Hasil untuk "Electronic computers. Computer science"

BU Yunyang, QI Binting, BU Fanliang

In social media,people’s comments usually describe a certain sentiment region in the corresponding image,and there is correspondence information between image and text.Most previous multimodal sentiment analysis methods only explore the interactions between images and text from a single perspective,capturing the correspondence between image regions and text words,leading to results that are not optimal.In addition,data on social media is strongly personal and subjective,and the sentiment in the data is multidimensional and complex,which leads to the emergence of data with weak image and text sentiment consistency.To address the above two problems,a multimodal sentiment analysis model with interactive fusion of two perspectives under cross-modal inconsistency perception is proposed.On the one hand,cross-modal interaction of graphic and textual features from both global and local perspectives provides a more comprehensive and accurate sentiment analysis,which improves the perfor-mance and application of the model.On the other hand,the inconsistency scores of the graphical features are calculated to representthe degree of graphical inconsistency,as a way to dynamically regulate the weights of the unimodal and multimodal representations in the final sentiment features,thus improving the robustness of the model.Extensive experiments are conducted on two public datasets,MVSA-Single and MVSA-Multiple,and the results demonstrate the validity and superiority of the proposed multimodal sentiment analysis model compared to the existing baseline models,with F1 values increasing by 0.59 persentage points and 0.39 persentage points,respectively.

ZHOU Zheng, WANG Mei, YANG Linyao et al.

Traffic incident detection is a core component of intelligent transportation systems (ITS), but existing methods are limited in processing unstructured social media text, associated geographic information, and collaborative multi-task learning. To address this, a traffic incident detection model based on integrated geographical knowledge enhancement and multi-task learning (GeoKE-MTL) was proposed to improve the accuracy and robustness of incident detection. The model consists of two main components: a knowledge enhancement module and a multi-task learning module. Experimental results show that on a self-built social media text dataset, GeoKE-MTL achieves F1 scores of 79.42% and 79.75% in incident location identification and traffic event identification tasks, respectively, outperforming mainstream baseline models in the incident location identification task. This study validates the effectiveness of integrating geographic knowledge enhancement with multi-task learning in improving detection performance, providing a new solution for real-time event perception in intelligent transportation systems.

I Putu Agus Eka Darma Udayana, Made Sudarma, I Ketut Gede Darma Putra et al.

Electroencephalogram (EEG) is a non-invasive technology that is widely used to record the electrical activity of the brain. However, often the EEG signal is contaminated by noise, including ocular artefacts and muscle activity, which can interfere with accurate analysis and interpretation. This research aims to improve the quality of EEG signals related to concentration by comparing the effectiveness of two denoising methods, namely Independent Component Analysis (ICA) and Principal Component Analysis (PCA). Using commercial EEG headsets, this study recorded Alpha, Beta, Delta, and Theta signals from 20 participants while they performed tasks that required concentration. Evaluation of the effectiveness of the denoising technique is carried out by focusing on changes in standard deviation and calculating the Percentage Residual Difference (PRD) value of the EEG signal before and after denoising. The results show that ICA provides better denoising performance than PCA, as reflected by a significant reduction in standard deviation and a lower PRD value. These results indicate that the ICA method can effectively reduce noise and preserve important information from the original signal.

Lindai Xie, Yingying Jiang, Chi-Ning Chang et al.

This multi-informant mixed-methods study uses a concurrent parallel sampling approach to investigate undergraduate students' and faculty's perceptions of utilizing AI in teaching and learning at U.S. universities. A survey developed based on the Technology Acceptance Model, Social Influence Theory, and existing literature was implemented to collect undergraduate students' data regarding students' perceived AI learning environment, perceived others' attitudes toward AI, and personal attitudes toward AI. Faculty's opinions were collected through semi-structured interviews in accordance with the survey variables. Quantitative findings indicated that the effect of the AI learning environment on students' personal attitudes toward AI was fully mediated by their perceptions of others' attitudes. This finding highlights the critical role of perceived others' attitudes towards AI since students tend to adapt to the AI learning environment by mirroring the attitudes they perceive from others. The qualitative findings explored faculty's use of AI tools, their attitudes toward AI and students' usage, the challenges they experienced, and the need for clear guidance and support to facilitate better incorporation of AI into their professional practices. The integration of quantitative and qualitative phases compares students' and faculty's usage and attitudes toward AI and brings important insights that focus on improving the AI-using environment, ensuring sufficient financial support, and offering professional training for both faculty and students. Based on the findings, students can be guided in developing informed attitudes about AI utilization through faculty's demonstration of appropriate AI usage, fostering meaningful conversations about AI integration, and experiential learning opportunities to practice AI-assisted learning.

