Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspectral Imaging With Random Printed Mask

Hyperspectral images can provide rich clues for various computer vision tasks. However, the requirements of professional and expensive hardware for capturing hyperspectral images impede its wide applications. In this paper, based on a simple but not widely noticed phenomenon that the color printer can print color masks with a large number of independent spectral transmission responses, we propose a simple and low-budget scheme to capture the hyperspectral images with a random mask printed by the consumer-level color printer. Specifically, we notice that the printed dots with different colors are stacked together, forming multiplicative, instead of additive, spectral transmission responses. Therefore, new spectral transmission response uncorrelated with that of the original printer dyes are generated. With the random printed color mask, hyperspectral images could be captured in a snapshot way. A convolutional neural network (CNN) based method is developed to reconstruct the hyperspectral images from the captured image. The effectiveness and accuracy of the proposed system are verified on both synthetic and real captured images.
The network maintenance problem

In this research, we describe an optimization problem motivated by the need to maintain infrastructure net-works over time. We consider infrastructure networks in which product is transported between distinct origin-destination pairs, and at the same time the infrastructure assets need to be maintained by resources moving in the network. In order to perform maintenance the assets have to be shut down from time to time thus reduc-ing the system capacity for those time periods. The objective is to maximize the total transported product by aligning the maintenance activities appropriately. This problem combines flow maximization with maintenance scheduling capturing some important aspects of the motivating practical problem: (1) the interaction between utilization of network assets such as nodes and arcs and their maintenance demands, (2) the limited resources available to perform the maintenance, and (3) the time for moving the maintenance resources between different locations in the network. Depending on the application context, there are a number of natural ways to reflect these in a mathematical model, and this gives rise to a rich and challenging optimization problem which we call the network maintenance problem. We formally introduce the problem, and present a mixed integer programming formulation. Next, we consider the case of a single commodity and a single maintenance resource when the network is a single path. We describe a polynomial time algorithm which, under some simplifying assumptions, solve the single path case to optimality. The problem becomes more challenging when the simplifying assumptions are dropped.
Toward Exascale Computing through Neuromorphic Approaches

While individual neurons function at relatively low firing rates, naturally-occurring nervous systems not only surpass manmade systems in computing power, but accomplish this feat using relatively little energy. It is asserted that the next major breakthrough in computing power will be achieved through application of neuromorphic approaches that mimic the mechanisms by which neural systems integrate and store massive quantities of data for real-time decision making. The proposed LDRD provides a conceptual foundation for SNL to make unique advances toward exascale computing. First, a team consisting of experts from the HPC, MESA, cognitive and biological sciences and nanotechnology domains will be coordinated to conduct an exercise with the outcome being a concept for applying neuromorphic computing to achieve exascale computing. It is anticipated that this concept will involve innovative extension and integration of SNL capabilities in MicroFab, material sciences, high-performance computing, and modeling and simulation of neural processes/systems.
Energy Efficient Wireless Sensor Networks: A Survey on Energy-Based Routing Techniques

Wireless sensor network (WSN) is composed of sensing and detecting nodes distributed in the wireless communication region to access and sense the surrounding information of the particular deployed area. The distributed nature of WSN provides accessibility to tiny detecting nodes to sense the information statistics as well as various environmental and physical states. Efficient energy utilization is a considerable issue to maintain, expand and increase the lifetime of the wireless sensor network, as network contains battery-operated devices (nodes). Due to its sensing capability effect of nodes, WSN has a wide area of applications, such as environmental sensing, industrial process sensing, and health monitoring. These applications demand for the maintenance of network lifetime, energy efficiency, and QoS communication. These issues can be overcome with the energy efficient routing protocols. In literature, a number of routing protocols designed to address the energy efficiency, network lifetime and network capacity. To give complete understand of energy based routing mechanisms designed for WSN and cover the path for forthcoming research; this article deals with detailed information about various energy efficient routing techniques designed for WSN.
EMF Views: Dealing with several interrelated EMF models

Users only need to see some parts of an EMF model, others have to get the full model extended with data from another model, and others simply access to a combination of information coming from different models. Based on the unquestionable success and usefulness of database views to solve similar problems in the database world, EMF Views aims to bring the same concepts to the modeling world. This short talk is going to introduce the current version of EMF Views, notably showing different possible applications of model views such as Software developer views, enterprise architect views, view querying or transformation.
A Novel Hybrid DRAM/STT-RAM Last-Level-Cache Architecture for Performance, Energy, and Endurance Enhancement

