Reviewerly/exHarmony huggingface dataset:
Improving Implicit Stance Classification in Tweets Using Word and Sentence Embeddings

Argumentation Mining aims at finding components of arguments, as well as relations between them, in text. One of the largely unsolved problems is implicitness, where the text invites the reader to infer a missing component, such as the claim or a supporting statement. In the work of Wojatzki and Zesch (2016), an interesting implicitness problem is addressed on a Twitter data set. They showed that implicit stances toward a claim can be found with some success using just token and character n-grams. Using the same dataset, we show that results for this task can be improved using word and sentence embeddings, but that not all embedding variants perform alike. Specifically, we compare fastText, GloVe, and Universal Sentence Encoder (USE); and we find that, to our knowledge, USE yields state-of-the-art results for this task.
Bio-Medical Image Processing

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.
A simplified reflection-mode digital gradient sensing technique for measuring surface slopes, curvatures and topography

A simplified optical apparatus for reflection-mode Digital Gradient Sensing methodology (r-DGS) to perform full-field measurement of surface slopes is proposed. It eliminates the need for a beam splitter to record random speckles off a target and via the specular specimen normally. Instead, the target plane speckles reflected off the specimen surface are directly recorded at a known inclined angle. The simplification results in a highly efficient optical arrangement in terms of light loss intrinsic to the original apparatus that used a beam splitter. The simplified apparatus, however, requires altering the governing equations while interpreting the correlated speckle fields in the reference and deformed states. The details of these issues as well as successful demonstrations of simplified r-DGS to measure deflection of thin Si wafer subjected to flexure is reported. The modified approach is also demonstrated to study crack-tip deformations near an edge crack in a beam.
Preserving Data Privacy in Cloud using Homomorphic Encryption

Cloud computing these days, may be an elementary amendment happening within the space of data technology. It plays a vital role in terms of information storing and reducing the value to entrepreneurs. However, knowledge security continues to be one in all the highest issues for cloud computing. To produce the secure knowledge storage and retrieval, several techniques are projected however the bulk of them face some drawbacks that diminish the practicality of Cloud Computing. This work scrutinizes the Partial Homomorphic encoding that permits performing arts operations on encrypted knowledge while not compromising the encoding. The most aim is to propose a brand-new design to secure cloud computing, forestall security risks and improve the performance and also the time of information process.
Securing IoT applications with OP-TEE from hardware level OS

With ever increasing use of smart devices, and things connected to Internet the capabilities of these devices is also increasing. From a fridge tweeting about its contents to a microwave carrying out surveillance on the house all via internet. These capabilities come with problems only specific to IoT (Internet of Things), as computation ability is limited and in some cases the battery power too. In this work, we look at the applications, facilitation and architecture on which they operate. The challenges are briefly discussed and a solution to this called OPTEE is introduced. Architecture, applications and related techniques are presented. ARM processors had this TrustZone capability for a long time, given that not all processors today follow the standards OP-TEE was started. Open source tools like this are important in taking security to every platform. Many such opensource tools are available today and OP-TEE provides for an easily deployable solution on any target device forming TrustZones based on a secure monitor. Promising results were seen in work, making it clear that OPTEE is the future of securing IoT devices at a hardware level.
Research of Communication Between IoT Cloud Structure, Android Application and IoT Device Using TCP Sockets

This paper studies and describes communication between IoT cloud, Android mobile application and IoT embedded systems. It presents a way of transmitting temperature and humidity of the IoT cloud using TCP sockets. A JSON type data format is used between the cloud structure and the devices. Embedded systems STM32L4 discovery and Esp8266 are transmited data to IoT cloud using TCP sockets. Mobile application realizes the representation of temperature and humidity readings from a sensor. Architecture and software implementation of the experimental stage is also presented.
Exemplar based regular texture synthesis using LSTM

Exemplar based texture synthesis is an important technique for image processing and computer graph in texture mapping. So far, great achievements have been made in this field. However, both traditional and modern methods based on deep learning are making errors in synthesizing patterned texture due to the failure for catching the regularity of texture. To obtain a better synthesized result, a new framework for regular texture synthesis is proposed in this paper. Besides, we use recurrent neural network (RNN) of long-shot term memory (LSTM) to produce a regular texture based on exemplar. Our method can generate at any size texture without errors, which is an improvement for texture synthesis with deep learning techniques.Compared with traditional method as well as deep learning method, our method is obviously better in synthesizing regular texture.
Implementation of a Parking State Machine on Vision-Based Auto Parking Systems for Perpendicular Parking Scenarios

