Reviewerly/exHarmony huggingface dataset:
Synthetic AI training data generation enabling airfield damage assessment

Object detection AI’s enable robust solutions for fast, automated detection of anomalies in operating environments such as airfields. Implementation of AI solutions requires training models on a large and diverse corpus of representative training data. To reliably detect craters and other damage on airfields, the AI must be trained on a large, varied, and realistic set of images of craters and other damage. The current method for obtaining this training data is to set explosives in the concrete surface of a test airfield to create actual damage and to record images of this real data. This approach is extremely expensive and time consuming, results in relatively little data representing just a few damage cases and does not adequately represent damage to UXO and other artifacts that are detected. To address this paucity of training data, we have begun development of a training data generation and labeling pipeline that leverages Unreal Engine 4 to create realistic synthetic environments populated with realistic damage and artifacts. We have also developed a labeling system for automatic labeling of the detection segments in synthetic training images, in order to provide relief from the tedious and time-consuming process of manually labeling segments in training data and eliminate human errors incurred by manual labeling. We present comparisons of performance of two object detection AI’s trained on real and synthetic data and discuss cost and schedule savings enabled by the automated labeling system used for labeling of detection segments.
Planning with Expectation Models for Control.

In model-based reinforcement learning (MBRL), Wan et al. (2019) showed conditions under which the environment model could produce the expectation of the next feature vector rather than the full distribution, or a sample thereof, with no loss in planning performance. Such expectation models are of interest when the environment is stochastic and non-stationary, and the model is approximate, such as when it is learned using function approximation. In these cases a full distribution model may be impractical and a sample model may be either more expensive computationally or of high variance. Wan et al. considered only planning for prediction to evaluate a fixed policy. In this paper, we treat the control case - planning to improve and find a good approximate policy. We prove that planning with an expectation model must update a state-value function, not an action-value function as previously suggested (e.g., Sorg & Singh, 2010). This opens the question of how planning influences action selections. We consider three strategies for this and present general MBRL algorithms for each. We identify the strengths and weaknesses of these algorithms in computational experiments. Our algorithms and experiments are the first to treat MBRL with expectation models in a general setting.
Event Detection as Question Answering with Entity Information

In this paper, we propose a recent and under-researched paradigm for the task of event detection (ED) by casting it as a question-answering (QA) problem with the possibility of multiple answers and the support of entities. The extraction of event triggers is, thus, transformed into the task of identifying answer spans from a context, while also focusing on the surrounding entities. The architecture is based on a pre-trained and fine-tuned language model, where the input context is augmented with entities marked at different levels, their positions, their types, and, finally, the argument roles. Experiments on the ACE~2005 corpus demonstrate that the proposed paradigm is a viable solution for the ED task and it significantly outperforms the state-of-the-art models. Moreover, we prove that our methods are also able to extract unseen event types.
Improved Dynamic Routing Algorithm for Information Aggregation

Information aggregation is an essential component of text encoding, but it has been paid less attention. The pooling-based (max or average pooling) aggregation method is a bottom-up and passive aggregation method, and loses a lot of important information. Recently, attention mechanism and dynamic routing policy are separately used to aggregate information, but their aggregation capabilities can be further improved. In this paper, we proposed an novel aggregation method combining attention mechanism and dynamic routing, which can strengthen the ability of information aggregation and improve the quality of text encoding. Then, a novel Leaky Natural Logarithm (LNL) squash function is designed to alleviate the “saturation” problem of the squash function of the original dynamic routing. Layer Normalization is added to the dynamic routing policy for speeding up routing convergence as well. A series of experiments are conducted on five text classification benchmarks. Experimental results show that our method outperforms other aggregating methods.
Possibilities of Auto ML in Microsoft Replacing the Jobs of Data Scientists

Most of the people have confusion in the scope of Data Science because of the advancements in Artificial Intelligence (AI). Particularly, Auto ML (also known as Automated Machine Learning) has challenged the jobs of data scientists. This article aims on clearing the misconceptions about the subject.
Review Manipulation: Literature Review, and Future Research Agenda

