Hasil untuk "Information technology"

Yuanchen Cheng, Zichen Zhang, Yuqing Liu et al.

Accurate estimation of tuna catch is crucial for effective pelagic fishery management and resource conservation. However, existing manual counting methods suffer from issues such as low accuracy and poor timeliness, highlighting the urgent need for an efficient and automated solution. This paper proposes an automatic tuna counting method based on the YOLOv8n-DMTNet target detection algorithm combined with the improved ByteTrack tracking algorithm. The method uses YOLOv8n as the base model, enhanced with detail-enhanced convolution and a multi-scale feature fusion pyramid network, which significantly improves detection accuracy in complex marine environments. Additionally, a dynamic, task-aligned detection head is introduced to optimize the synergy between classification and localization tasks. To further improve counting accuracy, the ByteTrack algorithm is employed for target tracking, and a region-specific counting method is designed to prevent double counting and omission due to occlusion and motion irregularities. Experimental results show that the improved YOLOv8n-DMTNet model achieves a 9.2% increase in mAP@0.5 and a 6.4% increase in mAP@0.5:0.95 compared to YOLOv8n in the tuna detection task, while reducing the number of parameters by 42.3% and computational complexity by 33.3%. The counting accuracy reaches 93.5%, and the method demonstrates superior performance in terms of accuracy, robustness, and computational resource efficiency, making it well-suited for resource-constrained fishing vessel environments. This approach provides reliable technical support for automated catch counting in pelagic fisheries.

WANG Bo, JIANG Xuping, HUANG Qihong

Knowledge of weaponry and equipment is a crucial basis for formulating equipment utilization strategies and development pathways. To address issues such as data redundancy, high interaction difficulty, and low match accuracy of question answers, this paper constructs a Q&A system based on a knowledge graph for weaponry and equipment. The system achieves named entity recognition and classification of questions by fine-tuning the BERT model; it generates graph database query statements by filling named entities into question templates and generates answers by filling answer templates. Analysis of test results shows that this system is capable of effectively ranking correct answers at the top and has achieved a good balance between accuracy and comprehensiveness, although there is still room for improvement.

Jiawei Zhang

The advent of ChatGPT has sparked over a year of regulatory frenzy. However, few existing studies have rigorously questioned the assumption that, if left unregulated, AI chatbot's output would inflict tangible, severe real harm on human affairs. Most researchers have overlooked the critical possibility that the information market itself can effectively mitigate these risks and, as a result, they tend to use regulatory tools to address the issue directly. This Article develops a yardstick for reevaluating both AI-related content risks and corresponding regulatory proposals by focusing on inter-informational competition among various outlets. The decades-long history of regulating information and communications technologies indicates that regulators tend to err too much on the side of caution and to put forward excessive regulatory measures when encountering the uncertainties brought about by new technologies. In fact, a trove of empirical evidence has demonstrated that market competition among information outlets can effectively mitigate most risks and that overreliance on regulation is not only unnecessary but detrimental, as well. This Article argues that sufficient competition among chatbots and other information outlets in the information marketplace can sufficiently mitigate and even resolve most content risks posed by generative AI technologies. This renders certain loudly advocated regulatory strategies, like mandatory prohibitions, licensure, curation of datasets, and notice-and-response regimes, truly unnecessary and even toxic to desirable competition and innovation throughout the AI industry. Ultimately, the ideas that I advance in this Article should pour some much-needed cold water on the regulatory frenzy over generative AI and steer the issue back to a rational track.

Wentao HU, Hui LAO, Ao QIU et al.

In recent years, silicon photomultipliers (SiPMs) have emerged as preferred photoelectric conversion devices in positron emission tomography (PET) due to their outstanding performance. SiPMs possess single-photon resolution capability and time resolution below 100 ps, enabling precise photon arrival time measurements. These advances paved the way for emerging applications such as time-of-flight PET (TOF-PET), photon counting CT, and positron emission lifetime imaging, presenting new challenges to SiPM performance, the advancing of which to their physical limits has become a key focus area in next-generation SiPM research. In traditional SiPM architectures, signal processing and analog-to-digital conversion introduce noise and degrade time performance, thereby limiting the full SiPM potential. With the recent and rapid development of semiconductor manufacturing processes, SiPMs could be manufactured on standard CMOS process nodes, which marks a significant breakthrough in the SiPM field, allowing for the integration of digital logic within SiPM devices. This advancement opens the possibility of achieving more precise time, energy, and position information within a single SiPM, thereby providing potential possibilities to push SiPMs to their performance limits. In this study, we reviewed the development history, working principles, and performance parameters of SiPMs. We analyzed the limitations of traditional SiPMs, outlined key aspects of digital SiPM research, and introduced various current digital SiPM architectures. Finally, we summarized and anticipated key technologies in digital SiPMs.

