Reviewerly/exHarmony huggingface dataset:
LibGuides: Library Events: Home

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Ship and Corner Reflector Identification Based on Extreme Learning Machine

Corner reflector is a typical passive electronic jamming. In this paper, in order to reject corner reflector jamming effectively, the extreme learning machine algorithm is proposed to be used to discriminate ship and corner reflector base on the radar high resolution range profile (HRRP) differences, and the ship, the array corner reflectors and the combined corner reflectors electromagnetic models are built to verify the feasibility of this method. The simulation results show that there are average correct recognition rates all over 91% in SNR of ranging 0dB~15dB using the algorithm proposed with the average training time of 0.25 seconds. Compared to the support vector machine and deep belief network algorithm, the recognition rate and the training speed of the method proposed both have a better performance in the identification of ship and corner reflector.
LibGuides: Library Events: DIY and Creative Projects

Want to know what is going on at the Library? Check out all of our past and upcoming events here! Looking for something fun to do that will spruce of your home a bit? Check out these projects.
EMCC Libraries: So You Checked Out A WebCam, Now What?: Start Here

Basic information on East Mississippi Community College Library's webcam policies and how to use them.
Research Guides: Exploring Waltham: Resources for Research, Teaching, and Learning: Contact Us

A collection of resources about the City of Waltham to help students, faculty, and other partners engage with their surrounding community.
In-memory signal processing and computing based on the integrated phase-change photonic platform

Phase change photonics has become a promising research field due to its applications in non-volatile photonic memory, optical switching, signal processing, and neuromorphic photonic computing. Absorption tunability in phase change materials enables weight setting with fast speed, low power consumption, and multilevel operation for matrix multiplication with high dimensions. Applying wavelength division multiplexing scheme can largely enhance the computation capacity in a broadband manner. In this talk, we shall review our recent progress in photonic memory and neuromorphic devices using different switching methods.
Query-Specific Deep Embedding of Content-Rich Network

In this paper, we propose to embed a content-rich network for the purpose of similarity searching for a query node. In this network, besides the information of the nodes and edges, we also have the content of each node. We use the convolutional neural network (CNN) to represent the content of each node and then use the graph convolutional network (GCN) to further represent the node by merging the representations of its neighboring nodes. The GCN output is further fed to a deep encoder-decoder model to convert each node to a Gaussian distribution and then convert back to its node identity. The dissimilarity between the two nodes is measured by the Wasserstein distance between their Gaussian distributions. We define the nodes of the network to be positives if they are relevant to the query node and negative if they are irrelevant. The labeling of the positives/negatives is based on an upper bound and a lower bound of the Wasserstein distances between the candidate nodes and the query nodes. We learn the parameters of CNN, GCN, encoder-decoder model, Gaussian distributions, and the upper bound and lower bounds jointly. The learning problem is modeled as a minimization problem to minimize the losses of node identification, network structure preservation, positive/negative query-specific relevance-guild distance, and model complexity. An iterative algorithm is developed to solve the minimization problem. We conducted experiments over benchmark networks, especially innovation networks, to verify the effectiveness of the proposed method and showed its advantage over the state-of-the-art methods.
Bidirectional Estimation of Partially Black-Boxed Layers of SOM-Based Convolutional Neural Networks

The present paper aims to propose a new type of method, where complicated and incompressible components are temporarily black-boxed, to apply model compression for interpretation to more complicated networks. Then, those partially black-boxed components are estimated in forward and backward ways, namely, bi-directional estimation. Finally, the network with the estimated component is compressed as much as possible to have the simplest prototype model for easy interpretation. Then, we try to estimate the black-boxed layers in a new type of network architecture where the self-organizing map (SOM) is combined with the convolutional neural network (CNN) to process two-dimensional feature maps from the SOM. The method was applied to two well-known data sets, namely, the pulsar and occupancy detection data sets. In both experiments, we could successfully improve the generalization. In addition, the final compressed weights were very close to the correlation coefficient between inputs and targets of the original data sets for easy interpretation.
Optimistic Bounds for Multi-output Prediction

We investigate the challenge of multi-output learning, where the goal is to learn a vector-valued function based on a supervised data set. This includes a range of important problems in Machine Learning including multi-target regression, multi-class classification and multi-label classification. We begin our analysis by introducing the self-bounding Lipschitz condition for multi-output loss functions, which interpolates continuously between a classical Lipschitz condition and a multi-dimensional analogue of a smoothness condition. We then show that the self-bounding Lipschitz condition gives rise to optimistic bounds for multi-output learning, which are minimax optimal up to logarithmic factors. The proof exploits local Rademacher complexity combined with a powerful minoration inequality due to Srebro, Sridharan and Tewari. As an application we derive a state-of-the-art generalization bound for multi-class gradient boosting.
LibGuides: Quick Guide to Getting Started in a Remote Environment: Laptop Set-up

Quick overview of some recommended practices for working/studying remotely. How to set-up and office environment, manage interruptions, best browsers, etc.. Lenovo ThinkPad
Liveness Detection via Facial Expressions Queue

In this article, we have covered a mobile system that checks liveness based on human facial expressions. The facial expressions, the location of the eyes, eyebrows, mouth, and nose, which are the main elements of the face, can be distinguished by their variability and the distance between them. Further, the most important parameters are the size of the eye, how much the eyebrows are bent, how close the mouth is, and how much the mouth is open. We’ve done our system development with Android Studio and Android application development tools. The feature extraction of face is needed to recognize the facial expressions is based on the models stored by the facial points, while the final solution is found by SVM. This made it possible for each image categorized as neutral or with expressions. If not neutral, collect expressions and compare them with the expressions queue.
BRA2Cloud ??? A Brand new Resilient Agent-based Architecture to Cloud Computing

Cloud data centers, realizing that the amount of unused resources is significant, have started offering them as transient resources with unpredictable, irreversible revocation. The use of transient resources indicates many relevant issues still pose critical challenges, including security, strong and reliable connectivity and fault-tolerance approaches. To effectively use transient cloud servers to fulfill user requests, it is necessary to define an appropriate fault-tolerant mechanism and its respective parameters to avoid data loss if an unexpected failure occurs. We present an agent-based framework, namely BRA2Cloud, for integrating bag-of-tasks enabled systems using unreliable transient resources. To guarantee application execution and better use of idle resources, it is necessary to create an execution plan through fault tolerance definitions to increase reliability. To do this, BRA2Cloud agents combine features to predict failures in a multi-agent architecture that dynamically creates fault-tolerant multi-strategies, considering the current availability scenario and providing a resilient environment according to users’ application needs. Our approach was validated using real data retrieved between 2017 and 2019 from Amazon Spot Instances. Exhaustive experiments achieved high accuracy levels, reaching a 91% survival prediction success rate, which indicates the model is effective under realistic working conditions. We consider the results promising, decreasing up to 74.48% in total execution time when compared to other approaches in the literature. As the main requirements of our proposal, we have defined a series of features that BRA2Cloud should have in order to address the impact of these definitions on resiliency provision, application execution time reduction, and monetary cost reduction.
An uncertain graph-based approach for cyber-security risk assessment

We proposed a novel risk assessment approach for quantifying the security risk of lateral movement attacks, in which the attack propagation is modeled as an uncertain graph and the attack impact is a function of the set of compromised devices. We discussed several risk-based security metrics, including the expected loss, survival function, and conditional expectation - the last two measure the low-probability but high-impact events in the right tail of the loss distribution. The model is illustrated with a simple example and several directions for further research are discussed.
LibGuides: Library Events: Reading Recommendations

Want to know what is going on at the Library? Check out all of our past and upcoming events here! Looking for something fun to do? Check out these recommendations.
Accelerating Modernization of Software Acquisition to Better Serve the Warfighter … Special Emphasis on Software Assurance and Sustainment

This paper addresses the role of software as a force multiplier in satisfying Department of Defense (DoD) needs to accelerate modernization of acquisition so it can acquire, develop and transition capabilities faster than our adversaries at the speed of relevance. A special emphasis is placed on near-term software technology drivers, software assurance and the software sustainment ecosystem. This paper, number 1570593008, is written in support of the IEEE/NDIA/INCOSE Systems Security Symposium 2020 along with a presentation.
Language Revitalization: A Benchmark for Akan-to-English Machine Translation