Abstract Background: The phenomenon of review manipulation and fake reviews has gained Information Systems (IS) scholars’ attention during recent years. Scholarly research in this domain has delved into the causes and consequences of review manipulation. However, we find that the findings are diverse, and the studies do not portray a systematic approach. This study synthesizes the findings from a multidisciplinary perspective and presents an integrated framework to understand the mechanism of review manipulation. Method: The study reviews 88 relevant articles on review manipulation spanning a decade and a half. We adopted an iterative coding approach to synthesizing the literature on concepts and categorized them independently into potential themes. Results: We present an integrated framework that shows the linkages between the different themes, namely, the prevalence of manipulation, impact of manipulation, conditions and choice for manipulation decision, characteristics of fake reviews, models for detecting spam reviews, and strategies to deal with manipulation. We also present the characteristics of review manipulation and cover both operational and conceptual issues associated with the research on this topic. Conclusions: Insights from the study will guide future research on review manipulation and fake reviews. The study presents a holistic view of the phenomenon of review manipulation. It informs various online platforms to address fake reviews towards building a healthy and sustainable environment.
Sandi Bcd Ke Peraga Seven Segment Dengan Simulasi Proteus

Abstract This study discusses how to convert a BCD series to seven segments with the help of software applications.BCD password is a way of writing decimal numbers, each digit represented by a binary number.Seven segment is a display that is formed from 7 groups of LED (Light Emitting Diode) segments that function to emit light when passing through an electric current and arranged in such a way as to form numbers from 0 to 9.One application that is used is Proteus, where this application can be used to find out whether the circuit is correct or wrong.The types of ICs used are IC TTL 7447 and IC TTL 7448. Data collection techniques using quantitative descriptive and based on practicum and simulation.Based on the results of the study it can be concluded that the conversion of the BCD series into seven segments using Proteus simulations can be proven.So this application can be used to find out whether the circuit is correct or still wrong in order to study the conversion of the BCD series to seven segments. Keywords: BCD password, Seven Segment, Proteus Application, IC 7447, IC 7448
Rehearsal revealed: The limits and merits of revisiting samples in continual learning

Learning from non-stationary data streams and overcoming catastrophic forgetting still poses a serious challenge for machine learning research. Rather than aiming to improve state-of-the-art, in this work we provide insight into the limits and merits of rehearsal, one of continual learning's most established methods. We hypothesize that models trained sequentially with rehearsal tend to stay in the same low-loss region after a task has finished, but are at risk of overfitting on its sample memory, hence harming generalization. We provide both conceptual and strong empirical evidence on three benchmarks for both behaviors, bringing novel insights into the dynamics of rehearsal and continual learning in general. Finally, we interpret important continual learning works in the light of our findings, allowing for a deeper understanding of their successes.
Welcome and Introduction to SPIE Conference 11742

Introduction to SPIE Defense and Commercial Sensing conference 11742: Radar Sensor Technology XXV
OPTIMUM TILT ANGLE FOR MAXIMIZING LARGE SCALE SOLAR ELECTRICAL ENERGY OUTPUT

Many large-scale solar (LSS) plants that are being installed today have solar photovoltaic (PV) panels mounted on fixed structures, which limits its electrical energy production. Tracking system can be installed so that the PV panels could change its tilt angle automatically in accordance with the sun’s movement. However, it will increase the construction, operation and maintenance cost significantly. Another option is to manually adjust the tilt angle on periodically basis, but the time period and the optimum tilt angle need to be systematically determined. This paper investigates the impact of using monthly and seasonal optimum tilt angle, βopt on electrical energy production of LSS plant. The proposed strategy can be implemented by using tiltable solar panel mounting structures which is far cheaper than the tracking system. For the study, 1 MW LSS system model is used. Twelve cities around the globe with latitude angle ranging from 0º to 55º are strategically selected. The electrical energy output from the 1 MW LSS plant is simulated by using PV mathematical model that is developed in Matlab software. The overall results show that by adjusting the tilt angle of the PV modules into its optimum angle on monthly or seasonal basis, it would increase the generated energy output between 1.91% and 7.24% for monthly adjustments and between 1.59% and 6.06% for seasonal adjustments.
Heterogeneous Hand Gesture Recognition using 3D Dynamic Skeletal Data

Hand gestures area unite the foremost natural and intuitive non-verbal communication medium whereas interacting with a pc, and connected analysis have recently boosted interest. To boot, the distinctive options of the hand provided by current business cheap depth -camera are often exploited in numerous gesture recognition based systems, for human -computer interaction. This paper builds a sturdy hand form options from two modalities of depth and skeleton form approach, we have a tendency to use the movements, the rotations of the hand joints with relevance to their neighbors, and also the skeleton point cloud to find out the 3D geometric transformation. For the hand depth form approach, we have tendency to use the feature illustration from the hand element segmentation model. Finally, we propose a multi-level feature LSTM with CONV1D and CONV2D algorithm where LSTM is used to manage the range of hand options. Therefore, we tend to propose a completely unique technique by exploiting skeletal point clouds from skeletal form as well depth features from real hand depth form in order for the LSTM model to benefit from both. Our projected technique achieves the best result with skeletal and depth data.
THEATRICAL ALBUMS BY M. A. BULGAKOV IN THE HISTORICAL AND CULTURAL CONTEXT

