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Articles 1 - 9 of 9
Full-Text Articles in Computer Engineering
A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong
A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong
Computer Science Faculty Publications
Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. However, most previous methods treat word embeddings as isolated entities and fail to jointly consider both the intra-space and inter-space topological relations between words. This limitation makes it challenging to align words from embedding spaces with distinct topological structures, especially when the assumption of isomorphism may not hold. To this end, we propose a novel approach called the Structure-Aware Generative Adversarial Network (SA-GAN) model to explicitly capture multiple topological structure information to achieve accurate …
Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu
Spectrum Sensing With Energy Detection In Multiple Alternating Time Slots, Călin Vlădeanu, Alexandru Marţian, Dimitrie C. Popescu
Electrical & Computer Engineering Faculty Publications
Energy detection (ED) represents a low complexity approach used by secondary users (SU) to sense spectrum occupancy by primary users (PU) in cognitive radio (CR) systems. In this paper, we present a new algorithm that senses the spectrum occupancy by performing ED in K consecutive sensing time slots starting from the current slot and continuing by alternating before and after the current slot. We consider a PU traffic model specified in terms of an average duty cycle value, and derive analytical expressions for the false alarm probability (FAP) and correct detection probability (CDP) for any value of K . Our …
Wg2An: Synthetic Wound Image Generation Using Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Ozgur Guler
Wg2An: Synthetic Wound Image Generation Using Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Ozgur Guler
Engineering Technology Faculty Publications
In part due to its ability to mimic any data distribution, Generative Adversarial Network (GAN) algorithms have been successfully applied to many applications, such as data augmentation, text-to-image translation, image-to-image translation, and image inpainting. Learning from data without crafting loss functions for each application provides broader applicability of the GAN algorithm. Medical image synthesis is also another field that the GAN algorithm has great potential to assist clinician training. This paper proposes a synthetic wound image generation model based on GAN architecture to increase the quality of clinical training. The proposed model is trained on chronic wound datasets with various …
Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park
Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park
VMASC Publications
The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, …
Scalable And Fully Distributed Localization In Large-Scale Sensor Networks, Miao Jin, Su Xia, Hongyi Wu, Xianfeng David Gu
Scalable And Fully Distributed Localization In Large-Scale Sensor Networks, Miao Jin, Su Xia, Hongyi Wu, Xianfeng David Gu
Electrical & Computer Engineering Faculty Publications
This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and a non-uniform nodal distribution. In contrast to current state-of-the-art connectivity-based localization methods, the proposed algorithm is highly scalable with linear computation and communication costs with respect to the size of the network; and fully distributed where each node only needs the information of its neighbors without cumbersome partitioning and merging process. The algorithm is theoretically guaranteed and numerically stable. Moreover, the algorithm can be readily extended to the localization of networks with a one-hop transmission range distance measurement, and the propagation of …
Efficient Corona Training Protocols For Sensor Networks, Alan A. Bertossi, Stephan Olariu, Cristina M. Pinotti
Efficient Corona Training Protocols For Sensor Networks, Alan A. Bertossi, Stephan Olariu, Cristina M. Pinotti
Computer Science Faculty Publications
Phenomenal advances in nano-technology and packaging have made it possible to develop miniaturized low-power devices that integrate sensing, special-purpose computing, and wireless communications capabilities. It is expected that these small devices, referred to as sensors, will be mass-produced and deployed, making their production cost negligible. Due to their small form factor and modest non-renewable energy budget, individual sensors are not expected to be GPS-enabled. Moreover, in most applications, exact geographic location is not necessary, and all that the individual sensors need is a coarse-grain location awareness. The task of acquiring such a coarse-grain location awareness is referred to as training. …
A Tabu Search Algorithm To Minimize The Makespan For The Unrelated Parallel Machines Scheduling Problem With Setup Times, Magdy Helal, Ghaith Rabadi, Ameer Al-Salem
A Tabu Search Algorithm To Minimize The Makespan For The Unrelated Parallel Machines Scheduling Problem With Setup Times, Magdy Helal, Ghaith Rabadi, Ameer Al-Salem
Engineering Management & Systems Engineering Faculty Publications
In this paper we propose a tabu search implementation to solve the unrelated parallel machines scheduling problem with sequence- and machine- dependent setup times to minimize the schedules makespan. The problem is NP-hard and finding an optimal solution efficiently is unlikely. Therefore, heuristic techniques are more appropriate to find near-optimal solutions. The proposed tabu search algorithm uses two phases of perturbation schemes: the intra-machine perturbation, which optimizes the sequence of jobs on the machines, and the inter-machine perturbation, which balances the assignment of the jobs to the machines. We compare the proposed algorithm to an existing one that addressed the …
Introduction: Data Communication And Topology Algorithms For Sensor Networks, Stephan Olariu, David Simplot-Ryl, Ivan Stojmenovic
Introduction: Data Communication And Topology Algorithms For Sensor Networks, Stephan Olariu, David Simplot-Ryl, Ivan Stojmenovic
Computer Science Faculty Publications
(First paragraph) We are very proud and honored to have been entrusted to be Guest Editors for this special issue. Papers were sought to comprehensively cover the algorithmic issues in the “hot” area of sensor networking. The concentration was on network layer problems, which can be divided into two groups: data communication problems and topology control problems. We wish to briefly introduce the five papers appearing in this special issue. They cover specific problems such as time division for reduced collision, fault tolerant clustering, self-stabilizing graph optimization algorithms, key pre-distribution for secure communication, and distributed storage based on spanning trees …
Pipelining Data Compression Algorithms, R. L. Bailey, R. Mukkamala
Pipelining Data Compression Algorithms, R. L. Bailey, R. Mukkamala
Computer Science Faculty Publications
Many different data compression techniques currently exist. Each has its own advantages and disadvantages. Combining (pipelining) multiple data compression techniques could achieve better compression rates than is possible with either technique individually. This paper proposes a pipelining technique and investigates the characteristics of two example pipelining algorithms. Their performance is compared with other well-known compression techniques.