Hasil untuk "Electronic computers. Computer science"

Nayeli A. Rodríguez-Briones, Daniel K. Park

This work introduces an approach rooted in quantum thermodynamics to enhance sampling efficiency in quantum machine learning (QML). We propose conceptualizing quantum supervised learning as a thermodynamic cooling process. Building on this concept, we develop a quantum refrigerator protocol that enhances sample efficiency during training and prediction without the need for Grover iterations or quantum phase estimation. Inspired by heat-bath algorithmic cooling protocols, our method alternates entropy compression and thermalization steps to decrease the entropy of qubits, increasing polarization toward the dominant bias. This technique minimizes the computational overhead associated with estimating classification scores and gradients, presenting a practical and efficient solution for QML algorithms compatible with noisy intermediate-scale quantum devices.

YAN Yan, LIU Kun, ZHANG Yanli et al.

Location information was recognized as a critical personal data asset in the digital age, offering convenient services while simultaneously posing significant risks of privacy breaches. Local differential privacy models, which do not rely on trusted third parties, had garnered widespread attention. However, significant challenges were identified in existing location protection methods, including the difficulty of adapting spatial partitioning to complex location distributions, along with high communication and computational overhead that limited system efficiency. To address these challenges, a dynamic local differential privacy location protection method based on a critical path was proposed. A spatial index adapted to user distribution density was constructed through non-uniform quadtree spatial partitioning and Hilbert curve traversal, which effectively improved data usability. Subsequently, the proposed critical path encoding mechanism was executed on the server side to compress the complex partition structure into concise path information, thereby reducing communication overhead during parameter transmission. On the user side, the Hilbert index encoding of the user’s region was perturbed using a randomized response mechanism under the local differential privacy model to protect the privacy of the original location. On the server side, the collected perturbed location encodings from users were aggregated and analyzed. Based on the spatiotemporal continuity of location distribution, the proposed spatial partition structure dynamic adjustment strategy was then implemented to efficiently adapt to dynamic changes in user distribution at an extremely low computational cost. Experiments conducted on real-world location datasets demonstrate that the proposed method provides improved location data availability and algorithm runtime efficiency while achieving local differential privacy protection for user locations.

Yankun Wu, Yuta Nakashima, Noa Garcia

Social biases in generative models have gained increasing attention. This paper proposes an automatic evaluation protocol for text-to-image generation, examining how gender bias originates and perpetuates in the generation process of Stable Diffusion. Using triplet prompts that vary by gender indicators, we trace presentations at several stages of the generation process and explore dependencies between prompts and images. Our findings reveal the bias persists throughout all internal stages of the generating process and manifests in the entire images. For instance, differences in object presence, such as different instruments and outfit preferences, are observed across genders and extend to overall image layouts. Moreover, our experiments demonstrate that neutral prompts tend to produce images more closely aligned with those from masculine prompts than with their female counterparts. We also investigate prompt-image dependencies to further understand how bias is embedded in the generated content. Finally, we offer recommendations for developers and users to mitigate this effect in text-to-image generation.

Nathan Tibbetts, Sifat Ibtisum, Satish Puri

The emergence of new, off-path smart network cards (SmartNICs), known generally as Data Processing Units (DPU), has opened a wide range of research opportunities. Of particular interest is the use of these and related devices in tandem with their host's CPU, creating a heterogeneous computing system with new properties and strengths to be explored, capable of accelerating a wide variety of workloads. This survey begins by providing the motivation and relevant background information for this new field, including its origins, a few current hardware offerings, major programming languages and frameworks for using them, and associated challenges. We then review and categorize a number of recent works in the field, covering a wide variety of studies, benchmarks, and application areas, such as data center infrastructure, commercial uses, and AI and ML acceleration. We conclude with a few observations.

Briana Lee, Samantha Limon, Alyssia Chen et al.

The technology industry offers exciting and diverse career opportunities, ranging from traditional software development to emerging fields such as artificial intelligence, cybersecurity, and data science. Career fairs play a crucial role in helping Computer Science (CS) students understand the various career pathways available to them in the industry. However, limited research exists on how CS students experience and benefit from these events. Through a survey of 86 students, we investigate their motivations for attending, preparation strategies, and learning outcomes, including exposure to new career paths and technologies. We envision our findings providing valuable insights for career services professionals, educators, and industry leaders in improving the career development processes of CS students.