Bulgakov’s theatrical album is both a creative laboratory and a diary. One of the fi rst questions to be asked concerns the semiotic nature of a theatrical album, which has its own specifi c language. The theatrical album as a unique complex phenomenon and combination of styles has not yet been studied. This article is an attempt to fi ll the gap.
Indoor 3D Video Monitoring Using Multiple Kinect Depth-Cameras

This article describes the design and development of a system for remote indoor 3D monitoring using an undetermined number of Microsoft® Kinect sensors. In the proposed client-server system, the Kinect cameras can be connected to different computers, addressing this way the hardware limitation of one sensor per USB controller. The reason behind this limitation is the high bandwidth needed by the sensor, which becomes also an issue for the distributed system TCP/IP communications. Since traffic volume is too high, 3D data has to be compressed before it can be sent over the network. The solution consists in selfcoding the Kinect data into RGB images and then using a standard multimedia codec to compress color maps. Information from different sources is collected into a central client computer, where point clouds are transformed to reconstruct the scene in 3D. An algorithm is proposed to merge the skeletons detected locally by each Kinect conveniently, so that monitoring of people is robust to self and inter-user occlusions. Final skeletons are labeled and trajectories of every joint can be saved for event reconstruction or further analysis.
Web storage technology platform usage by faculty members in the faculty of education, Ahmadu Bello University, Zaria

This paper examined Web Storage Technology Platform usage by Faculty Members in the Faculty of Education, Ahmadu Bello University, Zaria. Two (2) objectives were identified for this paper which are; to identify the types of WST Platform the Faculty Members are aware of, to find out the extent to which Faculty Members utilise WST Platforms in the Faculty of Education, A.B.U., Zaria. Quantitative research methodology was employed and cross-sectional survey research design was used for this study. The population of the study is the whole 175 Faculty Members in the Faculty of Education, A. B.U., Zaria, excluding staff on sabbatical and staff on study leave. Self-developed questionnaire was used to collect data from the respondents. Descriptive statistics (frequency distribution, percentage, mean and standard deviation) was used to analyse the data collected from the respondents. This study discovered that, google cloud (google docs, gmail) was the most common WST Platform that Faculty Members are aware of. The study also revealed that Faculty Members most commonly utilise Google cloud (google docs, gmail) as the type of WST Platform in the Faculty of Education A.B.U., Zaria and partially utilise drop box. The researcher recommended that; the University management should put more efforts through the ICT Department/unit in creating awareness on different type of WST Platforms available for Faculty Members to utilise. Also, WST Platform service providers should provide more concise, clear and understandable steps to facilitate the high use of the platforms for academic activities.Keywords: Web Storage Technology Platform, Awareness, Usage, Faculty Members
Weighted certainty grids for dynamic search

Teams of manned and unmanned active sensors can provide tactical military units, search and rescue teams, and emergency response units with timely information; however, limited numbers of these systems mean their tasking must be prioritized, and the information they provide needs to be synthesized to avoid overwhelming users. Automated methods can fuse a <i>priori</i> and real-time information to provide decision-makers with time- critical situational awareness and a basis for search prioritization and route planning. Previous work has shown how expected entropic information gain can be used as a measure of utility in motion planning, though in multi- target search scenarios not all information is equally valuable. This research investigates generating certainty grids for dynamic search prioritization using a time-dependent cell valuation that incorporates entropy as well as threat- and geography-specific importance of information relative to the mission. We compare two different approaches to calculating posterior probability and entropy: a Bayesian log odds method based on prior works on obstacle avoidance; and a Dempster-Shafer Theory approach using a plausibility measure. The resulting weighted certainty grid map is provided for dynamic search. We then demonstrate how this adaptive, integrated situational awareness approach performs in different simulated, small unit tactical scenarios.
A Novel Visualization System of Using Augmented Reality in Knee Replacement Surgery: Enhanced Bidirectional Maximum CorrentropyAlgorithm