Pei Tang, Zhenyu Ding, Minnan Jiang et al.

Autonomous driving technology plays a key role in addressing traffic safety issues and relieving traffic congestion by virtue of its capabilities of enabling accurate environmental perception and real-time response. Aiming at the problem of limited computing power of mobile driving platform, an improved algorithm based on YOLOv8n: LBT-YOLO was proposed. The algorithm is improved in three aspects: firstly, replacing part of the traditional convolutional layers by linear deformable convolution, and designing a new C2L module by optimizing the C2F module, so as to reduce the number of model parameters and maintain the detection accuracy at the same time. Secondly, a new neck network structure BCFPN (Bidirectional Collocated Feature Pyramid Network) is designed based on the weighted bidirectional feature pyramid network, which enhances the feature fusion and the interaction of contextual information, and improves the detection accuracy of the model. Finally, a new detection head TADDH (Task Aligned Dynamic Detection Head) is proposed. This detection head reduces the number of parameters by sharing the neck network features, and performs task decomposition alignment to achieve high accuracy target detection using dynamic convolution and dynamic feature selection. After a series of improvements, LBT-YOLO outperforms YOLOv8n and other detection algorithms on the Autonomous Driving BDD100K dataset, with an average accuracy improvement of 2.4% while reducing the number of model parameters by 48.2%.

Mubarak Yakubova, Olga Manankova, Assel Mukasheva et al.

The research problem described in this article is related to the security of an IP network that is set up between two cities using hosting. The network is used for transmitting telephone traffic between servers located in Germany and the Netherlands. The concern is that with the increasing adoption of IP telephony worldwide, the network might be vulnerable to hacking and unauthorized access, posing a threat to the privacy and security of the transmitted information. This article proposes a solution to address the security concerns of the IP network. After conducting an experiment and establishing a connection between the two servers using the WireShark sniffer, a dump of real traffic between the servers was obtained. Upon analysis, a vulnerability in the network was identified, which could potentially be exploited by malicious actors. To enhance the security of the network, this article suggests the implementation of the Transport Layer Security (TLS) protocol. TLS is a cryptographic protocol that provides secure communication over a computer network, ensuring data confidentiality and integrity during transmission. Integrating TLS into the network infrastructure, will protect the telephone traffic and prevent unauthorized access and eavesdropping.

Tala Talaei Khoei, Naima Kaabouch

Intrusion Detection Systems are expected to detect and prevent malicious activities in a network, such as a smart grid. However, they are the main systems targeted by cyber-attacks. A number of approaches have been proposed to classify and detect these attacks, including supervised machine learning. However, these models require large labeled datasets for training and testing. Therefore, this paper compares the performance of supervised and unsupervised learning models in detecting cyber-attacks. The benchmark of CICDDOS 2019 was used to train, test, and validate the models. The supervised models are Gaussian Naïve Bayes, Classification and Regression Decision Tree, Logistic Regression, C-Support Vector Machine, Light Gradient Boosting, and Alex Neural Network. The unsupervised models are Principal Component Analysis, K-means, and Variational Autoencoder. The performance comparison is made in terms of accuracy, probability of detection, probability of misdetection, probability of false alarm, processing time, prediction time, training time per sample, and memory size. The results show that the Alex Neural Network model outperforms the other supervised models, while the Variational Autoencoder model has the best results compared to unsupervised models.

Dzakiya Ishmatul Ulya, Anang Kunaefi, Dwi Rolliawati et al.