Choongseok Song, Doo Seok Jeong

Computing-In-Memory (CIM) offers a potential solution to the memory wall issue and can achieve high energy efficiency by minimizing data movement, making it a promising architecture for edge AI devices. Lightweight models like MobileNet and EfficientNet, which utilize depthwise convolution for feature extraction, have been developed for these devices. However, CIM macros often face challenges in accelerating depthwise convolution, including underutilization of CIM memory and heavy buffer traffic. The latter, in particular, has been overlooked despite its significant impact on latency and energy consumption. To address this, we introduce a novel CIM dataflow that significantly reduces buffer traffic by maximizing data reuse and improving memory utilization during depthwise convolution. The proposed dataflow is grounded in solid theoretical principles, fully demonstrated in this paper. When applied to MobileNet and EfficientNet models, our dataflow reduces buffer traffic by 77.4-87.0%, leading to a total reduction in data traffic energy and latency by 10.1-17.9% and 15.6-27.8%, respectively, compared to the baseline (conventional weight-stationary dataflow).

Daniel Apai, Rory Barnes, Matthew M. Murphy et al.

The search for extraterrestrial life in the Solar System and beyond is a key science driver in astrobiology, planetary science, and astrophysics. A critical step is the identification and characterization of potential habitats, both to guide the search and to interpret its results. However, a well-accepted, self-consistent, flexible, and quantitative terminology and method of assessment of habitability are lacking. Our paper fills this gap based on a three year-long study by the NExSS Quantitative Habitability Science Working Group. We reviewed past studies of habitability, but find that the lack of a universally valid definition of life prohibits a universally applicable definition of habitability. A more nuanced approach is needed. We introduce a quantitative habitability assessment framework (QHF) that enables self-consistent, probabilistic assessment of the compatibility of two models: First, a habitat model, which describes the probability distributions of key conditions in the habitat. Second, a viability model, which describes the probability that a metabolism is viable given a set of environmental conditions. We provide an open-source implementation of this framework and four examples as a proof of concept: (a) Comparison of two exoplanets for observational target prioritization; (b) Interpretation of atmospheric O2 detection in two exoplanets; (c) Subsurface habitability of Mars; and (d) Ocean habitability in Europa. These examples demonstrate that our framework can self-consistently inform astrobiology research over a broad range of questions. The proposed framework is modular so that future work can expand the range and complexity of models available, both for habitats and for metabolisms.

Rebecca M. M. Hicke, Ross Deans Kristensen-McLachlan

Metaphors are everywhere. They appear extensively across all domains of natural language, from the most sophisticated poetry to seemingly dry academic prose. A significant body of research in the cognitive science of language argues for the existence of conceptual metaphors, the systematic structuring of one domain of experience in the language of another. Conceptual metaphors are not simply rhetorical flourishes but are crucial evidence of the role of analogical reasoning in human cognition. In this paper, we ask whether Large Language Models (LLMs) can accurately identify and explain the presence of such conceptual metaphors in natural language data. Using a novel prompting technique based on metaphor annotation guidelines, we demonstrate that LLMs are a promising tool for large-scale computational research on conceptual metaphors. Further, we show that LLMs are able to apply procedural guidelines designed for human annotators, displaying a surprising depth of linguistic knowledge.

Raphaël Millière

Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the centre stage of philosophical debates about cognition. This development is directly relevant to long-standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful.

Amirhossein Azarbahram, O. A. López, R. Souza et al.

