Processing System Research Articles

Artificial intelligibility: the role of gender in assigning humanness to natural language processing systems

ABSTRACT In place of ‘Artificial Intelligence’, this article proposes artificial intelligibility as the more accurate term for describing the mistaken assignment of humanness to non-living objects. Artificial Intelligibility is manifested when a user assumes an object has capacity to understand them simply because it is understandable to them. Relatively simplistic Natural Language Processing systems may perform genres of humanness in conversational interactions to the degree that they are imagined to be sentient, as ‘Artificial Intelligence’. However, decolonial scholars have observed that humanness developed as a mutable, sociogenic construct. Decolonial gender theorists have further derived the role played by heteronormativity and repronormativity in upholding this power. Equating the intelligible performance of a gendered genre of humanness with intelligence risks obfuscating this interplay while reinforcing eugenic stratifications of life. This article reframes behavioural measures of ‘intelligence’ to gendered ‘intelligibility’ to explore the role of gender in enabling entry into the symbolic order of humanness. It presents three key findings from my doctoral thesis to question how and why some gendered, NLP-incorporating devices can be imagined to have lives that matter within the same economy of value that renders some humans and animals killable.

Sistem Pendukung Keputusan Pemilihan Kepala Cabang SCO di JNE Menerapkan Kombinasi Metode Dematel dan Aras

The head of the SCO (Sales Counter Officer) branch is a person who has good experience, knowledge and skills to lead and process finances every day who offer products available from the goods delivery service at the JNE Ekpress company. The SCO branch head also plays a role in organizing and coordinating SCO workers on duty, starting from arranging shifts, taking work records, and minimizing losses caused by SCO input errors. Decision Support Systems are computer-based systems consisting of several components, namely language system components, knowledge system components and problem processing system components, which are interconnected with each other in making a decision. through the use of data and decision models in order to solve a problem. DEMATEL (Decision Making Trial And Evaluation Laboratory) aims to find direct (dependency) in a variable system. The Additive Ratio Assessment (ARAS) method is a method used for ranking criteria. In carrying out the ranking process, this method is very suitable for use in selecting candidates for SCO branch heads. Where the highest value is the best value of the final result. Based on the test results, it is known that Rahmad is a candidate who meets the criteria to become the new SCO branch head with a score of 0.957972 or 96%.

Multidimensional Fiber‐to‐Chip Optical Processing Using Photonic Integrated Circuits

AbstractMultidimensional multiplexing technologies have been proven to be a promising scheme to meet the demands of high‐capacity optical interconnects and optical processing networks. However, challenges remain to realize multidimensional hybrid multiplexing data transmission and signal processing between few‐mode fiber transmission links and on‐chip optical processing networks, since the optical coupling between fiber and chip is almost exclusively in the single‐mode regime. Here, a multidimensional fiber‐to‐chip optical processing system is proposed and demonstrated for hybrid wavelength‐, mode‐, and polarization‐division multiplexing signals, where multidimensional coupling between few‐mode fiber transmission link and on‐chip multi‐mode processing network is realized by using a 3D photonic integrated (de)multiplexers on a glass chip as well as 2D photonic integrated (de)multiplexers on a silicon chip, and the on‐chip multidimensional optical processing network composed by parallel cascaded micro‐ring resonator array performs a function of reconfigurable optical add/drop multiplexer. By operating wavelength‐division multiplexing in conjunction with mode‐division multiplexing and polarization‐division multiplexing, the communication bandwidth of fiber‐to‐chip optical processing systems can be further scaled.

Threat, safety, safeness and social safeness 30 years on: Fundamental dimensions and distinctions for mental health and well-being.

In 1993, the British Journal of Clinical Psychology published my paper titled 'Defence and safety: Their function in social behaviour and psychopathology'. The paper highlightsthat to understand people's sensitivity to threat, we also need to understand their ability to identify what is safe. This paper offers an update on these concepts, highlighting distinctions that were implicit but not clearly defined at the time. Hence, the paper seeks to clarify distinctions between: (i) threat detection and response, (ii) safety and safety seeking, (iii) safeness and (iv) their social and non-social functions and forms. Threat detection and response are to prevent or minimize harm (e.g., run from a predator or fire). Safety checking relates to monitoring for the absence and avoidance of threat, while safety seeking links to the destination of the defensive behaviour (e.g., running home). Safety seeking also relates to maintaining vigilance to the appearance of potential harms and doing things believed to avoid harm. Threat-defending and safety checking and seeking are regulated primarily through evolved threat processing systems that monitor the nature, presence, controllability and/or absence of threat (e.g., amygdala and sympathetic nervous system). Safeness uses different monitoring systems via different psychophysiological systems (e.g., prefrontal cortex, parasympathetic system) for the presence of internal and external resources that support threat-coping, risk-taking, resource exploration. Creating brain states that recruit safeness processing can impact how standard evidence-based therapies (e.g., exposure, distress tolerance and reappraisal) are experienced and produce long-term change.