QRIS, a mobile payment transaction system standardized by Bank Indonesia, has become the subject of extensive public discourse on Twitter. Employing VADER for sentiment analysis and LDA for topic modeling, this study aims to capture the nuanced perspectives of the Indonesian public toward QRIS. Our methodology includes real human validation for tweets that have been initially labeled by VADER. Our unique contributions lie in employing a mixed-methods approach for comprehensive sentiment and topic analysis, as well as making our dataset publicly available for future research. We achieve a sentiment labeling accuracy of 81.66%, uncovering that 67% of the sentiment towards QRIS is positive, 28.2% negative, and 4.17% neutral. Positive tweets mostly cover six dominant topics with a value of 0.488037, whereas negative sentiments are concentrated around three dominant topics with a   value of 0.383938. These findings not only affirm the generally positive public response towards QRIS but also highlight areas requiring attention for its continued success. Our study translates these insights into actionable recommendations, aiming to provide a multidimensional understanding that stakeholders can leverage for system enhancement. This study serves as a foundation for future works in sentiment analysis and public opinion mining related to financial technologies, particularly in the Indonesian context.

Xianguo Ren, Haiqing Tian, Kai Zhao et al.

pH value is a crucial indicator for evaluating silage quality. In this study, taking maize silage as the research object, a quantitative prediction model of pH value change during the secondary fermentation of maize silage was constructed based on computer vision. Firstly, maize silage samples were collected for image acquisition and pH value determination during intermittent and always-aerobic exposure. Secondly, after preprocessing the acquired image with the region of interest (ROI) interception, smoothing, and sharpening, the color and texture features were extracted. In addition, Pearson correlation analysis and RF importance ranking were used to choose useful feature variables. Finally, based on all feature variables and useful feature variables, four regression models were constructed and compared using random forest regression (RFR) and support vector regression (SVR): RFR model 1, RFR model 2, SVR model 1, and SVR model 2. The results showed that—compared with texture features—the correlation between color features and pH value was higher, which could better reflect the dynamic changes in pH value. All four models were highly predictive. The RFR model represented the quantitative analysis relationship between image information and pH value better than the SVR model. RFR model 2 was efficient and accurate, and was the best model for pH prediction, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>c</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></mrow></semantics></math></inline-formula>, <i>RMSEC</i>, <i>RMSEP</i>, and <i>RPD</i> of 0.9891, 0.9425, 0.1758, 0.3651, and 4.2367, respectively. Overall, this study proved the feasibility of using computer vision technology to quantitatively predict pH value during the secondary fermentation of maize silage and provided new insights for monitoring the quality of maize silage.

Qianqian Qian, Ke Cheng, Wei Qian et al.

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments.

Sabine Vogler, Friederike Windisch

Point-of-care diagnostic tests for community-acquired acute respiratory tract infections (CA-ARTI) can support doctors by improving antibiotic prescribing. However, little is known about health technology assessment (HTA), pricing and funding policies for CA-ARTI diagnostics. Thus, this study investigated these policies for this group of devices applied in the outpatient setting in Europe. Experts from competent authority responded to a questionnaire in Q4/2020. Information is available for 17 countries. Studied countries do not base their pricing and funding decision for CA-ARTI diagnostics on an HTA. While a few countries impose price regulation for some publicly funded medical devices, the prices of CA-ARTI diagnostics are not directly regulated in any of the surveyed countries. Indirect price regulation through public procurement is applied in some countries. Reimbursement lists of medical devices eligible for public funding exist in several European countries, and in some countries these lists include CA-ARTI diagnostics. In a few countries, the public payer funds the health professional for performing the service of conducting the test. Given low levels of regulation and few incentives, the study findings suggest room for strengthening pricing and funding policies of CA-ARTI diagnostics to contribute to increased acceptance and use of these point-of-care tests.

Tiancheng Li, Yan Song, Enbin Song et al.

Finite mixture such as the Gaussian mixture is a flexible and powerful probabilistic modeling tool for representing the multimodal distribution widely involved in many estimation and learning problems. The core of it is representing the target distribution by the arithmetic average (AA) of a finite number of sub-distributions which constitute a mixture. While the mixture has been widely used for single sensor filter design, it is only recent that the AA fusion demonstrates compelling performance for multi-sensor filter design. In this paper, some statistic and information-theoretic results are given on the covariance consistency, mean square error, mode-preservation capacity, and the information divergence of the AA fusion approach. In particular, based on the concept of conservative fusion, the relationship of the AA fusion with the existing conservative fusion approaches such as covariance union and covariance intersection is exposed. A suboptimal weighting approach has been proposed, which jointly with the best mixture-fit property of the AA fusion leads to a max-min optimization problem. Linear Gaussian models are considered for algorithm illustration and simulation comparison, resulting in the first-ever AA fusion-based multi-sensor Kalman filter.