High-capacity L4 architectures as a last-level cache (LLC) have been recently introduced between L3-SRAM and off-chip memory. These LLC architectures have either employed DRAM or spin-transfer torque (STT-RAM) memory technologies. It is a known fact that DRAM LLCs feature a higher energy consumption, while STT-RAM LLCs feature a lower write endurance compared to their counterparts. This paper proposes an efficient hybrid DRAM/STT-RAM LLC architecture that exploits the best characteristics offered by individual memory technologies while mitigating their drawbacks. More precisely, we introduce a novel mechanism for the storage and management of the hybrid LLC tags and a proactive L3-SRAM writeback policy that combines multiple dirty blocks that are mapped to the same LLC row. Our hybrid architecture reduces the LLC interference by having less writeback accesses and row fetches. The endurance is improved by reducing the number of STT-RAM block writes. We show that our LLC architecture reduces the total number of STT-RAM block writes by 78% and improves the average performance by 13% compared to a recently proposed STT-RAM LLC. Compared to the state-of-the-art DRAM LLC, we report an average energy and performance improvement of 24% and 17.1%, respectively.
The importance of iterative design in collaborative efforts for educational resources: a case study of the Plant Information Center (PIC) Website

Advisor Justin R. Dopke. The importance of iterative design in collaborative efforts for educational resources: A case study of the Plant Information Center (PIC) Website. A Master's paper for the M. This case study of the Plant Information Center (PIC) Website examines the effect of usability testing and iterative design on the methods of communication and collaboration when designing educational systems for children. Interviews were conducted with PIC members responsible for the design and development of the website as well as the usability test preformed in order to gain insight regarding the impact of iterative design on the development process. Data analysis suggests that the usability test was beneficial to the redesign process and also had positive impacts; increasing the group members' awareness of the potential benefits from collaboration and increasing the amount of subsequent collaborative activities among group members. Acknowledgements I would like to thank the entire PIC staff for participating in this study and those individuals who participated in the original PIC Usability Test. I would especially like to thank the advice and patience of my Master's Paper advisor –Dr. Finally, I would like to express my appreciation and thanks to my wife Susan, without whom none of this would have happened. Electronic educational software has seen an incredible evolution over the last two decades. This is largely due to the development of relatively inexpensive microcomputers and personal computers (PC's) such as the Apple IIE and the first IBM desktop systems. With the advent of the graphic user interface (GUI) the call for educational software at the K-12 level blossomed, as did the challenges to software manufacturers needing to satisfy demand, as well as educators, who were now required to evaluate software that best fit their individual (both educator's and student's) needs. Finally, understanding of the needs of children in relation to electronically based educational resources was also far from complete, on both the development and the implementation fronts. In particular, since most of the educational software available was labeled as an " educational game " , teachers were generally very reluctant to incorporate this type of new media into the classroom. With the advent of the Internet in the mid 1990's and its increasing availability to K-12 schools along with new access to inexpensive personal computers, there was a trend to substitute more traditional teaching materials with electronic resources. Unfortunately, the content and quality of these materials …
Generating Emotional Controllable Response Based on Multi-Task and Dual Attention Framework

Building an empathic conversation agent in open-domain is a key step towards affective computing and intelligent interactions. However, most current methods either focus on the consistency of content or the controllability of emotion and handling both factors are not yet properly solved. In this paper, we propose the multi-task and dual attentions (MTDA) framework for generating an emotional response. The MTDA framework decomposes the input utterance into the content layer and emotional layer, and then encodes and decodes them separately, which makes this end-to-end model more interpretable and controllable. A multi-task learning based encoder is employed in the MTDA framework, which can obtain the representation of the content and the emotion through unsupervised learning and supervised learning. A dual attention mechanism is adopted for decoding, which ensures that specific emotional responses are coherent with the content and the emotion of the input. We also combine the MTDA framework with state of the art generative models to train emotional generation systems. Extensive experiments show that our model can not only adapt to different target emotion goals but also generate coherent and informative responses.
Digitally enhanced mixed signal systems—the big picture