Recently autonomous parking has got a great attention from researchers from both Academy and Industry and being known as one of the key subsets of autonomous driving. Since autonomous driving is at its hype but with many remaining concerns, autonomous parking is more practical for daily applications and more feasible to be immediately realized mass-production in the automotive industry. Current solutions for autonomous parking require multiple maneuvers and a significant amount of time to park, which lead problematic in many real-life applications, depending on traffic situation around the ego-vehicle. In this paper, we present a parking strategy for vision-based autonomous parking systems in which the ego-vehicle is capable of completing its auto-park by one maneuver, or up to maximum three required maneuvers. Experiments show that the proposed method provides a significant improvement in reducing the number of maneuvers as well as time to park. Indeed, a rate of more than 95% successful parking in heavy traffic flows within a parking area is achieved using this method. We will also prove that the method is lightweight and very efficient for real-time applications.
An optimal sensor placement algorithm (O-SPA) for improving tracking precision of human activity in real-world healthcare systems

In this article, an Optimal Sensor Placement Algorithm (O-SPA) is designed for improving the precision of human activity. This placement algorithm is designed based on the fact of improving the Rate of Observation (RO) and sensing in the day-to-day activities of human. SPA for RO and sensing improves the analysis precision by accumulating and classifying useful sensor information. Useful sensor information is achieved by approximating the sensor data mitigating positioning errors. The analysis process differentiates useful and less preference information from the placed sensors to reduce complications in classification. This device positioning algorithm is more specific for the precision of data accumulation and manipulation by reducing sensing errors to improve the efficiency of healthcare systems The efficiency of healthcare systems with optimal sensor deployment is measured in terms of accuracy (98.9%), classification time (0.079s), replications (4.12), recall (96.29%), and error factor (0.000021). The efficiency results are the best compared to Hybrid sensing aided human activity recognition (HHAR), Multi-sensor fusion with ensemble pruning system (MSF-EP) and Activities of daily living Compressive sensing (ADL).
Which Refactoring Reduces Bug Rate?

We present a methodology to identify refactoring operations that reduce the bug rate in the code. The methodology is based on comparing the bug fixing rate in certain time windows before and after the refactoring. We analyzed 61,331 refactor commits from 1,531 large active GitHub projects. When comparing three-month windows, the bug rate is substantially reduced in 17% of the files of analyzed refactors, compared to 12% of the files in random commits. Within this group, implementing 'todo's provides the most benefits. Certain operations like reuse, upgrade, and using enum and namespaces are also especially beneficial.
Eliciting Expert Knowledge to Inform Training Design

Purpose: To determine the elicitation methodologies best placed to uncover and capture the expert operator’s reflective cognitive judgements in complex and dynamic military operating environments (e.g., explosive ordinance disposal) in order to develop the specification for a reflective eXplainable Artificial Intelligence (XAI) agent to support the training of domain novices. Approach: A bounded literature review of the latest developments in expert knowledge elicitation was undertaken to determine the ’art-of-the-possible’ in respects to uncovering an expert’s cognitive judgements in complex and dynamic environments. Candidate methodologies were systematically and critically reviewed in order to identify the most promising methodologies for uncovering expert situational awareness and metacognitive evaluations in pursuit of actionable threat mitigation strategies in high-risk contexts. Research outputs are synthesized into an interview protocol for eliciting and understanding the in-situ actions and decisions of experts in high-risk, complex operating environments. Practical implications: Trainees entering high-risk operating environments can benefit from exposure to expert reflective strategies whilst learning the trade. Typical operator training focuses on technical aspects of threat mitigation but often overlooks reflective self-evaluation. The present study represents an initial step towards determining the feasibility of designing a reflective XAI agent to augment the performance of trainees entering high-risk operations. Outputs of the expert knowledge elicitation protocol documented here shall be used to refine a theoretical framework of expert operator judgement, in order to determine decision support strategies of benefit to domain novices.
Overview of MV-HEVC prediction structures for light field video