Rajeev Gangwar, Mohit Lal Bera, G. P. Teja et al.

One of the major obstacles faced by quantum-enabled technology is the environmental noise that causes decoherence in the quantum system, thereby destroying much of its quantum aspects and introducing errors while the system undergoes quantum operations and processing. A number of techniques have been invented to mitigate the environmental effects, and many of these techniques are specific to the environment and the quantum tasks at hand. Here, we propose a protocol that makes arbitrary environments effectively noise-free or transparent using an ancilla, which, in particular, is well suited to protect information stored in atoms. The ancilla, which is the photons, is allowed to undergo restricted but a wide class of noisy operations. The protocol transfers the information of the system onto the decoherence-free subspace and later retrieves it back to the system. Consequently, it enables full protection of quantum information and entanglement in the atomic system from decoherence. We propose experimental schemes to implement this protocol on atomic systems in an optical cavity.

Leonardo Christino, Martha D. Ferreira, Fernando V. Paulovich

Knowing how to construct text-based Search Queries (SQs) for use in Search Engines (SEs) such as Google or Wikipedia has become a fundamental skill. Though much data are available through such SEs, most structured datasets live outside their scope. Visualization tools aid in this limitation, but no such tools come close to the sheer amount of information available through general-purpose SEs. To fill this gap, this paper presents Q4EDA, a novel framework that converts users' visual selection queries executed on top of time series visual representations, providing valid and stable SQs to be used in general-purpose SEs and suggestions of related information. The usefulness of Q4EDA is presented and validated by users through an application linking a Gapminder's line-chart replica with a SE populated with Wikipedia documents, showing how Q4EDA supports and enhances exploratory analysis of United Nations world indicators. Despite some limitations, Q4EDA is unique in its proposal and represents a real advance towards providing solutions for querying textual information based on user interactions with visual representations.

Johannes Rude Jensen, Victor von Wachter, Omri Ross

We document an ongoing research process towards the implementation and integration of a digital artefact, executing the lifecycle of a leveraged trade with permissionless blockchain technology. By employing core functions of the 'Dai Stablecoin system' deployed on the Ethereum blockchain, we produce the equivalent exposure of a leveraged position while deterministically automating the monitoring and liquidation processes. We demonstrate the implementation and early integration of the artefact into a hardened exchange environment through a microservice utilizing standardized API calls. The early results presented in this paper were produced in collaboration with a team of stakeholders at a hosting organization, a multi-national online brokerage and cryptocurrency exchange. We utilize the design science research methodology (DSR) guiding the design, development, and evaluation of the artefact. Our findings indicate that, while it is feasible to implement the lifecycle of a leveraged trade on the blockchain, the integration of the artefact into a traditional exchange environment involves multiple compromises and drawback. Generalizing the tentative findings presented in this paper, we introduce three propositions on the implementation, integration, and implications of executing key business processes with permissionless blockchain technologies. By conducting computational design science research, we contribute to the information systems discourse on the applied utility of permissionless blockchain technologies in finance and beyond.

Michal Krelina

Quantum technology is an emergent and potentially disruptive discipline, with the ability to affect many human activities. Quantum technologies are dual-use technologies, and as such are of interest to the defence and security industry and military and governmental actors. This report reviews and maps the possible quantum technology military applications, serving as an entry point for international peace and security assessment, ethics research, military and governmental policy, strategy and decision making. Quantum technologies for military applications introduce new capabilities, improving effectiveness and increasing precision, thus leading to `quantum warfare', wherein new military strategies, doctrines, policies and ethics should be established. This report provides a basic overview of quantum technologies under development, also estimating the expected time scale of delivery or the utilisation impact. Particular military applications of quantum technology are described for various warfare domains (e.g. land, air, space, electronic, cyber and underwater warfare and ISTAR -- intelligence, surveillance, target acquisition and reconnaissance), and related issues and challenges are articulated.

