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2nd IEEE Conference on Information Technology and Data Science, CITDS 2022

作者： Tashu, Tsegaye Misikir Fattouh, Sara Kiss, Peter Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (Camem-BERT and FlauBERT) and visual data (SE-ResNeXt-50), using simple fusion techniques. The proposed method significantly outperformed the performance of the unimodal models, as well as the reported performance of similar models on our specific task. We made experiments with multiple fusing techniques, and found, that the best preforming technique to combine the individual embedding of the unimodal network is based on the combination of concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems. © 2022 IEEE.

关键词： Neural network models

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An equivalent reformulation and multi-proximity gradient algorithms for a class of nonsmooth fractional programming

arXiv

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arXiv 2023年

作者： Zhou, Junpeng Zhang, Na Li, Qia School of Computer Sciences and Engineering Sun Yat-Sen University Guangzhou510006 China Department of Applied Mathematics College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Computer Sciences and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Guangzhou510006 China

In this paper, we consider a class of structured fractional programs, where the numerator part is the sum of a block-separable (possibly nonsmooth nonconvex) function and a locally Lipschitz differentiable (possibly nonconvex) function, while the denominator is a convex (possibly nonsmooth) function. We first present a novel reformulation for the original problem and show the relationship between optimal solutions, critical points and KL exponents of these two problems. Inspired by the reformulation, we propose a flexible framework of multi-proximity gradient algorithms (MPGA), which computes the proximity operator with respect to the Fenchel conjugate associated with the convex denominator of the original problem rather than evaluating its subgradient as in the existing methods. Also, MPGA employs a nonmonotone linear-search scheme in its gradient descent step, since the smooth part in the numerator of the original problem is not globally Lipschitz differentiable. Based on the framework of MPGA, we develop two specific algorithms, namely, cyclic MPGA and randomized MPGA, and establish their subsequential convergence under mild conditions. Moreover, the sequential convergence of cyclic MPGA with the monotone line-search (CMPGA ML) is guaranteed if the extended objective associated with the reformulated problem satisfies the Kurdyka-Lojasiewicz (KL) property and some other mild assumptions. In particular, we prove that the corresponding KL exponents are 12 for several special cases of the fractional programs, and so, CMPGA ML exhibits a linear convergence rate. Finally, some preliminary numerical experiments are performed to demonstrate the efficiency of our proposed algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Gradient methods

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Implementation of Artificial Ant Colony Algorithm (AACA) in dynamic labyrinth generation for android-based 2D games

Implementation of Artificial Ant Colony Algorithm (AACA) in ...

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2019 International Conference on Science Education and Technology, ICOSETH 2019

作者： Herumurti, Darlis Kuswardayan, Imam Khotimah, Wijayanti Nurul Yunanto, Andhik Ampuh Yusuf, Andi Akram Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Indonesia

Playing games can make people feel fun. Players can be more fun if you have a lot of variations, for example, variations of items, maps, or enemies. Map variations can be fulfilled by implementing a dynamic labyrinth generator. In this research, the game has a feature called Dynamic labyrinth generation using the Artificial Ant Colony Algorithm (AACA) method. For deployment, this game is implemented on the Android mobile platform. The evaluation is conducted by testing functionality in system games. The result indicates AACA can generate dynamic mazes and with optimal time with less than five seconds. So, the conclusion from this research that the AACA method can generate dynamic labyrinth quickly and well for Android Game. © Published under licence by IOP Publishing Ltd.

关键词： Android (operating system)

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Wheelchair Wheel Design with Transformable “Wheel-Leg” Configuration for Climbing Stairs and Various Surfaces

Wheelchair Wheel Design with Transformable “Wheel-Leg” Con...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Islam Magomedov Timur Aygumov Anzor A. Kanametov Bissnes informatics Kadyrov Chechen state university Grozny Russia Department of software engineering computer technology and automated systems Dagestan State Technical University Dagestan Russia Department of Electronics and Digital Information Technologies Kabardino-Balkarian State University Nalchik Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The dynamic and uneven terrain of our environment introduces complexity for locomotion vehicles to navigate easily across different settings. Wheelchairs belong to the category of vehicles that require locomotion across various uneven terrains, such as stairs and other partitions. Therefore, the aim of this work is to design a wheel that can travel through various environments without using complex mechanisms. This aim was achieved by creating a novel transformable wheel. This wheel can turn into a legged wheel without using a complex mechanism and, when unnecessary, transform back into a normal wheel. Several simulations were carried out to understand the design's capability to overcome obstacles. In this case, the stair was the obstacle, and the results illustrated that the wheel is capable of climbing stairs without serious issues. Different parameters, such as speed and force, were examined with various values. The results showed that the weight on the wheelchair plays a significant role in climbing. For instance, with lighter weight, the structure moves without major slipping. The roughness of the surface is also related to the wheelchair's ability to reach the top of the stairs. A finer depth of cuts on the surface of the stairs increases the chance of the legged wheel firmly gripping and moving up the stairs.