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for charging the Internet of Things. Practical RF-WPT systems usually require energy beamforming (EB), which can compensate for the severe propagation loss by directing beams toward the devices. The EB flexibility depends on the transmitter architecture, existing a trade-off between cost/complexity and degrees of freedom. Thus, simpler architectures such as dynamic metasurface antennas (DMAs) are gaining attention. Herein, we consider an RF-WPT system with a transmit DMA for meeting the energy harvesting requirements of multiple devices and formulate an optimization problem for the minimum-power design. First, we provide a mathematical model to capture the frequency-dependant signal propagation effect in the DMA architecture. Next, we propose a solution based on semi-definite programming and alternating optimization. Results show that a DMA-based structure can outperform a fully-digital implementation and that the required transmit power decreases with the antenna array size, while it increases and remains almost constant with frequency in DMA and FD, respectively.

Muhammad Daffa Arviano Putra, Muhammad Darwis, Retno Hendrowati

Forum adalah suatu wadah untuk bertukar informasi dan pemikiran. Dalam era digital, informasi merupakan hal yang paling berharga dan forum menjadi sarana tepat untuk mendapatkan informasi. Akibatnya, forum saat ini juga hadir dalam bentuk aplikasi. Suatu aplikasi forum dapat memberikan banyak manfaat bagi penggunanya. Pada penelitian ini, dirancang aplikasi forum khusus mahasiswa Universitas Paramadina berbentuk mobile. Kehadiran aplikasi forum mahasiswa ini diharapkan meningkatkan kolaborasi dan semangat diskusi di antara mahasiswa, sehingga memperkaya pengalaman belajar mahasiswa di Universitas Paramadina. Proses pengembangan sistem aplikasi forum dilakukan dengan menerapkan konsep perancangan berbasis objek. Konsep ini diterapkan dengan pembuatan model-model diagram Unified Modeling Language (UML). Dengan demikian, rancangan sistem yang dibuat mengikuti struktur yang efisien dan mengikuti standard. Hasil rancangan kemudian diimplementasikan dalam bentuk prototype dengan memanfaatkan framework Flutter dan dilakukan pengujian secara menyeluruh untuk memastikan ketepatan dan kegunaan aplikasi dengan menggunakan metode blackbox dan whitebox. Hasil pengujian tersebut menunjukkan bahwa aplikasi forum tersebut telah berfungsi dengan baik.

Ronnie de Souza Santos, Brody Stuart-Verner, Cleyton de Magalhaes

Modern society is diverse, multicultural, and multifaceted. Because of these characteristics, we are currently observing an increase in the debates about equity, diversity, and inclusion in different areas, especially because several groups of individuals are underrepresented in many environments. In computer science and software engineering, it seems counter-intuitive that these areas, which are responsible for creating technological solutions and systems for billions of users around the world, do not reflect the diversity of the society to which it serves. In trying to solve this diversity crisis in the software industry, researchers started to investigate strategies that can be applied to increase diversity and improve inclusion in academia and the software industry. However, the lack of diversity in computer science and related courses, including software engineering, is still a problem, in particular when some specific groups are considered. LGBTQIA+ students, for instance, face several challenges to fit into technology courses, even though most students in universities right now belong to Generation Z, which is described as open-minded to aspects of gender and sexuality. In this study, we aimed to discuss the state-of-art of publications about the inclusion of LGBTQIA+ students in computer science education. Using a mapping study, we identified eight studies published in the past six years that focused on this public. We present strategies developed to adapt curricula and lectures to be more inclusive to LGBTQIA+ students and discuss challenges and opportunities for future research

Rima Hazra, Mayank Singh, Pawan Goyal et al.

Interdisciplinarity has over the recent years have gained tremendous importance and has become one of the key ways of doing cutting edge research. In this paper we attempt to model the citation flow across three different fields -- Physics (PHY), Mathematics (MA) and Computer Science (CS). For instance, is there a specific pattern in which these fields cite one another? We carry out experiments on a dataset comprising more than 1.2 million articles taken from these three fields. We quantify the citation interactions among these three fields through temporal bucket signatures. We present numerical models based on variants of the recently proposed relay-linking framework to explain the citation dynamics across the three disciplines. These models make a modest attempt to unfold the underlying principles of how citation links could have been formed across the three fields over time.

