Code Patterns Research Articles

Single-Track Magnetic Tape Absolute Position Sensor with Self-Adaptivity.

In this study, we demonstrate a single-track magnetic code tape-based absolute position sensor system. Unlike traditional dual-track systems, our method simplifies manufacturing and avoids crosstalk between tracks, offering higher tolerance to alignment errors. The sensing system employs an array of magnetic field sensing elements that recognize the bit sequence encoded on the tape. This approach allows for accurate position determination even when the number of sensing elements is fewer than the number of bits covered, and without the need for specific spacing between sensing elements and bit length. We demonstrate the system's ability to learn and adapt to various magnetic code patterns, including those that are irregular or have been altered. Our method can identify and localize the sensed magnetic field pattern directly within a self-learned magnetic field map, providing robust performance in diverse conditions. This self-adaptive capability enhances operational safety and reliability, as the system can continue functioning even when the magnetic tape is misaligned or has undergone changes.

Accelerating page loads via streamlining JavaScript engine for distributed learning

Distributed learning based on JavaScript-based frontends is typically implemented at the endpoint to maximize performance. Yet, JavaScript-based frontends often experience suboptimal performance. To reconcile these disparities in performance between EDGE and endpoint deployments, strategic optimization is essential, particularly for preserving privacy in distributed learning. Real-time streaming optimizations are imperative to align the performance of disparate components for smooth integration. The reliance on JavaScript for various web functionalities can lead to increased resource consumption and slower page loads. Thus, we introduce a streamlined JavaScript engine designed to optimize structural patterns in JavaScript code, with three key enhancements. Firstly, we reduce the computational burden of the JavaScript engine necessary for setting up the browser's runtime environment. Secondly, we refine the parsing process for specific code patterns, boosting the efficiency of our lightweight engine. Thirdly, we streamline the Inter-Process Communication (IPC) to maintain high performance, even with limited memory resources. Our evaluations demonstrate that our approach reduces the median Total Computation Time (TCT) by 45.2%, and surpasses existing leading solutions, Siploader and Prepack, with improvements ranging from 1.13× to 1.39×.

Muscle Sympathetic Action Potential Firing Patterns Following Intranasal Insulin Administration in Healthy Adults

Objective: Systemic insulin increases muscle sympathetic nervous system activity (MSNA) in healthy adults by augmenting firing frequency of medium-sized sympathetic action potentials (AP) and recruiting previously latent, larger axons. Whether these neural coding patterns are due to central or peripheral effects of insulin is unclear. We examined the impact of elevated central insulin on AP firing patterns in healthy adults. We hypothesized intranasal insulin administration, which increases central insulin levels, would increase MSNA via elevated firing frequency of APs and recruitment of previously latent, larger axons. Methods: Ten participants were assigned to time control [TC, n=6 (3M/3F)] or 160 IU of intranasal insulin [n=4 (3M/1F)] administered over 5 min. MSNA (fibular microneurography) was assessed at baseline and for 30 min following insulin administration. Sympathetic APs were identified using a matched mother wavelet and continuous wavelet transform. APs were divided into the proportion of those firing in small, medium, or large amplitude clusters. AP firing frequency, percent of APs firing within an MSNA burst (synchronous), and the probability of clusters firing more than once within an MSNA burst were identified. Data are reported as median (interquartile range). Results: MSNA burst frequency increased 15 min after intranasal insulin administration [24(20) to 31(23) bursts/min, p=0.03]. At this time, the percent of synchronous APs [72(27) to 90(24)%, p=0.02] and the probability of medium-sized AP clusters firing more than once within an MSNA burst [13(14) to 19(17)%, p=0.03] increased. No changes in MSNA burst frequency [23(14) to 25(13) bursts/min, p=0.27], percent of synchronous APs [74(15) to 75(34), p=0.48], nor firing probability of medium-sized AP clusters [6(12) to 8(7)%, p=0.97] occurred in TC. The total number of detected clusters did not change in either group (p>0.05). Conclusions: Intranasal insulin increases MSNA burst frequency due to a higher percent of synchronous APs, particularly medium-sized AP clusters; however, we did not observe recruitment of latent, larger axons. These exploratory data highlight potential differences in the sympathetic response to central versus peripheral insulin exposure in healthy adults. CAFNR Joy of Discovery (JKL, JP). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Multi-laser source interference suppression using the time-coding method for SPAD-based flash DToF systems

