usually simple, which is the molecular fingerprints, i.e., a single SMILES string. In this study, we improve previous work by proposing a novel representing manner, named SMILES#, to recode the SMILES string. This approach takes into account the properties of compounds and achieves superior performance. After that, we propose a deep learning model that combines recurrent neural networks with a convolutional neural network with an attention mechanism, using unlabeled data and labeled data to jointly encode molecules and predict binding affinity. Experimental results show that SMILES# with compound properties can effectively improve the accuracy of the model and reduce the RMS error on most data sets. We used the method to verify the related and unrelated compounds with the same target, and the experimental results show the effectiveness of the method.

Oriented Localization of Surgical Tools by Location Encoding.

Surgical tool localization is the foundation to a series of advanced surgical functions e.g. image guided surgical navigation. For precise scenarios like surgical tool localization, sophisticated tools and sensitive tissues can be quite close. This requires a higher localization accuracy than general object localization. And it is also meaningful to know the orientation of tools. To achieve these, this paper proposes a Compressive Sensing based Location Encoding scheme, which formulates the task of surgical tool localization in pixel space into a task of vector regression in encoding space. Furthermore with this scheme, the method is able to capture orientation of surgical tools rather than simply outputting horizontal bounding boxes. To prevent gradient vanishing, a novel back-propagation rule for sparse reconstruction is derived. The back-propagation rule is applicable to different implementations of sparse reconstruction and renders the entire network end-to-end trainable. Finally, the proposed approach gives more accurate bounding boxes as well as capturing the orientation of tools, and achieves state-of-the-art performance compared with 9 competitive both oriented and non-oriented localization methods on a mainstream surgical image dataset: m2cai16-tool-locations. A range of experiments support our claim that regression in CSLE space performs better than traditionally detecting bounding boxes in pixel space.

Expanding Column Line Code Adaptive (CLC-A) for Protecting 32-and 64-Bit Data

This article discusses a solution for combining the strengths, in terms of energy consumption and error-correction capacity, of the standard and extended CLC modes.

Research on fronthaul scheme based on DRoF

With the rapid increase of global data traffic, storage and transmission of massive data is a challenging problem. Therefore, data compression is necessary. Based on DRoF, mobile fronthual has the advantages of high data rate, good connectivity and system capacity, and becomes the key part of communication system transmission. In this paper, an optimized vector quantization data compression algorithm based on DRoF system is proposed, which mainly studies OFDM baseband modulation signals. Firstly, the modulation signal is transformed by DCT. Then the optimized fast-global K-means clustering algorithm is used for quantization to overcome the problem that the original algorithm may choose noise points as the cluster center. Finally, Huffman coding is used to reduce the code length of the quantized data. Simulation results show that the proposed algorithm has good CR and EVM performance within the error tolerance range. It has a high degree of compression and signal recovery.

Artificial-Noise-Aided Space–Time Line Code for Enhancing Physical Layer Security of Multiuser MIMO Downlink Transmission

We consider secure downlink multiple-input–multiple-output (MIMO) communications between a transmitter and multiple users when there exists a passive eavesdropper. A downlink main channel between the transmitter and a user is estimated through uplink pilot symbols in a time-division duplex mode. On the other hand, to hinder an eavesdropper from estimating the eavesdropper channel between the transmitter and the eavesdropper, the transmitter does not send any pilot symbols. The main channel is hence available at the transmitter only, while the eavesdropper channel is unavailable at any node. Assuming these channel state information (CSI) conditions, we propose a MIMO transmission method using space–time line code (STLC) with artificial noise (AN) for enhancing the physical layer security. The STLC enables noncoherent detection at the receiver, i.e., a user, without CSI and achieves full spatial diversity. We derive the lower bound of the secrecy sum rate for the proposed STLC transmission method without AN, with AN, and with AN and imperfect CSI. Moreover, numerical results verify that the proposed STLC scheme outperforms the conventional eigen-beamforming and the precoding space-time block code in terms of secrecy sum rate regardless of the signal-to-noise ratio, the number of transmit antennas, and the CSI uncertainty.

Full-Rate Space–Time Line Code for Four Receive Antennas

For multiple-input multiple-output systems with four receive antennas, conventional space–time line codes (STLCs) have supported code rates of 3/4, 4/7, and 1/2. To provide a wider range of transmission rates, we herein propose a rate-1 STLC for four-receive-antenna systems. We present an encoding method to achieve full rate and devise a simple decoding scheme that does not require full channel state information at the receiver. Assuming a large number of transmit antennas, we analyze the bit error rate performance and demonstrate that the proposed STLC can achieve full spatial diversity.