A Combination of UV and Disinfectant for Inactivating Viable but Nonculturable State Pseudomonas aeruginosa: Efficiency and Mechanisms

Conventional disinfection techniques, relying on a single disinfection step, often fail to directly eliminate microorganisms, instead causing them to enter a viable but nonculturable (VBNC) state. However, microorganisms in the VBNC state retain metabolic activity and can reactivate under suitable conditions, representing a “hidden source of contamination” that threatens drinking water safety. This study fundamentally assessed the feasibility of combined disinfection methods by integrating UV254 with disinfectant (NaClO, PAA, and PDS) for inactivating Pseudomonas aeruginosa (P. aeruginosa), an opportunistic pathogen that has been widely detected in water supply systems. The number of culturable cells was determined using the heterotrophic plate counting (HPC) method, and the number of VBNC cells was quantified using our recently developed qPCR approach. Quantitative analyses showed that combined disinfection methods can effectively reduce both culturable and VBNC cells by several orders of magnitude compared to a single disinfection step. Notably, VBNC P. aeruginosa, after 30 min of UV/NaCIO treatment, was below the detection limit (3.191 log CFU/mL) of PMA-qPCR. The reactivation experiment also confirmed that VBNC P. aeruginosa did not reactivate for 16 h after 30 min of UV/NaClO treatment under controlled laboratory conditions. The higher disinfection capacity of combined methods can be attributed to the generation of reactive radicals. This study highlighted combined disinfection as a promising approach for the inactivation of bacteria in the VBNC state, yet further studies are needed before an application can be considered for minimizing VBNC reactivation in city utility water processing or high-risk building water distribution systems.

Application of neural network algorithm in computer data information processing system

Application of neural network algorithm in computer data information processing system

YOLO-ET: A Machine Learning model for detecting, localising and classifying anthropogenic contaminants and extraterrestrial microparticles optimised for mobile processing systems

YOLO-ET: A Machine Learning model for detecting, localising and classifying anthropogenic contaminants and extraterrestrial microparticles optimised for mobile processing systems

Autonomous Artificial Olfactory Sensor Systems with Homeostasis Recovery via a Seamless Neuromorphic Architecture.

Neuromorphic olfactory systems have been actively studied in recent years owing to their considerable potential in electronic noses, robotics, and neuromorphic data processing systems. However, conventional gas sensors typically have the ability to detect hazardous gas levels but lack synaptic functions such as memory and recognition of gas accumulation, which are essential for realizing human-like neuromorphic sensory system. In this study, we propose a seamless architecture for a neuromorphic olfactory system capable of detecting and memorizing the present level and accumulation status of nitrogen dioxide (NO2) during continuous gas exposure, regulating a self-alarm implementation triggered after 147 and 85 s at a continuous gas exposure of 20 and 40ppm, respectively. Thin-film-transistor type gas sensors utilizing carbon nanotube semiconductors detect NO2 gas molecules through carrier trapping and exhibit long-term retention properties, which are compatible with neuromorphic excitatory applications. Additionally, the neuromorphic inhibitory performance was also characterized via gas desorption with programmable ultraviolet light exposure, demonstrating homeostasis recovery. These results provide a promising strategy for developing a facile artificial olfactory system that demonstrates complicated biological synaptic functions with a seamless and simplified system architecture. This article is protected by copyright. All rights reserved.

High-rate Gamma Spectrometry Using a LaBr3(Ce) Scintillator with a Fast Pulse Shaping.