Light field video is a promising technology for delivering the required six-degrees-of-freedom for natural content in virtual reality. Already existing multi-view coding (MVC) and multi-view plus depth (MVD) formats, such as MV-HEVC and 3D-HEVC, are the most conventional light field video coding solutions since they can compress video sequences captured simultaneously from multiple camera angles. 3D-HEVC treats a single view as a video sequence and the other sub-aperture views as gray-scale disparity (depth) maps. On the other hand, MV-HEVC treats each view as a separate video sequence, which allows the use of motion compensated algorithms similar to HEVC. While MV-HEVC and 3D-HEVC provide similar results, MV-HEVC does not require any disparity maps to be readily available, and it has a more straightforward implementation since it only uses syntax elements rather than additional prediction tools for inter-view prediction. However, there are many degrees of freedom in choosing an appropriate structure and it is currently still unknown which one is optimal for a given set of application requirements. In this work, various prediction structures for MV-HEVC are implemented and tested. The findings reveal the trade-off between compression gains, distortion and random access capabilities in MVHEVC light field video coding. The results give an overview of the most optimal solutions developed in the context of this work, and prediction structure algorithms proposed in state-of-the-art literature. This overview provides a useful benchmark for future development of light field video coding solutions.
Securing Machine Learning Engines in IoT Applications with Attribute-Based Encryption

Machine learning has been adopted widely to perform prediction and classification. Implementing machine learning increases security risks when computation process involves sensitive data on training and testing computations. We present a proposed system to protect machine learning engines in IoT environment without modifying internal machine learning architecture. Our proposed system is designed for passwordless and eliminated the third-party in executing machine learning transactions. To evaluate our a proposed system, we conduct experimental with machine learning transactions on IoT board and measure computation time each transaction. The experimental results show that our proposed system can address security issues on machine learning computation with low time consumption.
State space black-box modelling via Markov parameters based on evolving type-2 neural-fuzzy inference system for nonlinear multivariable dynamic systems

In this paper, an approach for state-space interval type-2 neural-fuzzy identification of multivariable dynamic systems, in an evolving and incremental learning context, is proposed. In combination with type-2 fuzzy systems, the adopted methodology regards the following aspects: recursive learning of the footprint of uncertainty, and recursive learning of the rule consequents part by a recursive linear state-space approaches where Markov parameters are incrementally and robustly learnt in sample-wise manner. The efficiency and applicability of the proposed methodology are demonstrated through experimental and computational results and compared with prominent learning algorithms of the literature.
Creative contextual dialog adaptation in an open world RPG

Role playing games rely typically on hand-written dialog that has no flexibility in adapting to the game state such as the level of the player. This is an even bigger problem for open world RPGs that make it possible to complete the game quests and objectives virtually in any given order. We present a computationally creative method for adapting Fallout 4 dialog to the changes in the game state using word embeddings for semantics and a BRNN for sequence-to-sequence paraphrasing of syntax.
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Cyclic codes are an interesting type of linear codes and have wide applications in communication and storage systems due to their efficient encoding and decoding algorithms. The objective of this paper is to investigate the parameters of some q-ary cyclic codes of length n=q2m−1q+1. The dimensions of these codes are settled and the lower bounds on their minimum distances are presented by employing the Hartmann-Tzeng bound or BCH bound. Moreover, when q=2,3, we give the largest coset leaders δ1 modulo n and investigate the parameters of the BCH codes C(q,n,δ1,1).
A Boosting based Adaptive Oversampling Technique for Treatment of Class Imbalance

The topic of class imbalance and its consequences have steered up the field of research for quite a long, as they bring pivotal impact over real-life scenarios such as medical disease diagnosis, fraud detection, etc. The typical solutions include data-level (undersampling or oversampling) or algorithmic-level (cost-sensitive learning) approaches. Synthetic Minority Oversampling Technique (SMOTE) has been acknowledged as one of the most effective data level solutions, but often suffers from the drawback of overfitting due to uniform oversampling rate. The ensemble learning techniques have recently emerged as effective; but can yield best results when integrated with data level solutions. In this work, a Boosting based oversampling technique is introduced with a customized oversampling rate, within an ensemble framework through cost-sensitive error formulation. The oversampling rate is tailored by using Local Covariance Matrix (LCM), while AdaBoost ensemble model with C4.5 weak learner is implemented as the ensemble framework. The work is compared with six benchmark techniques, for seven binary datasets. The experimental results prove the efficiency of the proposed work in treatment of imbalanced data.
Learning About Mobile Learning: Pre-Service Teachers’ Perspectives

This exploratory research project characterised the degree of adoption of mobile learning devices among 149 fourth year pre-service teachers (PST) studying education. It also examined the impact of perceptions about possibilities and constraints in the adoption of mobile devices in schools. The findings reveal that pre-service teachers were generally at the fourth of a six-stage continuum of technological adoption that can be characterized as familiarity and confidence which was one level higher than academic staff. Some of those constraints appear to reside on equity issues, lack of mobile learning support resources, pedagogically unproductive use of mobile tools and student distractedness. Gender did not have a major impact on participants’ opinions but access to a mobile tablet did.
Designing Classifier For Human Activity Recognition Using Artificial Neural Network