Flash-type direct time-of-flight (DToF) image sensors use an in-pixel successive approximation register time-to-digital converter (SAR TDC) for time quantization. However, in a scene where multiple DToF systems exist simultaneously, different laser signals from multiple sources will produce mutual signal interference between DToF systems, causing the DToF system’s incorrect measurement. In this paper, we present a method called time coding, which inserts delay time bins between different working periods to suppress the interference laser together with the SAR TDC. The time-coding method is designed using a 110 nm complementary metal oxide semiconductor (CMOS) technology and verified by behavioral model and circuit simulation. Regardless of traditional systems or systems equipped with time coding, DToF systems with certain patterns of time coding can reduce interference noise by at least 95%, maintaining a measurement accuracy of 99% or higher at long distances.

CodeTree: A System for Learnersourcing Subgoal Hierarchies in Code Examples

Subgoal-labeled code examples help learners understand code patterns and apply them to different problem contexts. Subgoal labels are multi-level in nature and based on goal structures that define the hierarchical functional units in code. Data-driven methods and experts can supply the goal structures, but they do not work in environments with scarce data and limited availability of experts. Previous research has shown that learnersourcing is effective for sourcing high-quality subgoal labels of given goal structures. We extend this research by learnersourcing goal structures themselves, thereby making the generation of subgoal-labeled materials fully learner-driven. We introduce CodeTree, a system that generates multi-level goal structures by aggregating learner-generated subgoals from two subgoal learning activities---Generation and Selection. In a between-subjects study, 45 novices studied three code examples with either CodeTree or code explanations alone. The results showed that CodeTree could learnersource high-quality goal structures and subgoal labels for all three examples with just five learners. Learners reported a significantly higher learning gain and satisfaction compared to the baseline.

A ternary complementary Gray code phase unwrapping method in fringe projection profilometry

This paper proposes a phase unwrapping method based on ternary Gray code in fringe projection profilometry. This method does not require additional projection images or any prior knowledge. It accurately determines the segmentation thresholds of the ternary Gray code using only the average intensity and intensity modulation calculated from phase-shifting fringes, effectively reducing projection costs and achieving lower codeword errors. Additionally, a segment selection strategy suitable for the ternary Gray code is proposed. By employing the complementary method, three sets of candidate orders with certain codeword shifts can be generated. The wrapped phase is then divided into three segments: upper, middle, and lower segments, and correspondingly matched with the central regions of the three sets of candidate orders, thus enabling the solution of the absolute phase. Through the segment selection strategy, error-prone areas at the edges of orders are effectively avoided, further enhancing the accuracy of measurement results. Compared to the binary Gray code, the ternary complementary Gray code can encode up to 3m−1 orders when the number of Gray code patterns is m; when the required number of encoding orders is K, only ⌈log3K⌉+1 Gray code patterns are needed. Its additional value capacity precisely enables it to encode more orders, thereby increasing the frequency of phase-shifting fringes and further enhancing measurement efficiency. A series of experiments validates the feasibility, robustness, and superiority of this method.

Repetition Suppression Reveals Cue-Specific Spatial Representations for Landmarks and Self-Motion Cues in the Human Retrosplenial Cortex.

The efficient use of various spatial cues within a setting is crucial for successful navigation. Two fundamental forms of spatial navigation, landmark-based and self-motion-based, engage distinct cognitive mechanisms. The question of whether these modes invoke shared or separate spatial representations in the brain remains unresolved. While nonhuman animal studies have yielded inconsistent results, human investigation is limited. In our previous work (Chen et al., 2019), we introduced a novel spatial navigation paradigm utilizing ultra-high field fMRI to explore neural coding of positional information. We found that different entorhinal subregions in the right hemisphere encode positional information for landmarks and self-motion cues. The present study tested the generalizability of our previous finding with a modified navigation paradigm. Although we did not replicate our previous finding in the entorhinal cortex, we identified adaptation-based allocentric positional codes for both cue types in the retrosplenial cortex (RSC), which were not confounded by the path to the spatial location. Crucially, the multi-voxel patterns of these spatial codes differed between the cue types, suggesting cue-specific positional coding. The parahippocampal cortex exhibited positional coding for self-motion cues, which was not dissociable from path length. Finally, the brain regions involved in successful navigation differed from our previous study, indicating overall distinct neural mechanisms recruited in our two studies. Taken together, the current findings demonstrate cue-specific allocentric positional coding in the human RSC in the same navigation task for the first time and that spatial representations in the brain are contingent on specific experimental conditions.