The performance of a LaBr3(Ce) gamma spectrometer at high count rates was investigated up to an input count rate of 1.3 Mcps. In order to make its pulse processing faster, a preamplifier provided by the detector manufacturer was eliminated, and the signal from the photomultiplier tube was fed directly to a digital pulse processing system. To accomplish both fast pulse processing and good energy resolution, the pulse-shaping parameters were optimized at a low count rate of 1.5 kcps, and then measurements were carried out at various count rates. Input count rates ranging from 1.5 kcps to 21 kcps were produced using a set of 137Cs resin sources, while higher rates between 45 kcps and 1.3 Mcps were produced using a 1.2-GBq 137Cs source. The spectrometer showed an excellent performance for the input rate up to 150 kcps, while the dead time increased rapidly for the input rates above 150 kcps. The system dead time has been improved greatly by eliminating the preamplifier.

The comparison of hot and cold executive functions in patients with bipolar II disorder, borderline personality disorder, and healthy individuals.

Differential diagnosis of bipolar II disorder (BD-II) and borderline personality disorder (BPD) has always been challenging for clinicians due to symptoms' overlap. This study aimed to compare hot and cold executive functions (EFs) in BD-II patients, as well as BPD and healthy controls (HCs), in order to differentiate these two disorders. In the present study, 30 BD-II and 30 BPD patients undergoing the drug therapy with mood stabilizers, and 30 HC were examined using EFs evaluated tests. The data were then analyzed using ANOVA and Tukey post hoc test. The BD-II Patients performed significantly less in all cold EFs than the HC. Also, BPD patients had meaningfully lesser performance compared to HC in all cold EFs except sustained attention. No significant difference was perceived between the two patient groups in the cold EFs. In BD-II patients, the risky decision-making as a hot EFs' component was not significantly different from HC; nevertheless, its amount was significantly higher in BPD than in the HC and BD-II patients. These findings underline the differences between the two mentioned disorders based on the hot EFs, which may indicate further disorder in the emotional information processing system among the BPD patients.

Design Wallace Tree Multiplier Using Reversible Gates

Abstract: Multipliers are essential components in digital circuit design. They have wide spread applications in digital signal processing and communication systems. Circuit designers in VLSI design seek compact, small scale circuits with low power consumption and minimal delay. The Wallace tree multiplier is an advanced version of tree based multipliers. Numerous algorithms have been developed to create the fastest multipliers, and the Wallace tree multiplier is one such example. It utilizes reversible compressors, full adders, and half adders. The results demonstrate that the Wallace tree multiplier using reversible gates outperforms traditional multipliers in terms of power dissipation and delay, with values of 0.027W and 29.327nS re­spectively.

Real-Time Data Processing Method for CO2 Dispersion Interferometer on EAST

Abstract In this paper, a real-time data processing system is designed for the carbon dioxide dispersion interferometer (CO2-DI) on EAST to measure electron density and provide real-time feedback control. The system utilizes the parallel and pipelining capabilities of Field-Programmable Gate Array (FPGA) to digitize and process the intensity of signals from the detector. Finally, the real-time electron density signals are exported through a Digital-to-Analog Converter (DAC) module in the form of analog signals. The system has been successfully applied in the CO2-DI system to provide low-latency electron density data to the plasma control system on EAST. Experimental results of the latest campaign with long-pulse discharges on EAST (2022-2023) demonstrate that the system can respond effectively in the case of rapid density changes, which means it is reliable and accurate for future electron density calculation.

Literatur Review Management Analysis of Medical Record Management System in Puskesmas

The medical record management system at the community health center level is the same as the hospital medical record management system. This study aims to determine the management of the medical record system in health centers. This study is a literature review using methods of traditional review to seek, identify, evaluate, and interpret theoretical references relevant to the management of medical record systems in public health centers. The numbering system used in the health center is the Unit Numbering System, The medical record naming system uses a direct naming system, a centralized medical record document storage system at the health center, and a medical record document alignment system at the health center, namely a direct number alignment system and alignment system per region area. According to alphabetical order, assembly activities have not been carried out properly because there is no standard operating procedure for their implementation. The coding implementation at the community health centers did not use the ICD-10 book as a guideline for providing disease codes, the community health centers did not have a fixed retention schedule, so the retention and destruction of medical record documents at the community health centers had not been implemented. Conclusion: The numbering and naming system implemented at the community health centers is inappropriate, and the implementation of the medical record data processing system at the community health centers has not been optimal.

Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System

Image processing systems can be used to measure the accuracy of 3D-printed objects. These systems must compare images of the CAD model of the object to be printed with its 3D-printed counterparts to identify any discrepancies. Consequently, the integrity of the accuracy measurement process is heavily dependent on the image processing settings chosen. This study focuses on this issue by developing a customized image processing system. The system generates binary images of a given CAD model and its 3D-printed counterparts and then compares them pixel by pixel to determine the accuracy. Users can experiment with various image processing settings, such as grayscale to binary image conversion threshold, noise reduction parameters, masking parameters, and pixel-fineness adjustment parameters, to see how they affect accuracy. The study concludes that the grayscale to binary image conversion threshold has the most significant impact on accuracy and that the optimal threshold varies depending on the color of the 3D-printed object. The system can also effectively eliminate noise (filament marks) during image processing, ensuring accurate measurements. Additionally, the system can measure the accuracy of highly complex porous structures where the pore size, depth, and distribution are random. The insights gained from this study can be used to develop intelligent systems for the metrology of additive manufacturing.

ScalaBFS2: A High-performance BFS Accelerator on an HBM-enhanced FPGA Chip

The introduction of High Bandwidth Memory (HBM) to the FPGA chip makes it possible for an FPGA-based accelerator to leverage the huge memory bandwidth of HBM to improve its performance when implementing a specific algorithm, which is especially true for the Breadth-First Search (BFS) algorithm that demands a high bandwidth for accessing the graph data stored in memory. Different from traditional FPGA-DRAM platforms where memory bandwidth is the precious resource due to the limited DRAM channels, FPGA chips equipped with HBM have much higher memory bandwidths provided by the large quantities of HBM channels, but still a limited amount of logic (LUT, FF, and BRAM/URAM) resources. Therefore, the key to design a high-performance BFS accelerator on an HBM-enhanced FPGA chip is to efficiently use the logic resources to build as many as possible Processing Elements (PEs) and configure them flexibly to obtain as high as possible effective memory bandwidth that is useful to the algorithm from the HBM, rather than partially emphasizing the absolute memory bandwidth. To exploit as high as possible effective bandwidth from the HBM, ScalaBFS2 conducts BFS in graphs in a vertex-centric manner and proposes designs, including the independent module (HBM Reader) for memory accessing, multi-layer crossbar, and PEs that implement hybrid mode (i.e., capable of working in both push and pull modes) algorithm processing, to utilize the FPGA logic resources efficiently. Consequently, ScalaBFS2 is able to build up to 128 PEs on the XCU280 FPGA chip (produced with the 16 nm process and configured with two HBM2 stacks) of a Xilinx Alveo U280 board and achieves performance of 56.92 Giga Traversed Edges Per Second (GTEPS) by fully using its 32 HBM memory channels. Compared with the state-of-the-art graph processing system (i.e., ReGraph) built on top of the same board, ScalaBFS2 achieves 2.52x~4.40x performance speedups. Moreover, when compared with Gunrock running on an Nvidia A100 GPU that is produced with the 7 nm process and configured with five HBM2e stacks, ScalaBFS2 achieves 1.34x~2.40x speedups on absolute performance, and 7.35x~13.18x speedups on power efficiency.

Computational imaging with randomness

AbstractImaging is a longstanding research topic in optics and photonics and is an important tool for a wide range of scientific and engineering fields. Computational imaging is a powerful framework for designing innovative imaging systems by incorporating signal processing into optics. Conventional approaches involve individually designed optical and signal processing systems, which unnecessarily increased costs. Computational imaging, on the other hand, enhances the imaging performance of optical systems, visualizes invisible targets, and minimizes optical hardware. Digital holography and computer-generated holography are the roots of this field. Recent advances in information science, such as deep learning, and increasing computational power have rapidly driven computational imaging and have resulted in the reinvention these imaging technologies. In this paper, I survey recent research topics in computational imaging, where optical randomness is key. Imaging through scattering media, non-interferometric quantitative phase imaging, and real-time computer-generated holography are representative examples. These recent optical sensing and control technologies will serve as the foundations of next-generation imaging systems in various fields, such as biomedicine, security, and astronomy.

Pengembangan Sistem E-learning Berbasis Web Pada Sekolah Tingi Teknologi Informatika Sony Sugema

Sony Sugema College of Information Technology is one of the educational institutions that has a data processing system, so it requires that all data can be processed properly, stored in an orderly manner and its origin can be easily traced. However, the academic system used still has shortcomings, namely not optimal course participation, management of student data, teacher data, course schedules, course allocation and the absence of an online learning system throughout the semester. Then, to overcome deficiencies in the academic system, the author aims to develop the academic system of the Sony Sugema College of Information Technology using the Waterfall software development method. Then develop a system using the Hypertext Preprocessor (PHP) programming language, MySQL database and bootstrap framework, as well as Visual Studio Code as a coding tool. By creating this information system, it is hoped that it can help schools in managing learning data. The final result of this research is a web-based E-learning system that can optimize attendance information services, manage student data, lecturer data, lecture schedules, course classification, course division, and online learning systems during the semester period.