Christy Atika Sari, Eko Hari Rachmawanto

Tanaman merupakan bagian terpenting dalam kehidupan makhluk hidup sebagai oksigen untuk bernafas, selain itu juga digunakan sumber makanan, bahan bakar, obat-obatan dan masih banyak lagi manfaatnya. Salah satunya tanaman buah pepaya, bisa digunakan untuk bahan makanan maupun obat-obatan. Tanaman buah pepaya ini memiliki banyak jenis dan bisa diklasifikasikan berdasarkan bentuk daunnya. Jenis daun buah papaya yang digunakan dalam penelitian ini, yaitu : daun buah pepaya Sumatera, daun buah pepaya California, daun buah pepaya Hawai, daun buah pepaya cibinong dan daun buah pepaya Bangkok. Jumlah dataset yang digunakan adalah 150 citra dan akan dibagi menjadi 5 kelas yang terdiri dari 25 data training dan 5 data testing masing-masing kelas. Proses klasifikasi ini menggunakan metode Local Binary Pattern untuk ektraksi fitur dan metode Naïve Bayes Classifier sebagai metode klasifikasinya. Metode Local Binary Pattern operator sederhana dan efisien untuk menggambarkan pola gambar local dan mendapatkan hasil yang baik dalam tekstur pengambilan gambar. Sedangkan metode Naïve Bayes Classifier adalah metode yang paling sederhana dengan menggunakan peluang yang ada, dimana tempatnya mengasumsikan bahwa setiap variabel adalah independensi. Berdasarkan hasil pengujian yang dilakukan, penggunaan Naïve Bayes Classifier ditambah dengan ekstraksi fitur Local Binary Pattern didapatkan nilai akurasi 96% pada percobaan pertama dan 93% pada percobaan kedua.

Yuxiang Lu, Zhuqing Jia, Syed A. Jafar

Double blind $T$-private information retrieval (DB-TPIR) enables two users, each of whom specifies an index ($θ_1, θ_2$, resp.), to efficiently retrieve a message $W(θ_1,θ_2)$ labeled by the two indices, from a set of $N$ servers that store all messages $W(k_1,k_2), k_1\in\{1,2,\cdots,K_1\}, k_2\in\{1,2,\cdots,K_2\}$, such that the two users' indices are kept private from any set of up to $T_1,T_2$ colluding servers, respectively, as well as from each other. A DB-TPIR scheme based on cross-subspace alignment is proposed in this paper, and shown to be capacity-achieving in the asymptotic setting of large number of messages and bounded latency. The scheme is then extended to $M$-way blind $X$-secure $T$-private information retrieval (MB-XS-TPIR) with multiple ($M$) indices, each belonging to a different user, arbitrary privacy levels for each index ($T_1, T_2,\cdots, T_M$), and arbitrary level of security ($X$) of data storage, so that the message $W(θ_1,θ_2,\cdots, θ_M)$ can be efficiently retrieved while the stored data is held secure against collusion among up to $X$ colluding servers, the $m^{th}$ user's index is private against collusion among up to $T_m$ servers, and each user's index $θ_m$ is private from all other users. The general scheme relies on a tensor-product based extension of cross-subspace alignment and retrieves $1-(X+T_1+\cdots+T_M)/N$ bits of desired message per bit of download.

Chung-Wei Weng, Yauhen Yakimenka, Hsuan-Yin Lin et al.

We propose to extend the concept of private information retrieval by allowing for distortion in the retrieval process and relaxing the perfect privacy requirement at the same time. In particular, we study the trade-off between download rate, distortion, and user privacy leakage, and show that in the limit of large file sizes this trade-off can be captured via a novel information-theoretical formulation for datasets with a known distribution. Moreover, for scenarios where the statistics of the dataset is unknown, we propose a new deep learning framework by leveraging a generative adversarial network approach, which allows the user to learn efficient schemes from the data itself. We evaluate the performance of the scheme on a synthetic Gaussian dataset as well as on the MNIST, CIFAR-10, and LSUN datasets. For the MNIST, CIFAR-10, and LSUN datasets, the data-driven approach significantly outperforms a nonlearning-based scheme which combines source coding with the download of multiple files.

Doyeon Weon, Kyungchun Lee

In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multiple-input-multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to predict the minimum metrics of the &#x201C;deep&#x201D; paths in sub-trees before commencing the tree search in SD. To reduce the complexity of the NN, we employ the input vector with a reduced dimension rather than using the original received signals and full channel matrix. The outputs of the NN, i.e., the predicted minimum path metrics, are exploited to determine the search order between the sub-trees, as well as to optimize the initial search radius, which may reduce the computational complexity of SD. For further complexity reduction, an early termination scheme based on the predicted minimum path metrics is also proposed. Our simulation results show that the proposed DPP-SD scheme provides a significant reduction in computational complexity compared with the conventional SD algorithm, despite achieving near-optimal performance.