An empirical investigation of the relationship between pattern grime and code smells

AbstractDevelopers are encouraged to adopt good design practices to maintain good software quality during the system's evolution. However, some modifications and changes to the system could cause code smells and pattern grime, which might incur more maintenance effort. As the presence of both code smells and pattern grime is considered a bad sign and raises a flag at code segments that need more careful examination, a potential connection between them may exist. Therefore, the main objective of this paper is to (1) empirically investigate the potential relationship between the accumulation of pattern grime and the presence of code smells and (2) evaluate the significance of individual code smells when they appear in a specific pattern grime category. To achieve this goal, we performed an empirical study using six‐grime metrics and 10 code smells on five Java open‐source projects ranging from 217 to 563 classes. Our statistical results indicate that, in general, the growth of grime is more likely to co‐occur with code smells using Spearman's correlation and Odd Ratio test. Specifically, there is a strong positive association between the growth of pattern grime at the class level and the presence of Shotgun Surgery smell according to the result of applying the Apriori algorithm, which gives conviction values equal to 1.66. The findings in this paper are helpful for developers and researchers as the presence of pattern grime could be considered a factor in improving the performance of existing smell detection methods. Furthermore, the link between grime and smells can be exploited as a hint for smell distribution in the system.

Custom static analysis to enhance insight into the usage of in-house libraries

For software maintenance and evolution, insight into the codebase is crucial. One way to enhance insight is the application of static analysis to extract and visualize program-specific relations from the code itself, such as call graphs and inheritance trees. Yet, software often contains in-house libraries: unique, domain-specific libraries whose usage is typically scattered throughout the codebase. To provide sufficient insight into the usage of those libraries, the static analysis must be customized with domain-specific information. In this paper, we propose a method to enhance insight into the usage of in-house libraries by producing custom overviews. Furthermore, we describe three exploratory case studies targeting industrial C++ and Ada codebases, in which the method was developed, evolved, and validated. The method prescribes how to create custom overviews using static analysis iteratively, starting from a user-provided, initial specification of proper library usage using code patterns. As a safeguard, the method includes cross-checks to detect code fragments that deviate from proper library usage. Whenever such a deviating library usage is found, the code owners determine whether that deviating library usage should be added to the specification of proper library usage or the code fragment should be made compliant. The latter alternative makes both the codebase more regular and keeps the custom static analysis simpler. The method creates custom overviews that reveal opportunities to improve the usage of the in-house libraries, e.g., the removal of domain-specific redundant code which cannot be detected using generic tools, such as compilers and linters.We observed that industrial codebases are regular enough to create custom overviews using static analysis in the three exploratory case studies. Furthermore, we observed that the cross-checks, which detect deviating library usage, ensure the validity and completeness of the custom overviews. We conclude that producing custom overviews for in-house libraries using the method is valuable and feasible.