A mixed-signal processing system consists of analog and digital signal processing systems that are connected by interfaces. Analog systems process analog signals, which are continuous in amplitude and in time. By contrast, digital systems process digital signals, which are discrete in amplitude and in time. The interfaces between these domains are data converters that either convert the analog signal to a digital signal by using an analog-to-digital converter (ADC) or convert the digital signal to an analog signal by employing a digital-to-analog converter (DAC).
Dynamic Access Point and Service Selection in Backscatter-Assisted RF-Powered Cognitive Networks

In this paper, we investigate the dynamic access point and service selection in a backscatter-assisted radio-frequency-powered cognitive network, where many secondary transmitters (STs) can choose different transmission services provided by multiple access points. To analyze the access point and service selection of the STs, we formulate the problem as an evolutionary game. The STs act as the players and adjust their selections of the access points and services based on their utilities. Specifically, we model the access point and service adaptation of the STs by the replicator dynamics, and analytically prove the existence and uniqueness, and the stability of the evolutionary equilibrium. We also consider the delay of information used by the STs to adapt their selection and perform the analysis by using delayed replicator dynamics. In particular, the stability region of the delayed replicator dynamics in a special case is derived. Furthermore, we develop a low-complexity algorithm for the access point and service selection in the network based on evolutionary game. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed access point and service selection strategy in the network.
Inertia Level Management Strategies in Synchronously Intercon-nected Power Systems

The paper analyses possible strategies of maintaining a required inertia level by individual control blocs/areas in synchronously connected power systems. These strategies take into account the possible (significantly probable) divisions of a large system into asynchronous, unbalanced subsystems. The analysis includes different values of the allowable rate of change of frequency (RoCoF) in individual units and specific RoCoF statistics (other than maximum). These strategies require solving properly defined optimization tasks. DOI: 10.12736/issn.2300-3022.2017403 Received: 14.02. 2017 Accepted: 08.03. 2017 Available online: 23.07. 2018
Data Transfer of Flash Commands Through Different Serial Peripheral Interface (SPI) Approaches

This paper focuses on data transmission of flash instructions via Serial Peripheral Interface(SPI) bus. Flash memory is a type of storage which can be utilized even when the power is off. Examples of non-volatile memory are Read Only Memory (ROM), hard disks, floppy disks etc. In this paper, we are developing the validation suite application to test the characteristic of the flash memory. Here the program code is implemented from flash. The microcontroller used in the implementation has the features of advanced protection that includes boot ROM and supports encrypted software. SPI is used as an interface for data transmission. The flash supports three approaches. 1. NormalSPI approach. 2. Quad approach 3.Octal approach. Normal SPI approach uses two data lines for transmitting and receiving the data where as in Quad approach, it uses four data lines and in Octal approach it uses eight data lines transmission and receiving purpose. Keywords—Flash memory, non volatile memory, encrypted software, NormalSPI , Quad, Octal.
Deprecating EUI-64 Based IPv6 Addresses

Stateless Address Autoconfiguration (SLAAC) for IPv6 typically results in hosts configuring one or more stable addresses composed of a network prefix advertised by a local router, and an Interface Identifier that typically embeds a hardware address (e.g., an IEEE LAN MAC address). The security and privacy implications of embedding hardware addresses in the Interface Identifier have been known and understood for some time now, and some popular IPv6 implementations have already deviated from such scheme to mitigate these issues. This document deprecates the use of hardware addresses in IPv6 Interface Identifiers, and recommends the use of an alternative scheme ([I-D .ietf-6man-stable-privacy-addresses]) for the generation of IPv6 stable addresses.
An alternative approach to training Sequence-to-Sequence model for Machine Translation

Follow this and additional works at: https://digitalcommons.colby. edu/honorstheses Part of the Artificial Intelligence and Robotics Commons, and the Other Computer Sciences Commons Colby College theses are protected by copyright. They may be viewed or downloaded from this site for the purposes of research and scholarship. Reproduction or distribution for commercial purposes is prohibited without written permission of the author.
Adaptive Unsupervised Feature Selection on Attributed Networks