关键词： Shape Navigation Wheelchairs Wheels Rotors Transforms Stairs Surface roughness Rough surfaces Vehicle dynamics

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Successful Simple Side Channel Analysis: Vulnerability of an atomic pattern $kP$ algorithm implemented with a constant time crypto library to simple electromagnetic analysis attacks

Successful Simple Side Channel Analysis: Vulnerability of an...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Alkistis Aikaterini Sigourou Ievgen Kabin Peter Langendörfer Nicolas Sklavos Zoya Dyka IHP — Leibniz-Institut für Innovative Mikroelektronik Frankfurt (Oder) Germany Computer Engineering and Informatics Dept. SCYTALE Group University of Patras Hellas BTU Cottbus-Senftenberg Cottbus Germany

Elliptic curve cryptosystem is a widely-used public key cryptosystem that is vulnerable to physical attacks, especially to Side Channel Analysis (SCA) attacks. This paper investigates a version of the atomic patterns $\boldsymbol{kP}$ algorithm for elliptic curves over prime finite fields. The atomic patterns for Elliptic Curve (EC) point doublings and additions are well-known countermeasures against simple SCA attacks. We implemented a binary left-to-right $\boldsymbol{kP}$ algorithm corresponding to the atomic patterns as proposed by Rondepierre and using the FLECC open-source cryptographic library. The library provides constant-time functions for the implementation of different mathematic operations in prime finite fields, making it suitable for SCA-aware implementations of cryptographic protocols, at a theoretical level. We were able to reveal the scalar k processed during a $\boldsymbol{kP}$ execution analyzing the measured electromagnetic trace of the $\boldsymbol{kP}$ execution. We determined at least two parts in the measured trace where the distinction between EC point doublings and EC point additions is good observable.

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Adrias: Interference-Aware Memory Orchestration for Disaggregated Cloud Infrastructures

Adrias: Interference-Aware Memory Orchestration for Disaggre...

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IEEE Symposium on High-Performance computer Architecture

作者： Dimosthenis Masouros Christian Pinto Michele Gazzetti Sotirios Xydis Dimitrios Soudris Microprocessors and Digital Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Greece IBM Research Europe Dublin Ireland Department of Informatics and Telematics Harokopio University of Athens Greece

Workload co-location has become the de-facto approach for hosting applications in Cloud environments, leading, however, to interference and fragmentation in shared resources of the system. To this end, hardware disaggregation is introduced as a novel paradigm, that allows fine-grained tailoring of cloud resources to the characteristics of the deployed applications. Towards the realization of hardware disaggregated clouds, novel orchestration frameworks must provide additional knobs to manage the increased scheduling *** present Adrias, a memory orchestration framework for disaggregated cloud systems. Adrias exploits information from low-level performance events and applies deep learning techniques to effectively predict the system state and performance of arriving workloads on memory disaggregated systems, thus, driving cognitive scheduling between local and remote memory allocation modes. We evaluate Adrias on a state-of-art disaggregated testbed and show that it achieves 0.99 and 0.942 R 2 score for system state and application’s performance prediction on average respectively. Moreover, Adrias manages to effectively utilize disaggregated memory, by offloading almost 1/3 of deployed applications with less than 15% performance overhead compared to a conventional local memory scheduling, while clearly outperforms naive scheduling approaches (random and round-robin), by providing up to ×2 better performance.

关键词： Deep learning Memory management Interference Hardware Resource management

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Communicative Robot Signals: Presenting a New Typology for Human-Robot Interaction

arXiv

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arXiv 2023年

作者： Holthaus, Patrick Schulz, Trenton Lakatos, Gabriella Soma, Rebekka University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom Norwegian Computing Center Department of Applied Research in ICT Oslo Norway University of Oslo Department of Informatics Oslo Norway

We present a new typology for classifying signals from robots when they communicate with humans. For inspiration, we use ethology, the study of animal behaviour and previous efforts from literature as guides in defining the typology. The typology is based on communicative signals that consist of five properties: the origin where the signal comes from, the deliberateness of the signal, the signal’s reference, the genuineness of the signal, and its clarity (i.e., how implicit or explicit it is). Using the accompanying worksheet, the typology is straightforward to use to examine communicative signals from previous human-robot interactions and provides guidance for designers to use the typology when designing new robot behaviours. © 2023, CC BY.

