Code Mapping Research Articles

Socioemotional regulation strategies in a project-based learning environment

Students working in small collaborative groups may experience conflicts due to emotional issues at the individual or group level. Students need to regulate these emotions to avoid or reduce negative socioemotional interactions that can interfere with group performance. In this article, we studied the socioemotional regulation strategies used by graduate pharmacy students as they worked together in a small-group project-based learning environment. For this, we video recorded groups of students working on a class project in an authentic learning context. We conducted a qualitative extreme case study of three groups who, over six weeks, collectively rated their emotions as low, medium, and high to determine how the groups regulated their emotions, as well as the similarities and differences between the groups. We conducted three analyses: code mapping, descriptive, and thematic. We found that the socioemotional regulation strategies fell into one of the following five themes: behavioral, interpersonal, cognitive, motivational, and a combination of motivational and cognitive. We found that the most commonly used strategies were interpersonal and that the strategies were used at varying interpersonal levels (i.e., self, peer, and group). We also found that some groups used more appropriate strategies and that the use of strategies may have been connected to individual differences and pre-existing relationships between group members. Understanding which strategies are useful in specific collaborative contexts can help educators guide groups of students to effectively regulate their emotions.

An RNA tagging approach for system-wide RNA-binding proteome profiling and dynamics investigation upon transcription inhibition.

RNA-protein interactions play key roles in epigenetic, transcriptional and posttranscriptional regulation. To reveal the regulatory mechanisms of these interactions, global investigation of RNA-binding proteins (RBPs) and monitor their changes under various physiological conditions are needed. Herein, we developed a psoralen probe (PP)-based method for RNA tagging and ribonucleic-protein complex (RNP) enrichment. Isolation of both coding and noncoding RNAs and mapping of 2986 RBPs including 782 unknown candidate RBPs from HeLa cells was achieved by PP enrichment, RNA-sequencing and mass spectrometry analysis. The dynamics study of RNPs by PP enrichment after the inhibition of RNA synthesis provides the first large-scale distribution profile of RBPs bound to RNAs with different decay rates. Furthermore, the remarkably greater decreases in the abundance of the RBPs obtained by PP-enrichment than by global proteome profiling suggest that PP enrichment after transcription inhibition offers a valuable way for large-scale evaluation of the candidate RBPs.

Performance of probabilistic amplitude shaping with BICM‐ID

It is numerically shown that bit error rate (BER) performance of probabilistic amplitude shaping (PAS) applying a bit-to-symbol mapping of non-Gray natural binary code assigned to 64-quadrature amplitude modulation is significantly improved by bit-interleaved coded modulation with iterative detection (BICM-ID). The obtained BER performance outperforms the PAS using a standard Gray code mapping. Furthermore, it has been numerically verified that the non-Gray natural binary code mapping is suitable for BICM-ID by analysing the extrinsic information transfer chart. In addition, two degradation factors, the shaping and the coding gaps are estimated, from the Shannon limit by analysing mutual information characteristics. The obtained results quantitatively show that BICM-ID can effectively suppress the coding gap of the PAS applying non-Gray mapping in BER performance.

Unary Coding and Variation-Aware Optimal Mapping Scheme for Reliable ReRAM-Based Neuromorphic Computing

Neural network (NN) computing contains a large number of multiply-and-accumulate (MAC) operations. The performance of NN accelerator is limited with the traditional von Neumann architecture due to the tremendous off-chip memory accesses. Resistive random-access memory (ReRAM)-based crossbars can naturally perform matrix–vector multiplication (MVM) operations and are well suitable for NN accelerators. In the existing ReRAM-based NN accelerators, the synaptic weights represented by the conductances of ReRAMs are mainly based on the binary coding. However, the imperfect fabrication process combined with stochastic filament-based switching leads to resistance variations of ReRAMs, which can significantly alter the weights in binary synapses and degrade the NN accuracy. Moreover, the NN accuracy further deteriorates with multilevel cells (MLCs) used for reducing hardware overhead. In this article, a novel unary coding of synaptic weights is proposed to overcome the resistance variations of MLCs and achieve reliable ReRAM-based neuromorphic computing. A variation-aware optimal mapping scheme is also proposed in compliance with the unary coding to guarantee high accuracy by leveraging a unique feature of unary coding—the existence of multiple ways to represent the same value. The optimal mapping obtains very small errors for weights with resistance variations of MLCs. Our simulation results show that under resistance variations, the proposed method achieves less than 0.08% and 3.43% accuracy loss on CIFAR10 and ImageNet, respectively, compared to the ideal accuracy. With each synaptic weight represented by four 2-b MLCs, the proposed method improves the accuracy over the traditional binary coding scheme by 83.39% and 87.6% for CIFAR10 and ImageNet, respectively.