The Lost World: Characterizing and Detecting Undiscovered Test Smells

Test smell refers to poor programming and design practices in testing and widely spreads throughout software projects. Considering test smells have negative impacts on the comprehension and maintenance of test code and even make code-under-test more defect-prone, it thus has great importance in mining, detecting, and refactoring them. Since Deursen et al. introduced the definition of “test smell”, several studies worked on discovering new test smells from test specifications and software practitioners’ experience. Indeed, many bad testing practices are “observed” by software developers during creating test scripts rather than through academic research and are widely discussed in the software engineering community (e.g., Stack Overflow) [ 70 , 94 ]. However, no prior studies explored new bad testing practices from software practitioners’ discussions, formally defined them as new test smell types, and analyzed their characteristics, which plays a bad role for developers in knowing these bad practices and avoiding using them during test code development. Therefore, we pick up those challenges and act by working on systematic methods to explore new test smell types from one of the most mainstream developers’ Q&A platforms, i.e., Stack Overflow. We further investigate the harmfulness of new test smells and analyze possible solutions for eliminating them. We find that some test smells make it hard for developers to fix failed test cases and trace their failing reasons. To exacerbate matters, we have identified two types of test smells that pose a risk to the accuracy of test cases. Next, we develop a detector to detect test smells from software. The detector is composed of six detection methods for different smell types. These detection methods are both wrapped with a set of syntactic rules based on the code patterns extracted from different test smells and developers’ code styles. We manually construct a test smell dataset from seven popular Java projects and evaluate the effectiveness of our detector on it. The experimental results show that our detector achieves high performance in precision, recall, and F1 score. Then, we utilize our detector to detect smells from 919 real-world Java projects to explore whether the six test smells are prevalent in practice. We observe that these test smells are widely spread in 722 out of 919 Java projects, which demonstrates that they are prevalent in real-world projects. Finally, to validate the usefulness of test smells in practice, we submit 56 issue reports to 53 real-world projects with different smells. Our issue reports achieve 76.4% acceptance by conducting sentiment analysis on developers’ replies. These evaluations confirm the effectiveness of our detector and the prevalence and practicality of new test smell types on real-world projects.

Codesentry: Revolutionizing Real-Time Software Vulnerability Detection With Optimized GPT Framework

Abstract The escalating complexity and sophistication of software vulnerabilities demand innovative approaches in cybersecurity. This study introduces a groundbreaking framework, named “CodeSentry”, employing a transformer-based model for vulnerability detection in software code. “CodeSentry” leverages a finely-tuned version of the Generative Pre-trained Transformer (GPT), optimized for pinpointing vulnerable code patterns across various benchmark datasets. This approach stands apart by its remarkable computational efficiency, making it suitable for real-time applications − a significant advancement over traditional, resource-intensive deep learning models like CNNs and LSTMs. Empirical results showcase “CodeSentry” achieving an impressive 92.65% accuracy in vulnerability detection, surpassing existing state-of-the-art methods such as SyseVR and VulDeBERT. This novel methodology marks a paradigm shift in vulnerability detection, blending advanced AI with practical application efficiency.

“C”ing the light – assessing code comprehension in novice programmers using C code patterns

ABSTRACT Background and Context Feedback on one’s progress is essential to new programming language learners, particularly in out-of-classroom settings. Though many study materials offer assessment mechanisms, most do not examine the accuracy of the feedback they deliver, nor give evidence on its validity. Objective We investigate the potential use of a preexisting set of C code snippets as the basis for a high-quality C programming ability assessment tool. Method We utilize the Rasch Model and the Linear Logistic Test Model to evaluate the validity and accuracy of the code snippets and to determine which C operations contribute most to their overall difficulty. Findings Our results show that these code snippets yield accurate assessments of programming ability and reveal the degree of difficulty associated with specific programming operations. Implications Our results suggest that the code snippets could serve as the basis for sophisticated, valid, and fair code comprehension skill assessment tools.

A mismatch in the expression of cell surface molecules induces tissue-intrinsic defense against aberrant cells

Tissue-intrinsic error correction enables epithelial cells to detect abnormal neighboring cells and facilitate their removal from the tissue. One of these pathways, "interface surveillance," is triggered by cells with aberrant developmental and cell-fate-patterning pathways. It remains unknown which molecular mechanisms provide cells with the ability to compare fate between neighboring cells. We demonstrate that Drosophila imaginal discs express an array of cell surface molecules previously implicated in neuronal axon guidance processes. They include members of the Robo, Teneurin, Ephrin, Toll-like, or atypical cadherin families. Importantly, a mismatch in expression levels of these cell surface molecules between adjacent cells is sufficient to induce interface surveillance, indicating that differences in expression levels between neighboring cells, rather than their absolute expression levels, are crucial. Specifically, a mismatch in Robo2 and Robo3, but not Robo1, induces enrichment of actin, myosin II, and Ena/Vasp, as well as activation of JNK and apoptosis at clonal interfaces. Moreover, Robo2 can induce interface surveillance independently of its cytosolic domain and without the need for the Robo-ligand Slit. The expression of Robo2 and other cell surface molecules, such as Teneurins or the Ephrin receptor is regulated by fate-patterning pathways intrinsic and extrinsic to the wing disc, as well as by expression of oncogenic RasV12. Combined, we demonstrate that neighboring cells respond to a mismatch in surface code patterns mediated by specific transmembrane proteins and reveal a novel function for these cell surface proteins in cell fate recognition and removal of aberrant cells during development and homeostasis of epithelial tissues.