M. Carroll, S. Rosenblatt, P. Jurcevic et al.

Abstract Superconducting qubits are a leading candidate for quantum computing but display temporal fluctuations in their energy relaxation times T 1. This introduces instabilities in multi-qubit device performance. Furthermore, autocorrelation in these time fluctuations introduces challenges for obtaining representative measures of T 1 for process optimization and device screening. These T 1 fluctuations are often attributed to time varying coupling of the qubit to defects, putative two level systems (TLSs). In this work, we develop a technique to probe the spectral and temporal dynamics of T 1 in single junction transmons by repeated T 1 measurements in the frequency vicinity of the bare qubit transition, via the AC-Stark effect. Across 10 qubits, we observe strong correlations between the mean T 1 averaged over approximately nine months and a snapshot of an equally weighted T 1 average over the Stark shifted frequency range. These observations are suggestive of an ergodic-like spectral diffusion of TLSs dominating T 1, and offer a promising path to more rapid T 1 characterization for device screening and process optimization.

Mohit Mittal, Martyna Kobielnik, Swadha Gupta et al.

Abstract Wireless sensor network (WSN) is widely acceptable communication network where human-intervention is less. Another prominent factors are cheap in cost and covers huge area of field for communication. WSN as name suggests sensor nodes are present which communicate to the neighboring node to form a network. These nodes are communicate via radio signals and equipped with battery which is one of most challenge in these networks. The battery consumption is depend on weather where sensors are deployed, routing protocols etc. To reduce the battery at routing level various quality of services (QoS) parameters are available to measure the performance of the network. To overcome this problem, many routing protocol has been proposed. In this paper, we considered two energy efficient protocols i.e. LEACH and Sub-cluster LEACH protocols. For provision of better performance of network Levenberg-Marquardt neural network (LMNN) and Moth-Flame optimisation both are implemented one by one. QoS parameters considered to measure the performance are energy efficiency, end-to-end delay, Throughput and Packet delivery ratio (PDR). After implementation, simulation results show that Sub-cluster LEACH with MFO is outperforms among other algorithms.Along with this, second part of paper considered to anomaly detection based on machine learning algorithms such as SVM, KNN and LR. NSLKDD dataset is considered and than proposed the anomaly detection method.Simulation results shows that proposed method with SVM provide better results among others.

Ghazaleh Khojasteh Toussi, Mahmoud Naghibzadeh, Saeid Abrishami et al.

Abstract A workflow is an effective way for modeling complex applications and serves as a means for scientists and researchers to better understand the details of applications. Cloud computing enables the running of workflow applications on many types of computational resources which become available on-demand. As one of the most important aspects of cloud computing, workflow scheduling needs to be performed efficiently to optimize resources. Due to the existence of various resource types at different prices, workflow scheduling has evolved into an even more challenging problem on cloud computing. The present paper proposes a workflow scheduling algorithm in the cloud to minimize the execution cost of the deadline-constrained workflow. The proposed method, EDQWS, extends the current authors’ previous study (DQWS) and is a two-step scheduler based on divide and conquer. In the first step, the workflow is divided into sub-workflows by defining, scheduling, and removing a critical path from the workflow, similar to DQWS. The process continues until only chain-structured sub-workflows, called linear graphs, remain. In the second step which is linear graph scheduling, a new merging algorithm is proposed that combines the resulting linear graphs so as to reduce the number of used instances and minimize the overall execution cost. In addition, the current work introduces a scoring function to select the most efficient instances for scheduling the linear graphs. Experiments show that EDQWS outperforms its competitors, both in terms of minimizing the monetary costs of executing scheduled workflows and meeting user-defined deadlines. Furthermore, in more than 50% of the examined workflow samples, EDQWS succeeds in reducing the number of resource instances compared to the previously introduced DQWS method.

Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal et al.

Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. These models combine neural and symbolic components to learn complex patterns and representations from data, using high-level concepts or known constraints. NP techniques can interface with symbolic domain knowledge from scientists, such as prior knowledge and experimental context, to produce interpretable outputs. We identify opportunities and challenges between current NP models and scientific workflows, with real-world examples from behavior analysis in science: to enable the use of NP broadly for workflows across the natural and social sciences.

Ram Sagar

The Aryabhatta Research Institute of Observational Sciences (ARIES), a premier autonomous research institute under the Department of Science and Technology, Government of India has a legacy of about seven decades with contributions made in the field of observational sciences namely atmospheric and astrophysics. The Survey of India used a location at ARIES, determined with an accuracy of better than 10 meters on a world datum through institute participation in a global network of Earth artificial satellites imaging during late 1950. Taking advantage of its high-altitude location, ARIES, for the first time, provided valuable input for climate change studies by long term characterization of physical and chemical properties of aerosols and trace gases in the central Himalayan regions. In astrophysical sciences, the institute has contributed precise and sometime unique observations of the celestial bodies leading to a number of discoveries. With the installation of the 3.6 meter Devasthal optical telescope in the year 2015, India became the only Asian country to join those few nations of the world who are hosting 4 meter class optical telescopes. This telescope, having advantage of geographical location, is well-suited for multi-wavelength observations and for sub-arc-second resolution imaging of the celestial objects including follow-up of the GMRT, AstroSat and gravitational-wave sources.

Diane Mizrachi, A. Salaz, S. Kurbanoglu et al.

This study reports the descriptive and inferential statistical findings of a survey of academic reading format preferences and behaviors of 10,293 tertiary students worldwide. The study hypothesized that country-based differences in schooling systems, socioeconomic development, culture or other factors might have an influence on preferred formats, print or electronic, for academic reading, as well as the learning engagement behaviors of students. The main findings are that country of origin has little to no relationship with or effect on reading format preferences of university students, and that the broad majority of students worldwide prefer to read academic course materials in print. The majority of participants report better focus and retention of information presented in print formats, and more frequently prefer print for longer texts. Additional demographic and post-hoc analysis suggests that format preference has a small relationship with academic rank. The relationship between task demands, format preferences and reading comprehension are discussed. Additional outcomes and implications for the fields of education, psychology, computer science, information science and human-computer interaction are considered.

Soummya Kar, Markus Püschel, José M. F. Moura

This paper focuses on finite-time in-network computation of linear transforms of distributed graph data. Finite-time transform computation problems are of interest in graph-based computing and signal processing applications in which the objective is to compute, by means of distributed iterative methods, various (linear) transforms of the data distributed at the agents or nodes of the graph. While finite-time computation of consensus-type or more generally rank-one transforms have been studied, systematic approaches toward scalable computing of general linear transforms, specifically in the case of heterogeneous agent objectives in which each agent is interested in obtaining a different linear combination of the network data, are relatively less explored. In this paper, by employing ideas from algebraic geometry, we develop a systematic characterization of linear transforms that are amenable to distributed in-network computation in finite-time using linear iterations. Further, we consider the general case of directed inter-agent communication graphs. Specifically, it is shown that \emph{almost all} linear transformations of data distributed on the nodes of a digraph containing a Hamiltonian cycle may be computed using at most $N$ linear distributed iterations. Finally, by studying an associated matrix factorization based reformulation of the transform computation problem, we obtain, as a by-product, certain results and characterizations on sparsity-constrained matrix factorization that are of independent interest.

Negar Rostamzadeh, Emily Denton, Linda Petrini

This paper offers a retrospective of what we learnt from organizing the workshop *Ethical Considerations in Creative applications of Computer Vision* at CVPR 2021 conference and, prior to that, a series of workshops on *Computer Vision for Fashion, Art and Design* at ECCV 2018, ICCV 2019, and CVPR 2020. We hope this reflection will bring artists and machine learning researchers into conversation around the ethical and social dimensions of creative applications of computer vision.