Characteristics of the neural coding of causality.

While causality processing is an essential cognitive capacity of the neural system, a systematic understanding of the neural coding of causality is still elusive. We propose a physically fundamental analysis of this issue and demonstrate that the neural dynamics encodes the original causality between external events near homomorphically. The causality coding is memory robust for the amount of historical information and features high precision but low recall. This coding process creates a sparser representation for the external causality. Finally, we propose a statistic characterization for the neural coding mapping from the original causality to the coded causality in neural dynamics.

Structural modification of 4, 5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives as BRD4 inhibitors using 2D/3D-QSAR and molecular docking analysis.

Cancer treatment continues to be one of the most serious public health issues in the world. The overexpression of BRD4 protein has led to a series of malignant tumors, hence the development of small molecule BRD4 protease inhibitors has always been a hot spot in the field of medical research. In this study, a series of 4,5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives were used to establish 3D/2D-QSAR models and to discuss the relationship between inhibitor structure and activity. Four ideal models were established, including the comparative molecular field analysis (CoMFA: [Formula: see text] = 0.574, [Formula: see text] = 0.947) model, comparative molecular similarity index analysis (CoMSIA: [Formula: see text]= 0.622, [Formula: see text] = 0.916) model, topomer CoMFA ([Formula: see text] = 0.691, [Formula: see text]= 0.912) model and hologram quantitative structure-activity relationship (HQSAR: [Formula: see text]= 0.759, [Formula: see text] = 0.963) model. They show quite good external predictive power for the test set, with [Formula: see text] values of 0.602, 0.624, 0.671 and 0.750, respectively. In addition, the contour and color code map given by the 2D/3D-QSAR model with the results of molecular docking analyzed to chalk up modification methods for improving inhibitory activity, which was verified by designing novel compounds. The analysis results are helpful to promote the modification of the inhibitor framework and to provide a reference for the construction of new and promising BRD4 inhibitor compounds.

Method for constructing multi-dimensional feature map of malicious code

Malicious code is characterized by a large number of types, rapid increase in number, continuous update of transmission routes, and continuous enhancement of back analysis and back detection methods. Therefore, how to effectively detect and analyze malicious code has been a problem of great concern. This paper studies the features of binary file and disassembly file of malicious code, introduces the concept of information gain, and proposes a method to construct the multi-dimensional characteristic graph of malicious code. Finally, the convolutional neural network is used to classify the multi-dimensional feature graph of malicious code, which provides a new idea for the feature extraction of malicious code.

Secure Colour Image Encryption Based on Dynamic DNA Coding and Different Chaotic Maps

Secure Colour Image Encryption Based on Dynamic DNA Coding and Different Chaotic Maps

Soil data definition for hydrologic response unit analysis in SWAT model of Langkawi Island, Malaysia

Soil and water assessment tool (SWAT) have been assessed to examine environmental conditions and watershed scale, particularly for water quality and natural resource management. In this study, SWAT model has been applied to the main river basins in Langkawi Island. Soil data, one of the spatially distributed data needed for SWAT model interface. Currently, no soil interpretation record (s5id) data code available in readable format for user soil SWAT database for Langkawi Island. The purpose of soil data definition is to create a soil input data setup for hydrologic response unit (HRU) analysis in SWAT model which includes soil map, soil type, soil texture, and soil s5id code. Study by Leman et al. (2007) showed that geological formation of soil in Langkawi consists of alluvium, granite, Machincang, Setul, Chuping and Singa formation. The dominant soil group was Acrisols (soil unit name: Orthic Acrisols, Ao) and the dominant soil texture classification was sandy clay loam. MY4284 and MY4464 defined as the code for soil interpretation record number (s5id). Percentage of coverage for MY4464 was (62.21%; 10,865.87 ha [26,850.15 ac]) and the percentage of coverage for MY4284 was (37.79%; 6,599.8 ha [16,308.46 ac]) within the selected watershed boundary of Langkawi Island. This data setup has been successfully tested and fully functional for usersoil database of Langkawi SWAT model analysis.