Language reconciles the ideas, beliefs and values of people from diverse cultural, social, economic, religious and professional backgrounds, and it is imperative for sustainable development. Undoubtedly, the emergence of Neural Machine Translation has gained significant advancement in language translation automation, and consequently, establishing seamless reconciliation between two communicating entities with diverse backgrounds. Comprising of sequence to sequence architecture, Neural Machine Translation models have outperformed statistical and rule-based models in terms of concordance and syntax, regardless of how complex the language structure is. However, structured language resources are scarce for low-resource languages like Akan. Research contributions toward the revitalization of these languages are limited, along with subtle adverse events such as cultural assimilation and language imperialism. In order to solve the problem of low-resource machine translation from Akan to English, we mine and use the first parallel corpus for the Akan-English translation. We establish a benchmark for the Akan-English translation using a deep hierarchical end-to-end attention-based neural machine translation model trained on the parallel corpus. Experimental results further confirm the effectiveness of our approach towards the task. Nevertheless, a confirmed grammatical parity between the two languages bequests improvement approaching a substantial revitalization of the Akan language.
Cutting Cakes and Kissing Circles

To divide a cake into equal sized pieces most people use a knife and a mixture of luck and dexterity. These attempts are often met with varying success. Through precise geometric constructions performed with the knife replacing Euclid's straightedge and without using a compass we find methods for solving certain cake-cutting problems exactly. Since it is impossible to exactly bisect a circular cake when its center is not known, our constructions need to use multiple cakes. Using three circular cakes we present a simple method for bisecting each of them or to find their centers. Moreover, given a cake with marked center we present methods to cut it into n pieces of equal size for n=3,4 and 6. Our methods are based upon constructions by Steiner and Cauer from the 19th and early 20th century.
Research Guides: NorQuest Library Basics: Research & Citation Help

A guide to accessing and using library resources for online students. This page will introduce you to resources that will help you learn about research and citation skills.
The Syllabus as a Student Privacy Document in an Age of Learning Analytics

The purpose of this paper is to reveal how instructors discuss student data and information privacy in their syllabi.,The authors collected a mixture of publicly accessible and privately disclosed syllabi from 8,302 library and information science (LIS) courses to extract privacy language. Using privacy concepts from the literature and emergent themes, the authors analyzed the corpus.,Most syllabi did not mention privacy (98 percent). Privacy tended to be mentioned in the context of digital tools, course communication, policies and assignments.,The transferability of the findings is limited because they address only one field and professional discipline, LIS, and address syllabi for only online and hybrid courses.,The findings suggest a need for professional development for instructors related to student data privacy. The discussion provides recommendations for creating educational experiences that support syllabi development and constructive norming opportunities.,Instructors may be making assumptions about the degree of privacy literacy among their students or not value student privacy. Each raises significant concerns if privacy is instrumental to intellectual freedom and processes critical to the educational experience.,In an age of educational data mining and analytics, this is one of the first studies to consider if and how instructors are addressing student data privacy in their courses, and the study initiates an important conversation for reflecting on privacy values and practices.
Representing Voices Using Convolutional Neural Network Embeddings

In today’s society services centered around voices are gaining popularity. Being able to provide the users with voices they like, to obtain and sustain their attention, is of importance for enhanci ...
Video analysis methods to track motion across frames

Traditional methods to analyze video comprising human action are based on the extraction of the spatial content from image frames and the temporal variation across image frames of the video. Typically, images contain redundant spatial content and temporal invariance. For instance, background data and stationary objects represent redundant spatial content as well as temporal invariance. The redundancy leads to increase in storage requirements and the computation time. This paper focuses on the analysis on the key point data obtained from the capture of body movement, hand gestures, and facial expression in video-based sign language recognition. The key point data is obtained from OpenPose. OpenPose provides two-dimensional estimates of the human pose for multiple persons in real-time. In this paper, the K-means cluster method is applied to the key point data. The K-means cluster method selects the key frames based on the number of centroids formed from the key point data. The method described in this paper generates the data required for deep learning applications.
Reasoning About Plan Robustness Versus Plan Cost for Partially Informed Agents

A common approach to planning with partial information is replanning: compute a plan based on assumptions about unknown information and replan if these assumptions are refuted during execution. To date, most planners with incomplete information have been designed to provide guarantees on completeness and soundness for the generated plans. Switching focus to performance, we measure the robustness of a plan, which quantiﬁes the plan’s ability to avoid failure. Given a plan and an agent’s belief, which describes the set of states it deems as possible, robustness counts the number of world states in the belief from which the plan will achieve the goal without the need to replan. We formally describe the trade-off between robustness and plan cost and offer a solver that is guaranteed to produce plans that satisfy a required level of robustness. By evaluating our approach on a set of standard benchmarks, we demonstrate how it can improve the performance of a partially informed agent.
Quality-driven Software Development within MDA Approach