Image registration and alignment are the main limitations of augmented reality-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts.markerless image registration method was used for Augmented reality-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right to left direction and non-border occlusion from left to right direction.This study has improved video precision to 0.57 mm ∼ 0.61 mm alignment error. Furthermore, with the use of bidirectional points, i.e. Forwards and backwards directional cloud point, the iteration on image registration was decreased. This has led to improved the processing time as well. The processing time of video frames was improved to 7.4 ∼11.74 fps.It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed augmented reality system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels, etc. This article is protected by copyright. All rights reserved.
The application of machine learning to signal processing for detection and identification of signals of interest and anomalies

A Signal of Interest (SOI) is a signal that has been recorded for further analysis. This is driven by mission requirements for both known and anomaly signals. Identifying anomalies/SOIs is reliant on the system operator’s knowledge which can be prone to human error. The objective of our project is to improve situational awareness by automating the identification of SOIs with Machine Learning/Artificial Intelligence (ML/AI) techniques. In this paper, we describe a prototype developed and integrated into the tactical system that streams live Radio Frequency (RF) into our real-time Graphical User Interface (GUI) and implements an Artificial Neural Network (ANN) algorithm with the ability to predict potential anomalies/SOIs in real-time.
IMPROVING SPEAKING SKILL THROUGH STORYTELLING

This researcher is proposed to know storytelling could develop students speaking skill and there are some significance differences between before and after the implementation using the storytelling to develop the students‘ speaking skill. The respondent of the study was 22 students of the second grade of secondary school Catholic Sao Paulo II Laclo in the school year 2018. The method of this study used qualitative with descriptive analysis. The qualitative method was used to count the mean score of student pre-test and post- test. While the descriptive analysis was used to describe the result of students encountered during the pre-test without intervention of researcher during the implementation of storytelling to taught speaking and show was the mean score of post-test to be compared with the pre-test. The result of this research showed that mean score of pre-test of the speaking skill from before using storytelling was 4.6. This is meant that students did not understand using storytelling to develop speaking skill and students still got difficulties in speaking skill. And after teaching the speaking skill through the storytelling, the mean score posttest was 7.3. So, the significance differences applied to the formula of t test is 4.6 greater than t table is 2.074 through the degree of freedom is 22 which was applied to level of significance deference is 0.5. Finally, it concluded that using storytelling could develop students‘ speaking skill. Based on the result of the research that has been aforementioned it can be interpreted that there are some significance differences between before and after the implementation teaching speaking skill by using storytelling. And storytelling could be considered as the new strategy or a new useful method to be used by the English teachers to teach Speaking skill to the Second Grade Students Of Secondary School Catholic Sao Paulo II Laclo in the School Year 2018.
Accurate 3D Facial Geometry Prediction by Multi-Task, Multi-Modal, and Multi-Representation Landmark Refinement Network

This work focuses on complete 3D facial geometry prediction, including 3D facial alignment via 3D face modeling and face orientation estimation using the proposed multi-task, multi-modal, and multi-representation landmark refinement network (M$^3$-LRN). Our focus is on the important facial attributes, 3D landmarks, and we fully utilize their embedded information to guide 3D facial geometry learning. We first propose a multi-modal and multi-representation feature aggregation for landmark refinement. Next, we are the first to study 3DMM regression from sparse 3D landmarks and utilize multi-representation advantage to attain better geometry prediction. We attain the state of the art from extensive experiments on all tasks of learning 3D facial geometry. We closely validate contributions of each modality and representation. Our results are robust across cropped faces, underwater scenarios, and extreme poses. Specially we adopt only simple and widely used network operations in M$^3$-LRN and attain a near 20\% improvement on face orientation estimation over the current best performance. See our project page here.
Exploring the Organization and Framing of the Literature Review of English Language Education Study Program Theses

Literature reviews provide the literature that illustrates the common knowledge and the researchers’ stand in their research. The literature review is designed with suitable organization and framing so the readers can build their knowledge. However, only few studies on the organization and framing of a literature review. Therefore, the researcher wanted to conduct a study on English Language Education Study Program (ELESP) theses. There are two research questions in this research: “How are the literature reviews of the ELESP students’ theses organized?” and “How are the literature reviews of the ELESP students’ theses framed?” The goal of this research is to explore the organization and framing of the literature review of the ELESP students’ theses. This research used content analysis. The data were gathered from thirty theses in education and linguistic fields from batch 2016 to 2018. The instruments to gather the data were two structured observation sheets for the organization and framing. The result shows that for the organization, thirty theses used thematic organization, none of them used chronological organization, and combinations of organization existed. Regarding the framing, some elements, author, year, topic, and result, were always mentioned in every observed thesis.