Asymmetric Supervised Deep Discrete Hashing Based Image Retrieval

Hashing has been widely used for image retrieval tasks. In view of the limitations of existing deep supervised hashing methods, a new asymmetric supervised deep discrete hashing method is proposed to maintain the semantic structure between different categories and generate binary codes. Firstly, a deep network is used to extract image features and reveal the similarity between each pair of images according to their semantic labels. To enhance the similarity between binary codes and ensure the retention of multi-label semantics, this paper design an asymmetric hashing method that utilizes a multi-label binary code mapping to make the hash codes have multi-label semantic information. In addition, the bit balance of the binary code is introduced to balance each bit, which encourages the number of -1 and +1 to be approximately similar among all training samples. Experimental results on two benchmark datasets show that the proposed method is superior to other methods in image retrieval.

Neural Network Instruction Set Extension and Code Mapping Mechanism

PDF HTML XML Export Cite reminder Neural Network Instruction Set Extension and Code Mapping Mechanism DOI: 10.21655/ijsi.1673-7288.00251 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:In recent years, Convolutional Neural Network (CNN) has received widespread attention in the field of machine learning due to its high-accuracy performance in character recognition and image classification. Nevertheless, the compute-intensive and memory-intensive characteristics of CNN have posed huge challenges to the general-purpose processor, which needs to support various workloads. Therefore, a large number of CNN-specific hardware accelerators have emerged to improve efficiency. Though significantly efficient, previous accelerators are not flexible enough. In this study, classical CNN models are analyzed and a domain-specific instruction set of 10 matrix instructions, called RV-CNN, is design based on the promising RISC-V architecture. By abstracting CNN computation into instructions, the proposed design can provide sufficient flexibility for CNN and possesses a higher code density than the general ISA. On this basis, a code-to-instruction mapping mechanism is proposed. By using the RV-CNN to build different CNN models on the Xilinx ZC702, this paper found that compared to x86 processors, RV-CNN has an average of 141 times energy efficiency and 8.91 times the code density; compared to GPU, it has an average of 1.25 times energy efficiency and 1.95 times the code density. In addition, compared to previous CNN accelerators, the design supports typical CNN models while at high energy efficiency. Reference Related Cited by

Research on ABAC Access Control Based on Big Data Platform

In the environment of big data, the traditional access control lacks effective and flexible access mechanism. Based on attribute access control, this paper proposes a HBMC-ABAC big data access control framework. It solves the problems of difficult authority change, complex management, over-authorization and lack of authorization in big data environment. At the same time, binary mapping codes are proposed to solve the problem of low efficiency of policy retrieval in traditional ABAC. Through experimental analysis, the results show that our proposed HBMC-ABAC model can meet the current large and complex environment of big data.

Mapping and Visualization of Source Code: A Survey

Mapping and Visualization of Source Code: A Survey

Development of a Global Health Learning Progression (GHELP) Model.

There has been a steady increase in global health experiential opportunities offered within healthcare professional training programs and with this, a need to describe the process for learning. This article describes a model to contextualize global health learning for students who complete international advanced pharmacy practice experiences (APPEs). Students from University of North Carolina at Chapel Hill, Purdue University, and the University of Colorado completed a post-APPE survey which included open-ended questions about knowledge, skills, and attitudes one week after completing an international APPE. Students were also invited to participate in a focus group. All 81 students who participated in an international APPE completed the open-ended survey questions and 22 students participated in a focus group discussion. Qualitative data from both the survey and focus groups were coded in a two-cycle open coding process. Code mapping and analytic memo writing were analyzed to derive to a conceptual learning model. The Global Health Experience Learning Progression (GHELP) model was derived to describe the process of student learning while on global health experiences. This progression model has three constructs and incorporates learning from external and internal influences. The model describes how students can advance from cultural awareness to cultural sensitivity and describes how student pharmacists who participate in international experiential education develop global health knowledge, skills, and attitudes.