Predicting Bulk Density for Agglomerated Raspberry Ketone via Integrating Morphological and Size Metrics Using Artificial Neural Networks

The bulk density of the particles, which is directly related to transportation and storage costs, is an important basic characteristic of products as well as an important parameter in many processing systems. This work quantified the relationship between the tapped bulk density of raspberry ketone with different degrees of agglomeration and morphological metrics (particle shape descriptors and roughness descriptors) and size metrics (size descriptors) and developed an artificial neural network (ANN) prediction model for the tapped bulk density of raspberry ketone. Samples prepared under different conditions were sieved and remixed, the tapped bulk density of the particles was then measured, and the descriptor features of the particles were obtained by combining them with image processing. The dimensions of the variables were decreased by principal component analysis and variance processing. To overcome the hyperparameter estimation of the heuristic-based artificial neural networks, the network model architectures were optimized by a neural architecture search strategy combining two-objective optimization. The results demonstrated that the tapped bulk density of raspberry ketone products is not only related to the descriptors of particle size and shape but also has a non-negligible relationship with particle roughness descriptors. The performance of the optimal ANN model demonstrated that the model can well predict the tapped bulk density of raspberry ketone with different degrees of agglomeration. The ANN model obtained by extracting morphology and size metrics through online image analysis can be used to measure the tapped bulk density in real-time and has the potential to be used for developing model-based online process monitoring.

Leveraging business intelligence solutions for urban parking management

Efficient parking management is essential for enhancing customer experience, mobility, accessibility, and overall quality of life in urban areas. In recent years, parking analytics have emerged as valuable tools for understanding drivers’ behavior and developing data-driven management strategies. However, the application of these tools is often hindered by the complexity of data extraction, transformation, loading and analysis. Additionally, the implementation of these tools can be time-consuming and costly, further limiting their practical use for operators and urban authorities. To address this issue, this paper presents the development of a Business Intelligence tool specifically designed to facilitate parking management through the automated flow of transaction data between collection, processing, and analysis systems. The tool provides easy-to-use analytical capabilities that allow parking managers to analyze parking transaction data, identify trends and patterns, and make informed decisions about parking management quickly and easily. The cost-effective implementation of this tool presents a valuable solution for managing on-street parking in urban areas. This study highlights the potential of Business Intelligence tools for parking management and contributes to improving the effectiveness of parking management.

Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems

In the realm of automated industry and smart production, the deployment of fault warning systems is crucial for ensuring equipment reliability and enhancing operational efficiency. Although there are a multitude of existing methodologies for fault warning, the proficiency of these systems in processing and analysing data is increasingly challenged by the progression of industrial apparatus and the escalating magnitude and intricacy of the data involved. To address these challenges, this research outlines an innovative fault warning methodology that combines a bi-directional long short-term memory (Bi-LSTM) network with an enhanced hunter–prey optimisation (EHPO) algorithm. The Bi-LSTM network is strategically utilised to outline complex temporal patterns in machinery operational data, while the EHPO algorithm is employed to meticulously fine-tune the hyperparameters of the Bi-LSTM, aiming to enhance the accuracy and generalisability of fault warning. The EHPO algorithm, building upon the foundational hunter–prey optimisation (HPO) framework, introduces an advanced population initialisation process, integrates a range of strategic exploration methodologies, and strengthens its search paradigms through the incorporation of the differential evolution (DE) algorithm. This comprehensive enhancement aims to boost the global search efficiency and accelerate the convergence speed of the algorithm. Empirical analyses, conducted using datasets from real-world industrial scenarios, have validated the improved warning performance of this proposed methodology against some benchmark techniques, as evidenced by superior metrics such as root mean square error (RMSE) and mean absolute error (MAE), albeit with a slight increase in computational resource requirements. This study not only proposes a novel paradigm for fault warning within complex industrial frameworks but also contributes to the discourse on hyperparameter optimisation within the field of machine learning algorithms.