Reviewerly/exHarmony huggingface dataset:
Embedded Processor-In-Memory Architecture for Accelerating Arithmetic Operations

A processor-in-memory (PIM) computer architecture is any design that performs some subset of logical operations in the same location as memory. The traditional model of computing involves a processor loading data from memory to perform operations, with a bus connecting the processor and memory. While this technique works well in many situations, a growing gap between memory performance and processor performance has led some researchers to develop alternative architectures.This paper details the implementation of a PIM architecture in a soft core microcontroller used to accelerate applications limited by register file size. Using an Artix-7 FPGA, an ATmega103 microcontroller soft core is modified to include a PIM core as an accelerator. The sample application of AES encryption provides a comparison between the baseline processor and the PIM enhanced machine. AES encryption using the modified microcontroller requires 38% fewer clock cycles without relying on application specific improvements, at the expense of increased program memory size and FPGA fabric utilization.
Conformal Feature-Selection Wrappers and ensembles for negative-transfer avoidance

In this paper we propose two methods for instance transfer based on conformal prediction. As a distinctive character, both of the methods are model independent and combine feature selection and source-instance selection to avoid negative transfer. The methods have been tested experimentally for different types of classification model on several benchmark data sets. The experimental results demonstrate that the new methods are capable of outperforming significantly standard instance transfer methods.
An Evaluation Approach for Selecting Suitable Defect Prediction Method at Early Phases

One of the most important quality indicators of a software product is its defect rates and accordingly, Software Defect Prediction (SDP) has become a widely-accepted practice in both academia and industry to reduce the cost of defects. Despite being reported less frequently, applying SDP in the early phases of software development is even more beneficial in reducing the cost of quality. In this regard, we expect that the existence of a standard procedure or approach for evaluating and selecting a convenient method for building a prediction model in the early phases could make SDP less challenging, especially for the practitioners. Therefore, in this paper, we identify criteria to evaluate SDP methods and select the best-fit one with respect to stakeholder needs which are elicited via a questionnaire in the early phases of the development lifecycle. We consider stakeholder needs in SDP method selection which is based on multi-criteria decision analysis. We investigate the feasibility of our evaluation approach over an open dataset suitable for early SDP, which initially confirm the feasibility of the proposed approach.
Privacy-friendly Blockchain Based Data Trading and Tracking

Data trading has become a promising way to bridge numerous data islands and benefit a wide range of data driven applications. However, due to the high risk of data privacy, the easy-to-copy feature of data, and potential malicious behaviors of users, there still lacks data trading solutions to provide privacy protection, copyright/ownership protection and auditability of user behaviors. In this work, we propose a blockchain and smart contract based framework to keep tamper-proof records of data trading transactions, which also preserves users' identity, behavior and data privacy. Data fingerprint is introduced to track data and protect data ownership. A set of protocols are also designed to prevent dishonest behaviors and ensure the security of data trading.
Geo-Sensor Framework and Composition Toolbox for Efficient Deployment of Multiple Spatial Context Service Platforms in Sensor Networks

Geo-sensor is the term used for the deployment of a wireless sensor network (WSN) in a real environment, which can be a hideous task due to many influential variables in a given environment. The spatial context of a sensor in a smart environment can be of huge significance and can also play an important role in improving the smart services provision. In this work, we propose a DIY geo-sensor framework and data composition toolbox for the deployment of sensors data in smart IoT environments along with geographical context. A geo-sensor framework is deployed, which enables the registration of multiple geo-sensor networks by providing multiple geo-sensor platforms. The framework’s logic is based on the combination of a geo-sensor service registry, geo-sensor composition toolbox, and geo-sensor platforms. A geo-sensor platform provides the content to the toolbox, enabling relaxed real-time geo-sensor data management. Our proposed work is two-fold. Firstly, we propose the design details for the geo-sensor framework and composition toolbox. The proposed design for the geo-sensor framework aims to provide a DIY platform for multiple geo-sensor networks and services deployment, giving access to multiple users resulting in reuse of resources and reduction in deployment costs by avoiding duplicate deployments. Secondly, we implement the proposed design based on RESTful web services and SOAP web services. Performance comparison analysis is then performed among the two web services to find the best suited implementation for given scenarios. The results of the performance analysis prove that RESTful web services are the better choice for ease of implementation, access, and light-weightiness.
Improving the scalability of blockchain through DAG

Current blockchain systems face the problems of poor scalability, low performance, and high cost. To address the previous bottlenecks, we plan to employ the DAG-based structure as the primary method and propose a concrete model, called 3D-DAG, to improve the scalability. The model consists of two layers: mainchain and sidechain, which separately severs for maintaining the pivot sequence and improving the parallelism. Our expecting results should significantly improve the performance and scalability without compromising the security.
A Natural Quadratic Approach to the Generalized Graph Layering Problem

We propose a new exact approach to the generalized graph layering problem that is based on a particular quadratic assignment formulation. It expresses, in a natural way, the associated layout restrictions and several possible objectives, such as a minimum total arc length, minimum number of reversed arcs, and minimum width, or the adaptation to a specific drawing area. Our computational experiments show a competitive performance compared to prior exact models.
Reliable Domain Adaptation with Classifiers Competition

Unsupervised domain adaptation (UDA) aims to transfer labeled source domain knowledge to the unlabeled target domain. Previous methods usually solve it by minimizing joint distribution divergence and obtaining the pseudo target labels via source classifier. However, those methods ignore that the source classifier always misclassifies partial target data and the prediction bias seriously deteriorates adaptation performance. It remains an open issue but ubiquitous in UDA, and to alleviate this issue, a Reliable Domain Adaptation (RDA) method is proposed in this paper. Specifically, we propose double task-classifiers and dual domain-specific projections to align those easily misclassified and unreliable target samples into reliable ones in an adversarial manner. In addition, the domain shift of both manifold and category space is reduced in the projection learning step. Extensive experiments on various databases demonstrate the superiority of RDA over state-of-the-art unsupervised domain adaptation methods.
Survey of use mobile augmented reality for teaching materials

Abstract This study discusses the review of the literature focuses on the use of Augmented Reality (AR) as a teaching material. Authors conducted a review 20 articles which were published from 2008 to 2018. Design studies also found a variety of experiments such as data, analysis, action research, surveys, mixed methods, and Research and Development. This review found that the use of AR as a teaching material in learning shows that the AR is able to improve your skills, understanding, motivation, response, and the results of student learning of the content and media used.
Design of Emergency Call Record Support System Applying Natural Language Processing Techniques

Currently Command and Control Centers (C2), such as the Integrated Security Service ECU 911, are managed by Computer Assisted Dispatch Systems (CADS). These systems facilitate the registration of incidents and the distribution of rescue resources. However, the information registration process does not yet have automated methods. Important data such as: name, address, reference and categories are recorded manually, which generates problems of loss of information and inefficiency, in terms of time and attention to the incident. As a solution to these problems, the design of an emergency call record support system is proposed, based on Natural Language Processing (NLP) techniques and algorithms. Taking into consideration an analysis of the processes of the ECU 911, three modules are proposed: (1) transcription of audio to text calls (ASR), (2) extraction of relevant information (NER) such as: address and references; and (3) call classification (TF-IDF/SVM) according to service and priority. Thus obtaining the design of an automated support system for CADS, which provides quality information in a timely manner.
Data-Driven Windows to Accelerate Video Stream Content Extraction in Complex Event Processing

This work presents a data-driven adaptive windowing approach to accelerate video content extraction in DNN-based Complex Event Processing (CEP) systems. The CEP windows continuously monitor low-level content of incoming video frames and exploit interframe correlations to accelerate the overall DNN content extraction process. The two main contributions are: 1) technique to create micro-batches of similar frames within the window by measuring dissimilarities among them, and 2) optimal frame resolution within micro-batches under specified accuracy thresholds for fast model processing. The initial experimental results show that our adaptive micro-batching approach improves 3.75X model throughput execution while maintaining application-level latency bounds under required accuracy constraints.
A Novel Screening System for Alzheimer’s Disease Based on Speech Transcripts Using Neural Network

Alzheimer's disease has become one of the biggest challenges in the healthcare system worldwide. Researches have shown that Alzheimer's disease is the sixth leading cause of death in the United States and even the fifth leading cause among people aged 65 and older. Moreover, the number of patients is escalating rapidly in recent years, which also increases the burden on the healthcare system. Therefore, a screening system that can help the doctor to diagnose Alzheimer's disease is demanded. In this paper, we proposed a screening system based on the transcripts of speeches spoken by subjects undertaking a neuropsychology test. While most of the related studies have utilized extracted syntactic and semantic features and relied on a feature selection process, the proposed system used word vectors as the representation of a spoken speech, and Recurrent Neural Network together with attention mechanism as the classifier. Using ten times 10-fold cross validation on an open dataset with 242 speeches samples spoken by healthy controls and 257 samples spoken by subjects with Alzheimer's disease, a mean accuracy of 0.835 is achieved in our work, which is outperforming the current state-of-the-art while requiring less effort.
Multi-point traversal path planning of manipulator based on improved RRT algorithm

With the development of robot technology, the manipulator has been used in various fields. A safe and effective motion path is the key to the application of manipulator. So that the path planning algorithm for multi-target point traversal of manipulators is studied and implemented on a 6-DOF (degree of freedom) manipulator in this paper. Firstly, for the general working scene of manipulators, the working space is divided into obstacle-free space and operation space, and the RRT (rapidly exploring random tree) algorithm is combined with joint trajectory planning algorithm. It will effectively reduce the search space of the RRT algorithm, thereby the planning efficiency is improved. Considering the general task requirements which is moving the end of the manipulator between multiple points, this paper proposes an efficient improved RRT algorithm to realize the construction of multiple random trees at the same time and effectively connect them. It can improve the utilization of random trees and the efficiency of path planning between multiple points. On this basis, the result of path planning is shortened by reducing the waypoints. So that the algorithm result is optimized. Finally, the effectiveness and advancement of the improved algorithm for multi-point traversal path planning are verified by simulation.
Classification and retrieval of thoracic diseases using patch-based visual words: a study on chest x-rays

Abstract This research work explores the Content-Based Medical Image Retrieval system (CBMIR) to categorization and retrieval of different types of common thoracic diseases such as Atelectasis, cardiomegaly, Effusion, Infiltration etc, based on local patch representation of ‘Bag of Visual Words’ approach, when performing patch-based image representation, the selected patch size has significant impact on image categorization and retrieval process. It is a challenging task in selecting the appropriate patch size to the current experimental dataset. Chest Xray8 medical image database is used, to analyze the impact of different patch size to categorize and retrieval of eight common thorax diseases. 1000 frontal view x-ray images is obtained (100 images from each category and 200 images combination of more than one disease) from the database. Different sizes of image patches (16 × 16 and 32 × 32) and different codebook sizes (500, 1000, 1500, 2000) created to identify best precision and recall values. From the excremental result, 32 × 32 patch size and 1500 codebook size gives the good precision and recall value using Radial Basis Function SVM kernel.
A Comparison of Text Classifiers on IT Incidents Using WEKA

IT service management and incident management is a hot topic in every company which serves IT services and they require human effort to manage. In ITIL framework for IT service management, it's always useful to link the incidents with configuration items, in other words the assets or components necessary to deliver IT services and this task is managed manually by IT support technicians in many IT service management tools. The aim of the study is to remove this manual linking step by applying text classification methods and instead to provide an automatic assignment of CI's to incidents. Four of the text classification methods, Naïve Bayes Multinomial, k-Nearest Neighbor, Support Vector Machine and J48 decision tree classifiers, are used on three different sets of incidents extracted from the same database by using different filters. The impact of some pre-processing steps is compared on different sizes of datasets.
Multi-agent Path Finding with Capacity Constraints

In multi-agent path finding (MAPF) the task is to navigate agents from their starting positions to given individual goals. The problem takes place in an undirected graph whose vertices represent positions and edges define the topology. Agents can move to neighbor vertices across edges. In the standard MAPF, space occupation by agents is modeled by a capacity constraint that permits at most one agent per vertex. We suggest an extension of MAPF in this paper that permits more than one agent per vertex. Propositional satisfiability (SAT) models for these extensions of MAPF are studied. We focus on modeling capacity constraints in SAT-based formulations of MAPF and evaluation of performance of these models. We extend two existing SAT-based formulations with vertex capacity constraints: MDD-SAT and SMT-CBS where the former is an approach that builds the model in an eager way while the latter relies on lazy construction of the model.
A New Correction for He Loss Applied to (U‐Th)/He Dating of Grains with Complex Shapes and Polymineralic Aggregates

Abstract Applying the (U‐Th)/He chronometer to small domains with complex shapes is challenging because of He recoil loss. The development of date correction factors proves challenging when considering either a single grain with complex geometry or polymineralic crystal aggregates composed of many small U‐Th‐bearing grains with additional impurities, such as iron‐oxide aggregates. We introduce a new geometrical metric for the recoil loss correction factor—the two‐point correlation distribution S 2 ( r )—that is independent of dimensionality (i.e., two‐dimensional [2‐D] or three‐dimensional [3‐D] images), grain shape, and existence or absence of internal porosity. S 2 ( r ) measures the probability of finding two points within the target phase (U‐Th‐He host, such as Fe oxides here) separated by a distance r in the physical sample (single grain or aggregate). It provides a metric that is independent of grain or aggregate size. We use a combination of forward recoil loss simulations in 2‐D and 3‐D domains (both complex single grains and aggregates) to show a one‐to‐one correspondence between a recoil loss factor and S 2 ( r = He stopping distance). We illustrate the workflow to retrieve the loss factors for different isotopes and correct date determination in a complex 3‐D jasperoid sample imaged by X‐ray computed microtomography. Our jasperoid sample contains appreciable amounts of iron oxide with quartz impurities and is representative of a sample for which completely monomineralic separates cannot be obtained. The computation of loss factors based on S 2 ( r ) is extremely efficient, and the codes, including a simple user interface, are made available at https://repository.library.brown.edu/studio/item/bdr:957056/ .
Breadth-First Search on Dynamic Graphs using Dynamic Parallelism on the GPU