关键词： Human robot interaction

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Image Retargeting Techniques Identification Using Supervised Deep Learning

Image Retargeting Techniques Identification Using Supervised...

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Intelligent Data Science Technologies and Applications (IDSTA), International Conference on

作者： Ruba Kharsa Rouaa Alzoubi Mohammad Alsmirat Mahmoud Al-Ayyoub Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajaman UAE

Image retargeting involves the adjustment of an image's dimensions to ensure that its content and visual quality are preserved when the image is resized to fit various screens or devices. This process retains all essential visual elements and details. Different techniques have been developed for this purpose, including cropping (CR), scaling (SCL), seam carving (SC), warping (WARP), scale-and-stretch (SNS), multi-operator (MULTI), and shift-map (SM). However, determining the most suitable method for retargeting a specific image with particular dimensions remains a challenge. Therefore, this research introduces initial work on developing CNN based deep learning model and a transfer learning model based on InceptionV3,to predict the optimal retargeting method for a given image and resolution. The study employed a dataset consisting of 46,716 images with varying resolutions, created using different retargeting techniques, categorized into six groups. Results demonstrates a promising effectiveness of the proposed approach for selecting the appropriate retargeting techniques.

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Villages Status Classification Analysis Involving K-Means Algorithm to Support Kementerian Desa Pembangunan Daerah Tertinggal dan Transmigrasi Work Programs 1

Villages Status Classification Analysis Involving K-Means Al...

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1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Hasugian, Paska Marto Hutahaean, Harvei Desmon Sinaga, Bosker Sriadhi Silaban, Saranom Informatics Engineering Study Program STMIK Pelita Nusantara Computer Science and Informatics Education Study Program Faculty of Engineering Universitas Negeri Medan Medan20221 Indonesia

Data mining is a technique of extracting information that has not been known before in a collection of data in the database. Data mining has been applied in various fields that require extracting information, some of the work that can be generated with data mining is classification, prediction, and data grouping. In this study, an analysis of village data collection was carried out to explore the potential or knowledge of the data that has been presented with the main objective of producing a grouping of village status. To support clustering or grouping activities the K-means algorithm used with the general process is to carry out the modeling process without supervision and is one of the methods for grouping data with a system partition, with the principle of allocating each data to the centroid or the closest average, work steps conducted in support of this research is to collect data related to the analysis of data grouping and proceed with the calculation process in accordance with the work steps of the K-means algorithm, the amount of data used as a test of 303 villages scattered in the old Padang regency. The results of calculations by displaying a new group of Cluster 0 is occupied by 120 villages, cluster 1 with a total data of 123 villages, Cluster 2 with a total of 6 villages, cluster 3 with a total of 33 villages while cluster 4 with a total of 21. © 2020 Published under licence by IOP Publishing Ltd.

关键词： K-means clustering

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Neuromorphic Camera Assisted High-Flow Imaging Cytometry for Particle Classification

Neuromorphic Camera Assisted High-Flow Imaging Cytometry for...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Ioannis Tsilikas Stavros Deligiannidis Aris Tsirigotis Georgios N. Tsigaridas Adonis Bogris Charis Mesaritakis Dept. Physics National Technical University of Athens Zografou Athens Greece Dept. Information and Communication Systems Engineering University of the Aegean Karlovasi Samos Greece Dept. Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9798350345995

In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and real-time classification of test particles, moving at a speed of 0.8m/sec with an accuracy of 97.6%. The key advantage of the utilized microscopy system, is the use of an event-based camera, generating spiking events, triggered by pixel's contrast changes. This bio-inspired operation, contrary to conventional CMOS cameras [1], alleviates bandwidth constraints and can significantly boost frame-rate capabilities, thus capturing high speed events. Following this paradigm, medical imaging modalities, where the detection and analysis of fast-moving particles is a necessity, such as high-flow cytometry, can greatly proliferate from the proposed approach.

关键词： Biomedical imaging Cameras CMOS cameras Cytometry Machine learning Real time imaging

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