Awang Herjunie Nurdy, Abdul Rahim, Arbansyah

Perkembangan teknologi yang pesat mempermudah akses ke berbagai hiburan digital, termasuk game online seperti Stumble Guys, yang telah diunduh lebih dari 163 juta kali dan mendapatkan ulasan beragam di Google Play Store. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna Stumble Guys menggunakan algoritma Naïve Bayes. Metode penelitian melibatkan tahapan Knowledge Discovery in Databases (KDD), meliputi pemilihan data, preprocessing, transformasi dengan CountVectorizer dan TF-IDF, serta pengklasifikasian dengan Naïve Bayes. Dengan menggunakan 1.500 ulasan dari Google Play Store, model Naïve Bayes mencapai akurasi 86%, dengan precision, recall, dan f1 score masing-masing sebesar 86%. Hasil penelitian menunjukkan bahwa Naïve Bayes efektif dalam mengklasifikasikan sentimen ulasan game Stumble Guys.

Loredana Caruccio, Stefano Cirillo, Giuseppe Polese et al.

In the last year, Large Language Models (LLMs) have transformed the way of tackling problems, opening up new perspectives in various works and research fields, due to their ability to generate and understand human languages. In this regard, the recent release of Claude 2.0 has contributed to the processing of more complex prompts. In this scenario, the goal of this paper is to evaluate the effectiveness of Claude 2.0 in a specific classification task. In particular, we considered the Forest cover-type problem, concerning the prediction of a cover-type value according to the geospatial characterization of target worldwide areas. To this end, we propose a novel iterative prompt template engineering approach, which integrates files by exploiting prompts and evaluates the quality of responses provided by the LLM. Moreover, we conducted several comparative analyses to evaluate the effectiveness of Claude 2.0 with respect to online and batch learning models. The results demonstrated that, although some online and batch models performed better than Claude 2.0, the new iterative prompt engineering approach improved the quality of responses, leading to better performance with increases ranging from 14% to 32% in terms of accuracy, precision, recall, and F1-score.

Wilmer Contreras Sepúlveda, Ángel David Torres-Palencia, José Javier Sánchez Mondragón et al.

Quantum computing is a rapidly emerging and promising field that has the potential to revolutionize numerous research domains, including drug design, network technologies and sustainable energy. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries allow for quantum simulations on classical computers and facilitate program execution on corresponding quantum hardware, e.g., Qiskit programs on IBM quantum computers. While all platforms have some differences, the main concepts are the same. QInterpreter is a tool embedded in the Quantum Science Gateway QubitHub using Jupyter Notebooks that translates seamlessly programs from one library to the other and visualizes the results. It combines the five well-known quantum libraries: into a unified framework. Designed as an educational tool for beginners, Qinterpreter enables the development and execution of quantum circuits across various platforms in a straightforward way. The work highlights the versatility and accessibility of Qinterpreter in quantum programming and underscores our ultimate goal of pervading Quantum Computing through younger, less specialized, and diverse cultural and national communities.

Michał J. Gajda

Philosophy of science attempts to describe all parts of the scientific process in a general way in order to facilitate the description, execution and improvements of this process. So far, all proposed philosophies have only covered existing processes and disciplines partially and imperfectly. In particular logical approaches have always received a lot of attention due to attempts to fundamentally address issues with the definition of science as a discipline with reductionist theories. We propose a new way to approach the problem from the perspective of computational complexity and argue why this approach may be better than previous propositions based on pure logic and mathematics.

Will Barnett, Ana Cavalcanti, Alvaro Miyazawa

The need for robotic systems to be verified grows as robots are increasingly used in complex applications with safety implications. Model-driven engineering and domain-specific languages (DSLs) have proven useful in the development of complex systems. RoboChart is a DSL for modelling robot software controllers using state machines and a simple component model. It is distinctive in that it has a formal semantics and support for automated verification. Our work enriches RoboChart with support for modelling architectures and architectural patterns used in the robotics domain. Support is in the shape of an additional DSL, RoboArch, whose primitive concepts encapsulate the notion of a layered architecture and architectural patterns for use in the design of the layers that are only informally described in the literature. A RoboArch model can be used to generate automatically a sketch of a RoboChart model, and the rules for automatic generation define a semantics for RoboArch. Additional patterns can be formalised by extending RoboArch. In this paper, we present RoboArch, and give a perspective of how it can be used in conjunction with CorteX, a software framework developed for the nuclear industry.

Eduardo Benítez Sandoval, Ricardo Sosa, Ricardo Sosa et al.