Fault System-Based Probabilistic Seismic Hazard Assessment of a Moderate Seismicity Region: The Eastern Betics Shear Zone (SE Spain)

Including faults as seismogenic sources in probabilistic seismic hazard assessments (PSHA) has turned into a common practice as knowledge of active faults is improving. Moreover, the occurrence of earthquakes in multi-fault ruptures has evidenced the need to understand faults as interacting systems rather than independent sources. We present a PSHA for the Southeastern Spain obtained by including the faults of a moderate seismicity region, the Eastern Betics Shear Zone (EBSZ) in SE Spain, as the main seismogenic sources in two separate source models, one considering background seismicity. In contrast with previous studies in Spain, earthquake occurrence of the EBSZ system is modeled considering different hypotheses of multi-fault ruptures at the whole fault system scale and weighted in a logic tree. We compare the hazard levels with those from an area source PSHA and a previous fault-based approach. The results show a clear control of the EBSZ faults in the seismic hazard for all return periods, increasing drastically the hazard levels in the regions close to the fault traces and influencing up to 20 km farther with respect to the area source PSHA. The seismic hazard is dependent on the fault slip rates as peak ground accelerations and territorial extension of the fault influence appear higher around the Alhama de Murcia and Carboneras faults, while lower slip rate faults (Palomares Fault) show minor contribution to the hazard. For the return period of 475 years and near-fault locations, our models are more consistent with the ground motion values reached in the 2011 Mw 5.2 Lorca event than the building code or national seismic hazard map, which suggest that our fault system-based model performs more accurate estimations for this return period. Fault data, mainly slip rates, and its uncertainties have a clear impact on the seismic hazard and, for some faults, the lack of detailed paleoseismic studies can compromise the reliability of the hazard estimations. This, together with epistemic uncertainties concerning the background seismicity, are key discussion points in the present study, having an impact on further research and aiming to serve as a case example for other low-to-moderate seismicity regions worldwide.

An automatic mapping technique for OpenACC kernel code based on deeply fused and heterogeneous many-core architecture

Now the OpenACC has become a popular programming interface for many-core application programming. Internationally, a lot of research have been done on OpenACC for CPU + GPU heterogeneous many-core architecture. Among them, the PGI OpenACC compiler developed by NVIDIA is the most advanced one. But there are few research on OpenACC related to the Home Grown Heterogeneous Many-Core (HGHM) Architecture that is different from GPU. This paper proposes an automatic mapping technique for OpenACC kernel code based on the OpenACC compiler to a heterogeneous and deeply fused many-core architecture. Our approach uses the static analysis and feedback dynamic analysis of the compiler to perform the automatic mapping of the program parallel kernel code to many-core devices, and it greatly improves the transformation quality of the compiler. Experimental results show that this technique can greatly improve the efficiency of using OpenACC to port applications to heterogeneous and fused many-core system without impacting program acceleration performance.

COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology.

BackgroundCreating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at either the domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was an extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform.ObjectiveThis study aimed to develop an application ontology for COVID-19 that can be deployed across the various use-case domains of the RCGP RSC research and surveillance activities.MethodsWe described our domain-specific use case. The actor was the RCGP RSC sentinel network, the system was the course of the COVID-19 pandemic, and the outcomes were the spread and effect of mitigation measures. We used our established 3-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system–independent COVID-19 case identification algorithm. As there were no gold-standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association Primary Health Care Informatics working group and extended networks.ResultsOur use-case domains included primary care, public health, virology, clinical research, and clinical informatics. Our ontology supported (1) case identification, microbiological sampling, and health outcomes at an individual practice and at the national level; (2) feedback through a dashboard; (3) a national observatory; (4) regular updates for Public Health England; and (5) transformation of a sentinel network into a trial platform. We have identified a total of 19,115 people with a definite COVID-19 status, 5226 probable cases, and 74,293 people with possible COVID-19, within the RCGP RSC network (N=5,370,225).ConclusionsThe underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential.