Human Activity Recognition (HAR) using built-in sensors in a mobile phone is a very active research area to infer the human daily activities in recent years. The machine-learning algorithm is an important role to determine human activities. Although Artificial Neural Network (ANN) is effective classifier to determine the human activity recognition, its performance mainly depends on selection of hidden neuron nodes. In this study, we design a classifier using Multi-Layer Perceptron (MLP) for human activity recognition using UCIHAR dataset. The study introduces a new method to find the hidden neuron nodes for ANN classifier to achieve a better performance of HAR. In this scenario, we also consider the optimal hidden layers and learning rate of ANN after trained the HAR dataset. The simulation result of proposed method 98.32% accuracy is that the proposed model is a better classifier with minimal errors and fixation of hidden neurons for human activity recognition process.
Experimental performance evaluation of various path planning algorithms for obstacle avoidance in UAVs

This paper presents the experimental results of the path generated by a few widely accepted path planning algorithms used for stationary obstacle avoidance in UAVs. The algorithms evaluated were Dijkstra, A*, Artificial Potential Field planning, RRT and RRT*. The evaluation parameters for these algorithms were the length of the path, maximum deviation angle, and the clearance between the path and the obstacles.
Load balancing in cloud computing using water wave algorithm

Summary Cloud computing as of late is a rising innovation for giving various administrations through internet to fulfil the requisites of clients dependent on needs with minimal expense. The client can benefit all kind of administration without building, observing, and keeping up the assets. Researchers developed distinctive asset planning calculation to satisfy the client requisites as per their condition. This article proposes a Water Wave Algorithm (WWA) for resource scheduling in cloud environment. The results for throughput, Response time, Turnaround time, Migration time, Resource utilization, Fault tolerance, and scalability over FCFS, MCT, MET, and OLB algorithms and the results prove that WWA provides better results for throughput, response time, resource utilization, and scalability.
Porphyry Copper Potential of the United States Southern Basin and Range Using ASTER Data Integrated with Geochemical and Geologic Datasets to Assess Potential Near-Surface Deposits in Well-Explored Permissive Tracts

Abstract ArcGIS was used to spatially assess and rank potential porphyry copper deposits using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data together with geochemical and geologic datasets in order to estimate undiscovered deposits in the southern Basin and Range Province in the southwestern United States. The assessment was done using a traditional expert opinion three-part method and a prospectivity model developed using weights of evidence and logistic regression techniques to determine if ASTER data integrated with other geologic datasets can be used to find additional areas of prospectivity in well-explored permissive tracts. ASTER hydrothermal alteration data were expressed as 457 alteration polygons defined from a low-pass filtered alteration density map of combined argillic, phyllic, and propylitic rock units. Sediment stream samples were plotted as map grid data and used as spatial information in ASTER polygons. Gravity and magnetic data were also used to define basins greater than 1 km in depth. Each ASTER alteration polygon was ranked for porphyry copper potential using alteration types, spatial amounts of alteration, stream sediment geochemistry, lithology, polygon shape, proximity to other alteration polygons, and deposit and prospects data. Permissive tracts defined for the assessment in the southern Basin and Range Province include the Laramide Northwest, Laramide Southeast, Jurassic, and Tertiary tracts. Expert opinion estimates using the three-part assessment method resulted in a mean estimate of 17 undiscovered porphyry copper deposits, whereas the prospectivity modeling predicted a mean estimate of nine undiscovered deposits. In the well-explored Laramide Southeast tract, which contains the most deposits and has been explored for over 100 years, an average of 4.3 undiscovered deposits was estimated using ASTER alteration polygon data versus 2.8 undiscovered deposits without ASTER data. The Tertiary tract, which contains the largest number of ASTER alteration polygons not associated with known Tertiary deposits, was predicted to contain the most undiscovered resources in the southern Basin and Range Province.
A Bayesian nonparametric approach to log-concave density estimation

The estimation of a log-concave density on R is a canonical problem in the area of shape-constrained nonparametric inference.We present a Bayesian nonparametric approach to this problem based on an exponentiated Dirichlet process mixture prior and show that the posterior distribution converges to the log-concave truth at the (near-) minimax rate in Hellinger distance.Our proof proceeds by establishing a general contraction result based on the log-concave maximum likelihood estimator that prevents the need for further metric entropy calculations.We further present computationally more feasible approximations and both an empirical and hierarchical Bayes approach.All priors are illustrated numerically via simulations.