Creativity in social robots requires further attention in the interdisciplinary field of human–robot interaction (HRI). This study investigates the hypothesized connection between the perceived creative agency and the animacy of social robots. The goal of this work is to assess the relevance of robot movements in the attribution of creativity to robots. The results of this work inform the design of future human–robot creative interactions (HRCI). The study uses a storytelling game based on visual imagery inspired by the game “Story Cubes” to explore the perceived creative agency of social robots. This game is used to tell a classic story for children with an alternative ending. A 2 × 2 experiment was designed to compare two conditions: the robot telling the original version of the story and the robot plot twisting the end of the story. A Robotis Mini humanoid robot was used for the experiment, and we adapted the Short Scale of Creative Self (SSCS) to measure perceived creative agency in robots. We also used the Godspeed scale to explore different attributes of social robots in this setting. We did not obtain significant main effects of the robot movements or the story in the participants’ scores. However, we identified significant main effects of the robot movements in features of animacy, likeability, and perceived safety. This initial work encourages further studies experimenting with different robot embodiment and movements to evaluate the perceived creative agency in robots and inform the design of future robots that participate in creative interactions.

Ming Yan, Jianxi Yang, Cen Chen et al.

Abstract Deep neural networks have achieved great success in both computer vision and natural language processing tasks. How to improve the gradient flows is crucial in training very deep neural networks. To address this challenge, a gradient enhancement approach is proposed through constructing the short circuit neural connections. The proposed short circuit is a unidirectional neural connection that back propagates the sensitivities rather than gradients in neural networks from the deep layers to the shallow layers. Moreover, the short circuit is further formulated as a gradient truncation operation in its connecting layers, which can be plugged into the backbone models without introducing extra training parameters. Extensive experiments demonstrate that the deep neural networks, with the help of short circuit connection, gain a large margin of improvement over the baselines on both computer vision and natural language processing tasks. The work provides the promising solution to the low‐resource scenarios, such as, intelligence transport systems of computer vision, question answering of natural language processing.

Matthew Kopec, Meica Magnani, Vance Ricks et al.

Embedding ethics modules within computer science courses has become a popular response to the growing recognition that CS programs need to better equip their students to navigate the ethical dimensions of computing technologies like AI, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this empirical study reports positive results from Northeastern's program that embeds values analysis modules into CS courses. The resulting data suggest that such modules have a positive effect on students' moral attitudes and that students leave the modules believing they are more prepared to navigate the ethical dimensions they will likely face in their eventual careers. Importantly, these gains were accomplished at an institution without a philosophy doctoral program, suggesting this strategy can be effectively employed by a wider range of institutions than many have thought.

Malika Nisal Ratnayake, Don Chathurika Amarathunga, Asaduz Zaman et al.

Insects are the most important global pollinator of crops and play a key role in maintaining the sustainability of natural ecosystems. Insect pollination monitoring and management are therefore essential for improving crop production and food security. Computer vision facilitated pollinator monitoring can intensify data collection over what is feasible using manual approaches. The new data it generates may provide a detailed understanding of insect distributions and facilitate fine-grained analysis sufficient to predict their pollination efficacy and underpin precision pollination. Current computer vision facilitated insect tracking in complex outdoor environments is restricted in spatial coverage and often constrained to a single insect species. This limits its relevance to agriculture. Therefore, in this article we introduce a novel system to facilitate markerless data capture for insect counting, insect motion tracking, behaviour analysis and pollination prediction across large agricultural areas. Our system is comprised of edge computing multi-point video recording, offline automated multispecies insect counting, tracking and behavioural analysis. We implement and test our system on a commercial berry farm to demonstrate its capabilities. Our system successfully tracked four insect varieties, at nine monitoring stations within polytunnels, obtaining an F-score above 0.8 for each variety. The system enabled calculation of key metrics to assess the relative pollination impact of each insect variety. With this technological advancement, detailed, ongoing data collection for precision pollination becomes achievable. This is important to inform growers and apiarists managing crop pollination, as it allows data-driven decisions to be made to improve food production and food security.

Jennifer Head, Dianne P. O'Leary

In June 1965, Sister Mary Kenneth Keller, BVM, received the first US PhD in Computer Science, and this paper outlines her life and accomplishments. As a scholar, she has the distinction of being an early advocate of learning-by-example in artificial intelligence. Her main scholarly contribution was in shaping computer science education in high schools and small colleges. She was an evangelist for viewing the computer as a symbol manipulator, for providing computer literacy to everyone, and for the use of computers in service to humanity. She was far ahead of her time in working to ensure a place for women in technology and in eliminating barriers preventing their participation, such as poor access to education and daycare. She was a strong and spirited woman, a visionary in seeing how computers would revolutionize our lives. A condensation of this paper appeared as, ``The Legacy of Mary Kenneth Keller, First U.S. Ph.D. in Computer Science," Jennifer Head and Dianne P. O'Leary, IEEE Annals of the History of Computing 45(1):55--63, January-March 2023.