Process-and-Forward two-way relay using multiple space-time line codes

In this paper, we consider a two-way relay (TWR) network that two source nodes (SNs) with multiple antennas exchange data to each other via a relay node (RN) with multiple antennas, when full channel state information (CSI) is available at RN and no CSIs are available at SNs. In case that both SNs transmit space-time block coded multiple data streams to the RN, a new precoding method is proposed for the RN by combing block-wise processing and multiple space-time line codes (STLCs) requiring no CSI at the receiver except the channel norm. An optimal precoding matrix is derived that maximized the minimum achievable rate using a gradient-based iterative algorithm. Moreover, to reduce the design complexity, we derive the closed-form precoding matrix minimizing the total mean square error (MSE), and its theoretical performance is analyzed in terms of the per-stream MSE and detection signal-to-interference-plus-noise ratio. Through numerical simulations, the theoretical MSE is verified by comparing with an experimental MSE, and it is demonstrated that the proposed precoding method is more beneficial than existing retransmission methods for the RN when the SN transfers both single and multiple data stream(s).

Novel Random Key Encoding Schemes for the Differential Evolution of Permutation Problems

Differential evolution is a powerful nature-inspired real-parameter optimization algorithm that has been successfully used to solve a number of hard optimization problems. It has been used to tackle both continuous and discrete optimization problems. The application of a continuous method to discrete problems involves several challenges, including solution representation and search space–solution space mapping. In this work, we study random key encoding, a popular encoding scheme that is used to represent permutations in high-dimensional continuous spaces. We analyze the search space it constitutes, study its structure and properties, and introduce two novel modifications of the encoding. We investigate the proposed encoding strategies in the context of four variants of the differential evolution algorithm and demonstrate their usefulness for two widespread permutation problems: 1) the linear ordering problem and 2) the traveling salesman problem.

Intelligent Reflecting Surface-Aided Space–Time Line Coded Systems

In this study, an intelligent reflecting surface (IRS)-aided space–time line code (STLC) system is examined to efficiently extend its coverage area. The IRS phase-shift values of each element can be designed using a semidefinite relaxation (SDR) method to maximize the received signal-to-noise ratio of the IRS-STLC systems. A unit-modulus constraint relaxation (UCR) method is proposed to reduce the computational complexity of the SDR method. The numerical results verify that the proposed UCR-based IRS can achieve comparable bit-error-rate performance to the SDR-based IRS, albeit with significantly reduced computational complexity. Owing to the proposed IRS, the coverage area can be significantly extended.

Link prediction based on higher-order structure extraction and autoencoder learning in directed networks

Link prediction, which aims to estimate the likelihood of links based on the observed links and/or node attributes, is a common task in complex network analysis and graph data mining. Modeling and exploring high-order structure, also called network motif, are essential for understanding the fundamental structure that control and mediate the behavior of various complex systems. Most existing link prediction methods do not fully consider the high-order structure in the network. In addition, they mainly focus on undirected networks and therefore ignore the directionality of links. To this end, we propose a novel Autoencoder model based on high-order structure to solve the problems of existing link prediction methods. Specifically, we first provide an efficient motif adjacency matrix learning algorithm, which can be used to extract high-order structure in directed networks, and construct multiple motif adjacency matrices. Then, we extend the Graph Autoencoder (GAE) and Variational Autoencoder (VAE) frameworks to solve the link prediction problem in directed networks, which consists of three core modules. (i) First (Node Embedding): a novel encoder scheme is presented to learn the node embeddings from each motif adjacency matrix; (ii) Second (Information Fusion): a robust attention scheme is further introduced to aggregate information of node embeddings learned from multiple motif adjacency matrices to form the final semantically rich node embedding matrix; (iii) Third (Link Prediction): an efficient decoder scheme is finally proposed to reconstruct the target directed network in a multiscale way, by jointly utilizing node embeddings and PageRank centrality, the directions of links can be effectively inferred based on the classic gravitational heuristic formula. Extensive experiments were applied on various types of directed networks to validate the performance of our proposed approach through comparisons with the state-of-the-art link prediction technologies.

Files for 'Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes'

Files for 'Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes'