Reviewerly/exHarmony huggingface dataset:
Scaling Up Writing in the Curriculum

Automated essay scoring (AES) allows writing to be assigned in large courses and can provide instant formative feedback to students. However, creating models for AES can be costly, requiring the collection and human scoring of hundreds of essays. We have developed and are piloting a web-based tool that allows instructors to incrementally score responses to enable AES scoring while minimizing the number of essays the instructors must score. Previous work has shown that techniques from the machine learning subfield of active learning can reduce the amount of training data required to create effective AES models. We extend those results to a less idealized scenario: one driven by the instructor's need to score sets of essays, in which the model is trained iteratively using batch mode active learning. We propose a novel approach inspired by a class of topological methods, but with reduced computational requirements, which we refer to as topological maxima. Using actual student data, we show that batch mode active learning is a practical approach to training AES models. Finally, we discuss implications of using this technology for automated customized scoring of writing across the curriculum.
Meander-Based River Coverage by an Autonomous Surface Vehicle

Autonomous coverage has tremendous importance for environmental surveying and exploration tasks performed on rivers both in terms of efficiency and data collection quality. Most surveys of rivers are performed manually using quite similar approaches. Using these practices to automate these processes improves the quality of survey operations. In addition to human expertise on the type of patterns, the coverage of a river can be optimized using the river meanders to determine the direction of coverage. In this work, we use the implicit information on the speed of the water current, inferred from the curves of the river, to reduce the cost of coverage. We use Autonomous Surface Vehicles (ASVs) to deploy the proposed methods and demonstrate the efficiency of our method. In addition, we compare the proposed method with previous coverage techniques developed in our lab. When taking into account meanders the coverage time has been decreased in average by more than $$20\%$$ . The deployments of the ASVs were performed on the Congaree River, SC, USA, and resulted in more than 27 km of total coverage trajectories.
Privacy-Preserving Annotation of Face Images Through Attribute-Preserving Face Synthesis

We investigate the viability of collecting annotations for face images while preserving privacy by using synthesized images as surrogates. We compare two approaches: a state-of-the-art 3-D face model based on deep neural networks (Extreme3D) to render a detailed 3-D reconstruction of the face from an input image; and a novel generative adversarial network architecture that we propose that extends BEGAN-CS to generate images conditioned on desired low-level facial attributes. Using these two alternative models, we conduct experiments on Mechanical Turk to annotate emotions ("joy" and "anger") on raw and synthesized versions of face images. Across 60 workers each annotating 3 versions of 60 images in each experiment, we find that: (1) The labeling accuracy when viewing surrogate images can be very similar to the accuracy when viewing raw images, but depends significantly on the labeling task. (2) The proposed extension to BEGAN-CS is effective in generating realistic images that correspond to the input vector of low-level facial attributes. (3) Overall, the GAN-based approach to generating surrogate images gives comparable accuracy as the 3-D face model, but is easier to train.
Human Motion Prediction Via Pattern Completion in Latent Representation Space

Inspired by ideas in cognitive science, we propose a novel and general approach to solve human motion understanding via pattern completion on a learned latent representation space. Our model outperforms current state-of-the-art methods in human motion prediction across a number of tasks, with no customization. To construct a latent representation for time-series of various lengths, we propose a new and generic autoencoder based on sequence-to-sequence learning. While traditional inference strategies find a correlation between an input and an output, we use pattern completion, which views the input as a partial pattern and to predict the best corresponding complete pattern. Our results demonstrate that this approach has advantages when combined with our autoencoder in solving human motion prediction, motion generation and action classification.
Improving processes by aligning Capability Maturity Model Integration and the Scaled Agile Framework®

Refined over the years with feedback from practitioners, Capability Maturity Model Integration (CMMI) is one of the most popular models for improving processes used to develop products and services. Scaled Agile Framework (SAFe®) is a disciplined structure to foster agile practices at the enterprise level. Organizations can achieve several synergies and benefits by using CMMI v2.0 in a SAFe environment. Whereas CMMI provides guidance on what process improvement teams need to do with respect to each of the model's practice areas, SAFe provides them with information on how to implement those practices. The experiences of a mid‐size IT firm in implementing CMMI in a SAFe environment highlight the importance of program increment planning, iteration management, iteration execution, built‐in quality, and enterprise management, as well as the implementation challenges that are likely to arise when streamlining processes to improve customer satisfaction.
An Interactive Virtual Reality Application in Education for Soil and Water Conservation

The paper proposes an interactive virtual reality (VR) application in education for soil and water conservation. Here an android app was developed using VR technology, which can be used for reading a picture book for learning concepts in soil and water conservation. A story is presented by the picture book. Learners play the app with VR in a smart phone and then inserting the smart phone into cheap VR helmets. Learners can watch the VR application by wearing the VR helmet so as to achieve immersive reading in a 3D VR environment. Hereafter, watching the VR application is called watching the 3D VR picture book or 3D VR video. Moreover, learners first wear the VR helmet to read the 3D VR video and then they can use the interaction sign, a double-circle shape, to trigger interactive actions such as playing sound for speaking story, going to next page, going to previous page, showing text annotations, displaying questions for getting responses to the questions, etc. Experimental results show the way learners read the 3D VR picture book has better learning effect, interaction. Furthermore, the interactive portfolios are also collected in the cloud space immediately while triggering actions by the app using the interaction sign. Each record is constructed by experience API. Therefore, interactive portfolios can be analyzed in the future for realizing the behaviors while reading 3D VR picture book.
Investigating the Capability of Agile Processes to Support Medical Devices Regulations: The Case of XP, Scrum, and FDD with EU MDR Regulations

Nowadays, medical devices rely on software whether completely such as mobile medical applications or as embedded software into medical chips such as tele surgery systems. Medical device software usually developed by using traditional development methods such as V-model and Waterfall model as these methods are straightforward and has the ability to comply with regulatory requirements such as documentation that ensures traceability. However, these methods could take a long time, could be costly, and these are inappropriate in case of requirements changing. In contrast, agile processes have several benefits such as producing a product of a high-quality with low cost and in short period with the capability to embrace change during development. Therefore, companies that develop medical device software can benefit from adopting agile practices. While the adoption rate of agile practices in software development in different industries is increasing, healthcare industries still have a low rate of agile adoption. This due to the gaps between agile practices and the stringent requirements of healthcare regulations such as documentation, traceability, and formality. The main question of this research is how capable are Agile processes can be in dealing with MDR requirements? This paper will investigate the capability of agile processes to support European Medical Device Regulation (MDR) requirements by extracting the MDR requirements that are related to software development life cycle. Then investigating the robustness of agile processes, to support MDR regulations. These objectives will be conducting by comparing and analysing the most popular agile processes (XP, Scrum, and FDD) to identify the gaps between MDR requirements and agile processes. The analysis revealed that XP is inappropriate for MDR requirements since it lacks the fixed up-front planning and also have insufficient documentations for treatability, Scrum has a model status report can be used for traceability but also it has insufficient documentation for MDR, and FDD is the closest agile practices to satisfy MDR requirements because it has a develop overall model phase which can be considered as semi-fixed up-front planning as well as has more documentations than the XP for traceability purposes such as UML modelling.
Flexible HLS-Based Implementation of the Karatsuba Multiplier Targeting Homomorphic Encryption Schemes

Custom accelerators for high-precision integer arithmetic are increasingly used in compute-intensive applications, in particular homomorphic encryption schemes. This work seeks to advance a strategy for faster deployment of these accelerators using the process of high-level synthesis (HLS). Insights from existing number theory software libraries and custom hardware accelerators are used to develop a scalable implementation of Karatsuba modular polynomial multiplication. The accelerator generated from this implementation by the high-level synthesis tool Vivado HLS achieves significant speedup over the implementations available in the highly-optimized FLINT software library. This is an important first step towards a larger goal of enabling HLS-based homomorphic encryption in the cloud.
Digital Image Restoration using Image Filtering Techniques

In modern age, image is useful media used to analyze the information within it. Sometime image which captured get blurred and noise from the environment. This phenomenon can reduce the quality of image. Image restoration technique we can restored degraded image to the nearest real image. Some filters and algorithms can be used to restoring the image, such as Weiner filter, Regularized filter, and Lucy-Richardson filter. This experiment tried to use these three techniques to restore blurry noised image using MATLAB software. Gaussian blur and noise were given to the images. Then, three methods are used to restore degraded images and compared their performance each other based on three parameters, such as Power Signal to Noise Ratio (PSNR) SSID (Structural Similarity Index), and MSE (Mean Squared Error). The experiment result shows that image restoration using Lucy-Richardson algorithm gives best performance on restoring thus degraded image in comparison of all parameter.
The Clustering Method to the Information Resource Data According to the Sign of the Number of Series of Units as a Tool to Improve the Statistical Coding Efficiency

A fundamentally new approach is considered to improve the statistical coding efficiency from the view point of length reduction of information and increasing the security of information resource. A method for clustering information resource data is worked out. The internal structure of the message element, namely the sign of the number of series of units (NSU), is used as a tool for clustering.
Snapshot High Dynamic Range Imaging via Sparse Representations and Feature Learning

Bracketed High Dynamic Range (HDR) imaging architectures acquire a sequence of Low Dynamic Range (LDR) images in order to either produce a HDR image or an “optimally” exposed LDR image, achieving impressive results under static camera and scene conditions. However, in real world conditions, ghost-like artifacts and noise effects limit the quality of HDR reconstruction. We address these limitations by introducing a post-acquisition snapshot HDR enhancement scheme that generates a bracketed sequence from a small set of LDR images, and in the extreme case, directly from a single exposure. We achieve this goal via a sparse-based approach where transformations between differently exposed images are encoded through a dictionary learning process, while we learn appropriate features by employing a stacked sparse autoencoder (SSAE) based framework. Via experiments with real images, we demonstrate the improved performance of our method over the state-of-the-art, while our single-shot based HDR formulation provides a novel paradigm for the enhancement of LDR imaging and video sequences.
RecoLibry-core: A component-based framework for building recommender systems

Recommendation systems are a key part of almost every modern consumer website. These systems include techniques to filter, explore and rank a huge amount of information based on users’ preferences or similar items. Designing and implementing a recommender system from scratch require skills of programming and recommending technologies. In this paper we describe RecoLibry-core, a framework to develop recommender systems based on the reuse of components provided by third-party frameworks.
An Agent Based Virtual Machine Migration Process for Cloud Environment

The adoption of cloud computing is increasing day by day. To move the data from physical machines to cloud, migration mechanism is required. After shifting the data there is need of migration amongst virtual machines of cloud data centers. It may be within the data center or outside the datacenter. To cater the issue of migration, this paper introduces the concept of migration as well as virtual machine migration. This paper proposes an algorithm for migration process with the help of agents which results comes out with minimum migration time.
Automated Facial Recognition Attendance System Leveraging IoT Cameras

In recent years, biometrics using facial recognition has become an important part of many industries like security, retail, marketing, health-care, etc. Internet of Things has played a pivotal role in enhancing and automating technology for practical applications. However, there exist some issues with implementing these systems practically. In this paper, an attendance management system is proposed which can detect and recognize faces of an entire class from a few pictures and mark the attendance of the recognized students. IoT cameras have been used instead of a smartphone camera to increase coverage and reduce the number of missed attendances. In addition to reducing manual work, it has also been ensured that this system is completely secure. The system proposed mainly comprises of IoT Camera Module, custom back-end server and separate Android apps for teachers and students. The system is also able to provide high accuracy at low cost as compared to other other similar applications and hence is suitable for real-life use.
Unsupervised Large‐Scale Search for Similar Earthquake Signals

Abstract Seismology has continuously recorded ground‐motion spanning up to decades. Blind, uninformed search for similar‐signal waveforms within this continuous data can detect small earthquakes missing from earthquake catalogs, yet doing so with naive approaches is computationally infeasible. We present results from an improved version of the Fingerprint And Similarity Thresholding (FAST) algorithm, an unsupervised data‐mining approach to earthquake detection, now available as open‐source software. We use FAST to search for small earthquakes in 6–11 yr of continuous data from 27 channels over an 11‐station local seismic network near the Diablo Canyon nuclear power plant in central California. FAST detected 4554 earthquakes in this data set, with a 7.5% false detection rate: 4134 of the detected events were previously cataloged earthquakes located across California, and 420 were new local earthquake detections with magnitudes −0.3≤ML≤2.4, of which 224 events were located near the seismic network. Although seismicity rates are low, this study confirms that nearby faults are active. This example shows how seismology can leverage recent advances in data‐mining algorithms, along with improved computing power, to extract useful additional earthquake information from long‐duration continuous data sets.
A Screening CAD Tool for the Detection of Microcalcification Clusters in Mammograms

Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)-based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. Thus, it might be used as a second reader as well as a screening tool to reduce the burden on radiologists.
Turning Logic Transistors into Secure, Multi-Time Programmable, Embedded Non-Volatile Memory Elements for 14 nm FINFET Technologies and Beyond

Described is a secure, multi-time programmable memory (MTPM) solution for the 14 nm FINFET node and beyond, which turns as-fabricated standard logic transistors into embedded non-volatile memory (eNVM) elements, without the need for any process adders or additional masks. These logic transistors, when employed as eNVM elements, are dubbed “Charge Trap Transistors” (CTTs). Outlined are the technological breakthroughs required for employing logic transistors as an MTPM. An erase technique, called “Self-heating Temperature Assisted eRase” (STAR), is introduced which enables 100% erase efficiency, as compared to <; 50% erase efficiency using conventional methods, in turn enabling MTPM functionality in CTTs. For the first time, hardware results demonstrate an endurance of > 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> program/erase cycles. Data retention lifetime of > 10 years at 125 °C and scalability to 7 nm have been confirmed.
3-D tracking of air targets using a single 2-D radar

Three-dimensional (3-D) aircraft tracking using two-dimensional (2-D) radar measurements is a common task in air traffic control (ATC) systems. In this paper, a new algorithm using a 2-D ATC radar, called HPEKF with probabilistic data association (HP-PDA-EKF), is proposed for aircraft tracking with false alarms and missed detections. An improved filter initialization technique is also proposed to yield better estimates. To mitigate the degradation in altitude estimation accuracy with increasing aircraft distance, ATC waypoints are used as extra measurement information, and a new height-parametrized cascaded PDA filter (HP-CPDAF) algorithm is proposed to make use of waypoints. The posterior Cramér–Rao lower bounds (PCRLBs) quantifying the achievable accuracies of these two filters are derived. Simulations are carried out to analyze the relationship between aircraft distance, waypoint accuracy and state estimation accuracy. The viability of using a single 2-D radar for 3-D ATC and the usefulness of the proposed algorithms under different operating conditions are analyzed. Simulation results demonstrate the validity of the proposed algorithms.
A comparative study of Mouse and Keystroke Based Authentication

One of the basic behavioural biometric methods is keystroke element. Being less expensive and not requiring any extra bit of equipment is the main advantage of keystroke element. The primary concentration of this paper is to give an inevitable review of behavioural biometrics strategies, measurements and different methodologies and difficulties and future bearings specially of keystroke analysis and mouse dynamics. Keystrokes elements frameworks utilize insights, e.g. time between keystrokes, word decisions, word mixes, general speed of writing and so on. Mouse Dynamics is termed as the course of actions captured from the moving mouse by an individual when interacting with a GUI. These are representative factors which may be called mouse dynamics signature of an individual, and may be used for verification of identity of an individual. In this paper, we compare the authentication system based on keystroke dynamics and mouse dynamics.
Multi-domain Embedding Strategies for Video Steganography by Combining Partition Modes and Motion Vectors

Digital video has various types of entities, which are utilized as embedding domains to hide messages in steganography. However, nearly all video steganography uses only one type of embedding domain, resulting in limited embedding capacity and potential security risks. In this paper, we firstly propose to embed in multi-domains for video steganography by combining partition modes (PMs) and motion vectors (MVs). The multi-domain embedding (MDE) aims to spread the modifications to different embedding domains for achieving higher undetectability. The key issue of MDE is the interactions of entities across domains. To this end, we design two MDE strategies, which hide data in PM domain and MV domain by sequential embedding and simultaneous embedding respectively. These two strategies can be applied to existing steganography within a distortion-minimization framework. Experiments show that the MDE strategies achieve a significant improvement in security performance against targeted steganalysis and fusion based steganalysis.
Dependability analysis for characterizing Google cluster reliability

Summary Cloud solutions are emerging as a new suitable way of transforming traditional IT data centers to highly available and reliable computing resources for hosting critical applications and data. However, software and hardware failures are a common problem in cloud datacenters that can lead to harmful damages. In this paper, we analyze the physical server failures in the Google cloud datacenter. We study the Google cluster properties to investigate the relationship among physical servers' failure rate and jobs failure events. The failure rate of Google cluster executed jobs and servers is taken into consideration during a 29‐day period. We present a reliability model for Google cluster physical machines using the continuous time Markov chains according to this observation. We attempt to analyze the obtained model through SHARPE software packages to improve the understanding of failure events in the Google cloud cluster. We also explore the cluster availability based on parameters like steady‐state availability, steady‐state unavailability, mean time to failure, and mean time to repair in the Google cluster.