The paper proposes extension of MDA called Quality Model Driven Architecture (Q-MDA) which can be used for quality control in software development process. The proposed Q-MDA approach, being an extension to the MDA, allows for considering quality requirements while conducting the design process. The main idea of presented extension consists of two elements. The first one is explicit decomposition of a model at given abstraction level on two interrelated models: software artifacts, which reflect functional requirements and quality model. The second element is simultaneous transformation of these two models. Such a procedure ensures that the transformation decisions should be based on the quality assessment of the created models.
Using Machine Learning to Detect Cancer Early

GRAIL's mission is to detect cancer early, when it can be cured. Building a classifier that can detect cancer early in a clinical setting is a complicated endeavor with unique challenges: data acquisition and stabilization can take years; cancer status (labels) can be ambiguous, noisy, and changing; sequencing data can be enormous and presents scaling issues. Because the cancer early detection machine learning classifier is being built in the context of a clinical trial environment, an extra level of rigor and planning is required.
Monto Carlo Tree Search in Real Time Strategy Games with Applications to Starcraft 2

This thesis presents an architecture for an agent that can play the real-time strategy game Starcraft 2 (SC2) by applying Monte Carlo Tree Search (MCTS) together with genetic algorithms and machine ...
Geology and Geochemistry of Iroko-Granites Southwestern Nigeria: implication on provenance

The geology, geochemistry of Iroko-granites have been studied and reported in this paper. The study area has been described as of Archaean –Early Proterozoic terrain underlain by migmatite-gneiss-quartzite complex with supracustal rocks. Large number of granites which outcrops in Iroko-Ekiti represent a typical occurrence of granitoids sporadically distributed in the basement and are known to belong to the Older Granite suites, which are attributable to the Pan-African Orogeny(750± 150Ma). The rocks occur, mostly as flat and low lying within sparse vegetation. Structures common on and around the outcrops include quartz vein, veinlets, pegmatite dykes which trend North-south, discrete exfoliated surfaces and xenoliths of older rocks. This study reveals that the granites belong to calc-alkalic suites, demonstrate metaluminous nature, and exhibit characteristics of I-type granites. The granite is a distinctive type in that it is relatively highly potassic, has high FeO/(FeO + MgO) ratio, and high average Zr (299.75ppm) concentration with other high field strength elements. The trace elements study implicates pronounced fractional crystallization during evolution of the granites and thus petrogenetically discriminates as Syn-collision provenance.
Synergistic Target Tracking of Surface Target with Multiple Cruise Missiles

In order to improve the tracking accuracy of surface target with multiple cruise missiles, an adaptive fusion tracking algorithm based on multi-missile network is proposed. For this method, optimized arrangement of chief missiles and attack missiles is performed to improve the penetration capability of multiple cruise missiles firstly. Then time alignment and space alignment is adopted to reduce the difference multiple cruise missiles. Finally, adaptive fusion tracking algorithm is performed to further improve the tracking accuracy of multiple cruise missiles based on the statistic threshold metric. Simulation results show that, target tracking can be finished more effectively than existing methods by using the proposed algorithm.
Physics-based simulated SAR imagery generation of vehicles for deep learning applications

This paper addresses large-scale multi-look dataset generation of multi-domain vehicles taken into account contributions of backscattered radiations from scatterers in the environment, operating contexts constraints, and atmospheric conditions. In this study, we use IRIS electromagnetic modeling and simulation system for virtualization of such test scenarios signifying the requirements. Through this approach, we initially construct and employ the physics-based CAD models of different category of vehicles in their inherent operating environments. Next, we apply physics-based remote sensing to generate realistic multimodality synthetic imagery of the test vehicles and systematically annotate them. To evaluate and verify the effectiveness of this approach, we compare the generated synthetic vehicle imagery with those images of the corresponding SAR remote sensors. In this paper, we discuss the technical aspects of this modeling and simulation and the obtained results.
Examination Branch-Omr And De-Forms Generation

OMR and De-form generation system is helpful to easily generate OMR sheets during examination for each and every subject and for each and every student uniquely which is identified by barcode. OMR is the process of capturing human-marked data from document  forms such as surveys and tests. The main aim is to make the job easy by automatically identifying student from his unique barcode. De-Forms helps to enter the details of absentees, malpractice student details during examination. It is a web application where we retrieve the data of the registered students from the payment module. We assign a unique OMR code for each student and generate barcode for unique OMR sheet. We then place the template and other components required and generate OMR by assigning the OMR status to true. We generate PDF file for all the OMR sheets and assign the OMR printing status to true. It helps the examination branch to make job easy by automation and reduces manual work and improves efficiency and time.