Yucheng He, Zhen Deng, Jianwei Zhang

Abstract In traditional nasal surgery, surgeons are prone to fatigue and jitter by holding the endoscope for a long‐time. Some complex operations require assistant surgeon to assist with holding the endoscope. To address the above problems, the authors design a remote centre of motion based nasal robot, and propose a voice‐based robot control method. First, through the operation space analysis of nasal surgery, the design scheme of the robot based on RCM mechanism is proposed. On this basis, the design parameters of the robot are analysed to complete the entire design of robot. Then, considering that the surgeon's hands are occupied by surgical instruments during complex surgical operations, a voice‐based robot control method is proposed. This method obtains direction instructions from surgeons by analysing the movement of the endoscopic image. Afterward, a commercial speech recognition interface is used to realise the offline grammar controlwords lib compatible with both Chinese and English, and the overall strategy of robot control is proposed. Finally, an experimental platform for virtual robot control is established, and the voice‐based robot control experiment is performed. The results show that the proposed voice‐based control method is feasible, and it provides guidance for the subsequent development and control of the actual robot system.

Sharaf Alhomdy, Fursan Thabit, Fua'ad Hasan Abdulrazzak et al.

The contagion of the Coronavirus (COVID-19) led to a global lockdown that put governments in emergency mode. With the total number of positive cases worldwide exceeding the 97.46 million mark, social distancing appears to be the only effective strategy to contain the virus at the moment. As a result, companies face obstacles and find it difficult to respond to this current challenge of remote working. The impact of the novel COVID-19 has created many new challenges, and many of us have had to adopt new ways of working. With the need for accessing to critical applications and the scalability of the infrastructure, cloud computing is emerging as an underlying technology. The cloud technology had a major role in fighting the epidemic; it becomes a salvation for governments and organizations in numerous fields of life, education, health, industry, communication, remote surveillance, and more information. Therefore, this study presents the benefits, characteristics and applications of cloud computing and explains how the cloud contributes to improving life in all regions of the world during COVID-19. It shows that the cloud computing helps countries in combating COVID 19, in education and health sectors, also in the economic and commercial aspects. It investigates the current state by distributing an online questionnaire to various people of academic and non-academic backgrounds in different places over the world in the ICT and education sectors. The results showed that there is an effective role for cloud computing during the epidemic.

Miao Teng

In this paper, we conduct an in-depth study of Japanese keyword extraction from news reports, train external computer document word sets from text preprocessing into word vectors using the Ship-gram model in the deep learning tool Word2Vec, and calculate the cosine distance between word vectors. In this paper, the sliding window in TextRank is designed to connect internal document information to improve the in-text semantic coherence. The main idea is to use not only the statistical and structural features of words but also the semantic features of words extracted through word-embedding techniques, i.e., multifeature fusion, to obtain the importance weights of words themselves and the attraction weights between words and then iteratively calculate the final weight of each word through the graph model algorithm to determine the extracted keywords. To verify the performance of the algorithm, extensive simulation experimental studies were conducted on three different types of datasets. The experimental results show that the proposed keyword extraction algorithm can improve the performance by a maximum of 6.45% and 20.36% compared with the existing word frequency statistics and graph model methods, respectively; MF-Rank can achieve a maximum performance improvement of 1.76% compared with PW-TF.

Marc Cheong, Kobi Leins, Simon Coghlan

Those working on policy, digital ethics and governance often refer to issues in `computer science', that includes, but is not limited to, common subfields of Artificial Intelligence (AI), Computer Science (CS) Computer Security (InfoSec), Computer Vision (CV), Human Computer Interaction (HCI), Information Systems, (IS), Machine Learning (ML), Natural Language Processing (NLP) and Systems Architecture. Within this framework, this paper is a preliminary exploration of two hypotheses, namely 1) Each community has differing inclusion of minoritised groups (using women as our test case); and 2) Even where women exist in a community, they are not published representatively. Using data from 20,000 research records, totalling 503,318 names, preliminary data supported our hypothesis. We argue that ACM has an ethical duty of care to its community to increase these ratios, and to hold individual computing communities to account in order to do so, by providing incentives and a regular reporting system, in order to uphold its own Code.

Bruno Buchberger

In this note, I develop my personal view on the scope and relevance of symbolic computation in software science. For this, I discuss the interaction and differences between symbolic computation, software science, automatic programming, mathematical knowledge management, artificial intelligence, algorithmic intelligence, numerical computation, and machine learning. In the discussion of these notions, I allow myself to refer also to papers (1982, 1985, 2001, 2003, 2013) of mine in which I expressed my views on these areas at early stages of some of these fields.