Remotely Controlled Laser‐Programmable Microwave Metasurfaces

AbstractProgrammable metasurfaces have brought significant advance to electromagnetic (EM) and related fields, making it easier and more efficient to manipulate the EM waves and realize higher tuning flexibility and range. However, how to achieve independent control of each unit of the programmable metasurface in a low‐overhead and wireless manner remains a challenge. Here, a laser‐programmable microwave metasurface is proposed and constructed, in which each unit can be controlled remotely by laser intensity in real time. The key is to design an ultra‐compact photoelectric passive boost circuit and integrate it into the meta‐unit. In this case, the light sensing‐to‐power‐to‐control‐to‐microwave manipulation process can be fully realized on the highly integrated meta‐unit, and thus the metasurface can be completely programmed at the unit level by laser coding patterns, without any wire connections and external power suppliers. As experimental demonstration, a phase‐coding laser‐programmable microwave metasurface is presented using a laser array. To show the capabilities of the metasurface, 2D beam scanning and vortex beam generation are demonstrated using 1‐bit phase modulations under the laser illuminations. This approach offers an effective route for the space lasers to manipulate microwaves accurately, which is helpful in developing advanced functional devices, passive platforms, and photoelectric hybrid systems.

Doing Research with Help from ChatGPT: Promising Examples for Coding and Inter-Rater Reliability

The rapid development of artificial intelligence and large language models (LLMs) has led to significant advancements in applying machine learning techniques across diverse disciplines, including educational science research. This study investigates the potential of LLMs like ChatGPT for qualitative data analysis, focusing on open, axial, selective coding, theme or pattern identification, and inter-rater reliability. Our findings indicate promising capabilities of ChatGPT in open coding, demonstrating accurate categorization of qualitative data. However, axial coding posed challenges due to the model's limited understanding, which was partially addressed by refining prompts based on ChatGPT's interpretation. ChatGPT also showed competence in selective coding and theme or pattern identification, providing additional insights. For inter-rater reliability, ChatGPT's performance varied across datasets, with improvements observed when providing contextual information. It is important to note the limitations and variability of LLMs such as ChatGPT, which is in public beta and subject to potential limitations in usage and reliability. Our study demonstrates ChatGPT's potential for coding and inter-rater reliability. Improved results are achieved with refined prompts and utilising ChatGPT's own definitions. The adoption of LLMs for qualitative analysis requires further exploration, including addressing algorithmic bias and the potential for inaccurate responses. Validation techniques are crucial in mitigating these risks.

Programmable topological metasurface to modulate spatial and surface waves in real time

Abstract We propose a programmable topological metasurface to integrate intelligent modulations of spatial and surface waves. A general design method is presented to design the programmable metasurface elements with PIN diodes. The surface waves can be controlled to propagate along the topological domain-wall interface by programming the C3-symmetry elements, while the spatial waves are modulated by the patterns of C6-symmetry elements. By independently controlling the bias voltages of meta-elements, the programmable topological metasurface can generate different coding patterns with distinct combinations of C3- and C6-symmetries in real time, respectively achieving the dynamical manipulation of the surface- and spatial-wave by using distinct element states in a time-division manner. To validate the modulation performance, we perform both near- and far-field tests with different coding patterns. Experimental results demonstrate good agreement with numerical simulations, thereby showcasing the flexible manipulations of surface waves and spatial waves by the topological metasurface. The proposed metasurface opens up new possibilities for multifunctional metadevices, which hold great potentials for future wireless communications and smart sensing systems.