The repolarization pattern of ARVC patients, healthy gene carriers and controls as analyzed with a 252-leads Body Surface Mapping Vest

Abstract Background Repolarization abnormalities have a central role on the diagnosis of ARVC according to recent HRS consensus document from 2019 stating that T wave inversion in the right precordial leads is a major criteria if it appears in V1-V3 or a minor criteria if it appears in only V1-V2. Purpose The aim of our study was to investigate whether repolarization patterns as recorded by a Body Surface Mapping (BSM) system consisting of a vest with 252 ECG leads, could differentiate ARVC patients and even gene carriers from normal individuals. Our hypothesis is that the method can potentially identify repolarization disturbances earlier or better than conventional 12-lead ECG. Method 12 definite ARVC patients, 20 healthy gene carriers and 8 family members who tested negative for the family mutation (controls) were included. All patients underwent 12-lead ECG, including right precordial leads (V4R) and BSM recordings. Repolarization (T-wave polarity and concordance with QRS complex vector) was analyzed qualitatively in all BSM recordings, the results of which were displayed on a color code map (fig.1). Results The mean age was 49.6, 43.6 and 38.8 years in ARVC patients, healthy gene carriers and controls, respectively. The number of males in the three groups were 8/12, 8/20 and 5/8, respectively. All 8 controls had similar repolarization patterns with negative and concordant T waves on the right back panel, and T waves that successively changed from negative concordant (green) to positive disconcordant (red) and finally positive concordant (blue) on the left front panel (pattern 1). All 12 ARVC patients had different repolarization patterns as compared to the controls. Two of these patients had no apparent repolarization changes on conventional 12 lead ECG. The pattern type 2 repolarization, as defined by same pattern as the controls at the right back panel but different pattern at the front left panel was seen in 3/12 ARVC patients. The remaining 9 ARVC patients had different repolarization patterns both on the front and on the back panel (pattern 3). Among gene carriers, 15 had a normal repolarization pattern (pattern 1) and 5 demonstrated an abnormal repolarization pattern (4 had pattern type 2 and one pattern 3) despite normal surface ECG. Conclusions Using BSM recordings, abnormal repolarization patterns can be detected in all ARVC patients, even in those without repolarization changes on conventional surface ECG. The observation that 25% of gene carriers had divergent repolarization patterns, may indicate an early stage of the disease, and be used as an early diagnostic marker of the disease. Further and larger studies are warranted to confirm these observations. Repolarisation patterns recorded by BSM Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Selanders Stiftelse

Full‐duplex ambient backscatter with physical layer network coding

Ambient backscatter which utiliees the ambient radio-frequency signals to enable transmissions between battery-less devices has attracted considerable attention in academia and industry. This study addresses the questions of full-duplex (FD) and self-interference cancellation (SIC) for ambient backscatter which adopts dual-antenna structure to achieve persistent information transmission and energy harvesting. The authors construct the physical-layer network coding (PNC) mapping principle of on–off keying in the dual-antenna structure and propose the PNC-based FD algorithm to eliminate the self-interference in the information domain. Furthermore, the bit-error-ratio and throughput performances are analysed under conventional half-duplex mode and the proposed FD mode, respectively. In order to facilitate system complexity and reduce power consumption, they design a hardware prototype to achieve SIC which consists of low-power passive analogue components. Numerical results are provided to confirm the theoretical studies. Circuit simulations demonstrate that the circuit is commendably consistent with the design concept.

Extending Gardiner’s code for Hieroglyphic recognition and English mapping

“Knowledge is power”… Writing is the main way to preserve the humanity knowledge across the ages. Therefore, an automatic and accurate mapping from ancient scripts to modern live language is a must to have. Such a system will support knowledge transfer, tourism and education. This article presents a new algorithm to segment and recognize the ancient Egyptian Hieroglyphs from images and produce the corresponding English meaning. The algorithm used image processing along with Optical Character Recognition (OCR). Then, the meaning behind the image containing Hieroglyphs is interpreted based on the context of the mapped English sentence. Gardiner’s sign list is a standard list used to classify the Hieroglyphics symbols such that similar shapes are grouped in the same category. Hence, Hieroglyphics script is mapped to the English language. Hieroglyphics could be either read from left to right or from right to left based on the face orientation of the Hieroglyphic symbol. However, Gardiner’s code does not help to automate the reading direction of the Hieroglyphics script. In this work, an extension of the list is proposed to resolve the ambiguity in the reading direction. The results of the segmentation and recognition of Hieroglyphics symbols are demonstrated and compared to similar work for Chinese character recognition. Moreover, the results obtained from the state-of-the-art used in Hieroglyphic character recognition compared to the results obtained from the proposed algorithm on Hieroglyphic character are addressed. The mapped English sentence is then compared to some defined patterns. When a match is found, the input gets structured and reformatted accordingly. Tests on the defined patterns were conducted, and the results were successful, however, some additional results are generated that show equivalent synonyms to the input English sentence.