Attributed networks are pervasive in numerous of high-impact domains. As opposed to conventional plain networks where only pairwise node dependencies are observed, both the network topology and node attribute information are readily available on attributed networks. More often than not, the nodal attributes are depicted in a high-dimensional feature space and are therefore notoriously difficult to tackle due to the curse of dimensionality. Additionally, features that are irrelevant to the network structure could hinder the discovery of actionable patterns from attributed networks. Hence, it is important to leverage feature selection to find a high-quality feature subset that is tightly correlated to the network structure. Few of the existing efforts either model the network structure at a macro-level by community analysis or directly make use of the binary relations. Consequently, they fail to exploit the finer-grained tie strength information for feature selection and may lead to suboptimal results. Motivated by the sociology findings, in this work, we investigate how to harness the tie strength information embedded on the network structure to facilitate the selection of relevant nodal attributes. Methodologically, we propose a principled unsupervised feature selection framework ADAPT to find informative features that can be used to regenerate the observed links and further characterize the adaptive neighborhood structure of the network. Meanwhile, an effective optimization algorithm for the proposed ADAPT framework is also presented. Extensive experimental studies on various real-world attributed networks validate the superiority of the proposed ADAPT framework.
Cross-modal Retrieval of Chinese-CQA Based on CCA Algorithm

With the development of Chinese Q&A community, there are a large number of questionanswer pairs has being accumulated. For this question-answer pairs may contain text, pictures, audio, video and other multi-modal data. And the key question for the Chinese Q&A community platform becomes how to match the questions with the most appropriate answers by using cross-modal information such as text and images. In this paper, we propose a question and answer retrieval model based on CCA cross-modal retrieval algorithm. Firstly, the LDA is used to represent Chinese text features, and then the image features are extracted using a convolutional neural network and the Kmeans clustering method is used to obtain image features. Finally, the Canonical Correlation Analysis (CCA) method is used to retrieve between the image and text, CCA method crosses the heterogeneous problem of the underlying multimedia data, and retains the correlation of the variables, then get crossmodel search results of questions and answers. After Clear the correlation between the two models, the image and text features are mapped to the same feature space, and the similarity of the feature vectors can be directly measured, multimodal retrieval with document retrieval map is implemented. The experimental results show that the cross-modal retrieval method based on CCA in Chinese community can improve the accuracy of answer retrieval.
Design of embedded sinusoidal signal generators for mixed signal Built-in Self-Test

One of the main key points to enable mixed-signal BIST solutions is the development of efficient on-chip analog signal generators that can provide appropriate test stimuli and replace costly external signal generators in standard analog and mixed-signal functional test protocols. In this line, we present in this thesis different sinewave generation strategies based on harmonic cancellation techniques to design a high-frequency on-chip sinusoidal synthetize. The proposed generators employ digital hardware to provide a set of phase-shifted digital square-wave signals. These square-wave signals are scaled and combined using different harmonic cancellation strategies in a simplified current-steering DAC. The selected generator allows the cancellation of all harmonic components up to the eleventh. Additionally, a simple calibration strategy has been devised to compensate the impact of process variations and mismatch on the effectiveness of the harmonic cancellation. The simplicity of the circuitry makes this approach suitable for mixed-signal BIST applications. Electrical simulations of a 28nm FDSOI design and experimental results are provided to validate the functionality of the proposed signal generator. Obtained results show a calibrated performance around 52dB of SFDR for a generated sinusoidal signal at 166 MHz.
A Holistic Energy-Efficient Real-Time Scheduler for Mixed Stream and Batch Processing Workloads

In recent years we have experienced a wide adoption of novel distributed processing frameworks such as Apache Spark for handling batch and stream processing big data applications. An important aspect that has not been examined in these systems yet, is the energy consumption during the applications’ execution. Reducing the energy consumption of modern datacenters is a necessity, as datacenters contribute over 2 percent of the total US electric usage. However, efficiently scheduling applications in distributed processing systems can be challenging as there is a trade-off between minimizing the datacenter's energy usage and satisfying the application performance requirements. In this work we propose, ExpREsS, a scheduler for orchestrating the execution of Spark applications in a way that enables us to minimize the energy consumption while ensuring that the applications’ performance requirements are met. Our approach exploits time-series segmentation for capturing the applications’ energy usage and execution times, and then applies a novel DVFS technique to minimize the energy consumption. In order to tackle the limited number of application's profiling runs, we exploit regression techniques to predict the applications’ execution times and power consumption. Our detailed experimental evaluation using realistic workloads on our local cluster illustrates the working and benefits of our approach.