Breadth-First Search is an important basis for many different graph-based algorithms with applications ranging from peer-to-peer networking to garbage collection. However, the performance of different approaches depends strongly on the type of graph. In this paper, we present an efficient algorithm that performs well on a variety of different graphs. As part of this, we look into utilizing dynamic parallelism in order to both reduce overhead from latency between the CPU and GPU, as well as speed up the algorithm itself. Lastly, integrate the algorithm with the faimGraph framework for dynamic graphs and examine the relative performance to a Compressed-Sparse-Row data structure. We show that our algorithm can be well adapted to the dynamic setting and outperforms another competing dynamic graph framework on our test set.
Real-Time Docking Ring Detection Based on the Geometrical Shape for an On-Orbit Spacecraft

Docking ring is a circular hatch of spacecraft that allows servicing spacecraft to dock in various space missions. The detection of the ring is greatly beneficial to automatic capture, rendezvous and docking. Based on its geometrical shape, we propose a real-time docking ring detection method for on-orbit spacecraft. Firstly, we extract arcs from the edge mask and classify them into four classes according to edge direction and convexity. By developing the arc selection strategy, we select a combination of arcs possibly belonging to the same ellipse, and then estimate its parameters via the least squares fitting technique. Candidate ellipses are validated according to the fitness of the estimation with the actual edge pixels. The experiments show that our method is superior to the state-of-the-art methods, and can be used in real time application. The method can also be extended to other applications.
Privacy Preserving Using Pup-Rup Model

Cloud services are exponentially growing day by day, roughly around 2.5 quintillion bytes of data created each day [1]. Many companies provide cloud services like cloud storage for your data, since it is in the hands of the third-party service provider, you will be left with privacy concern. While one can opt for a personal cloud device and store the data as the only drive with data, theft may render user without any chance of recoverability of the data. Our paper proposes a PUP-RUP (Privacy Under Personal cloud - Recoverability Under Public cloud) model, which addresses the privacy concerns in public cloud and recoverability issue in personal cloud by using both public and personal cloud where personal cloud device holds the sensitive data which can be shared with a shared link to the group of users and encrypting the same data it is stored in the public cloud which makes data unusable to the third-party service providers without key, maintained by user. We will see how PUP-RUP model perform against existing models.
Prediction Analysis of Student Specialization Suitability using Artificial Neural Network Algorithm

Some study programs consist of a core study program and additional programs required by the students to choose a field of specialization related to the disciplines studied. Due to the lack of gathered information about the student's ability as a relevant consideration for choosing a bachelor specialization, a system to analyze the suitability student's specialization is required. An analysis for predicting student's specialization using Artificial Neural Network algorithm therefore, was proposed to provide information about classification for the student based on their competence. The main purposes of this study were to analyze the result of student's specialization prediction using the classification method with Artificial Neural Network algorithm and to analyze the gap between the results of manual selection and the results of the algorithm. The advantages of Artificial Neural Network are its adaptability, nonlinearity, modularity, and openness to noisy, fuzzy, or soft data. The result showed that the accuracy using this process was around 94.81%. Thus, it could bring impacts and benefits for students, educators, and institutions.
Small Object Detection on Road by Embedding Focal-Area Loss

In recent years, with the continuous popularity of deep learning, the research on artificial intelligence has boosted the progress of many new applications, such as the autonomous driving. At present, the detection methods of vehicles, pedestrians and other objects in the self-driving technology have been investigated numerously, but there is no good solution for the detection of small objects such as stones on road. However, small targets on road seriously affects the stability of automated vehicle system. Therefore, it is important to carry out the detection of small targets on road. This paper designs a focal-area loss function which is learned by focusing the area change of small targets. The contribution of small object is weighted more in learning. We embed this focal-area loss into a newly proposed Scale Normalization for Image Pyramids (SNIP). Exhaustive experiments on Lost And Found (LAF) dataset show that our method can significantly boost the performance of state-of-the-art.