A Binary Coding Pattern for Virtual Absolute Encoder

In this article, a binary coding pattern has been presented for the optical type virtual encoder. Unlike the traditional encoder, it can sense the direction, speed, and absolute position of the moving object. In this pattern, each combination of the binary number is represented by pulse transition, thus it is named as pulse transition code (PTC). In addition to the n-track binary code, it has dual clock track. So, in the absence of the external clock (i.e. reference) signal, one track can generate a clock signal during the rotation of the measured object. This signal is not only used for decoding the PTC pattern but also maintain the decoding rate of the receiver unit. Since the second clock track is placed 90 out-of-phase, thus it can sense the speed and direction of the moving object with respect to the prior clock track. Compared to the conventional binary coding pattern, its resolution varies four times per track number ((^)^). Eventually, to demonstrate the decoding methodology of the proposed coding pattern, a 2-bit PTC pattern is designed and developed an algorithm for a Microcontroller based decoder unit. The developed virtual encoder exhibits a satisfactory performance in laboratory conditions.

The Impact of Multiple API Connection Request to An Open-Source Android-based Currency Tracker's Performance and The Mitigation

High traffic data requests through APIs can significantly impact the performance of mobile applications, affecting both the user interface and overall system performance. The user's perception and experience while performing tasks within the application are closely tied to the perceived performance of the user interface. To understand the impact of connection requests on application performance, a study is necessary, with responsiveness being a critical factor. This study aims to investigate the potential effects on the user interface when each unit or feature in the application requires a data connection through the API. The study will examine code patterns and conduct multiple data requests through APIs. Performance profiling will be the main method used to test each unit or block of code related to data request and reception via the API. The obtained results from the testing process will reveal the performance of the currency tracker application, specifically in areas directly associated with data request and reception via the API. To optimize the request and data calling process, best practices such as AsyncTask and Coroutine will be implemented. The implementation of these best practices will be assessed based on a predefined matrix to measure improvements needed to enhance applications’ performance. Therefore, the results of this study will demonstrate the influence of data request and reception processes within applications on network activity. Furthermore, the implementation of best practices, specifically AsyncTask and Coroutine, will enhance the overall performance of the application.

A Blockchain Approach to Extract Design Patterns From Source Codes

Design patterns are an effective technique for software development. However, lacking suitable documents often reduces traceability, and advantages remain unclear in source lines of code (SLOC). Design patterns are proven, reliable solutions to several frequent problems in object‐oriented software design. Detecting design patterns in a software system contributes to maintainability and reengineering and facilitates understanding codes. Furthermore, different design pattern implementations impede the detection of code pattern instances. A design pattern is a set of roles played by the code classes. Roles are the main details and determinants in patterns. A pattern can be detected in its defined role as it is an instance of a role. It is also of great importance in system documentation. The proposed method exploits blockchain‐based approaches for pattern mining and isolating design patterns in order to identify intercode relationships. The results demonstrate that the blockchain is effective and efficient in extracting the connections of features from source codes and adapting to design patterns with precision 97.2%.

Visible-light excitable thermally activated delayed fluorescence from hydrogen-bonded D-A aromatic N-heterocycle/polymer material and the application for anti-counterfeiting

Thermally activated delayed fluorescence (TADF) polymer materials have shown promising potential in the optical information encryption. However, these materials still face challenges related to complex and toxic fabrication processes for guest molecules, transitional metal doping, expensive catalysts, toxic organic solvents, and the requirement of UV light excitation. To address these challenges, we present the facile synthesis of a crystalline intramolecular hydrogen-bonded D-A aromatic N-heterocycle crystal (DI-1) with suitable energy splitting between singlet and triplet excited states (ΔEST), which is achieved using a slow solvent diffusion method with ethanol as the resolvent. Furthermore, we propose a green and straightforward host-guest doping strategy by coupling DI-1 with water-soluble PVA (DI-1/PVA-0.3 wt%) through a water-promoted dispersion method. The DI-1/PVA-0.3 wt% composite exhibits high quantum efficiency and long TADF lifetime when excited by visible light at room temperature. These desirable properties are attributed to the appropriate ΔEST of DI-1 with D-A structure and the establishment of a hydrogen bonding network between DI-1 and PVA. Additionally, we demonstrate the facile fabrication of an anti-counterfeiting 2D code pattern using environmentally friendly and cost-effective screen printing techniques with DI-1/PVA-0.3 wt%. This pattern can be easily deciphered using white light, offering insights into commercial production for dual optical and digital security protection. Our work provides a strategy for the development of TADF materials that can be excited by visible light, showcasing their potential application in information encryption and decryption.