Reviewerly/exHarmony huggingface dataset:
Equipping Teacher Educators for Digital Teaching and Learning

Considering that teachers are central to good education and teacher educators are central to good teacher education, it is logical that due care must be taken to equip teacher educators for digital teaching and learning. In fact, continuing professional development of teacher educators in terms of digital teaching and learning is a necessity of our times. Extending these arguments, the chapter, that is mainly based on the review and analysis of policy documents and practices as well as other available literature and statistics related to teacher educators, begins with discussions on role and importance of teacher educators, details the need and promises of preparing teacher educators for digital teaching and learning, delves upon practices of and challenges before teacher educators to master digital teaching and learning, and ends with presenting innovative strategies to empower teacher educators for the world of digital teaching and learning.
Battery Monitoring and Energy Forecasting for an Off-Grid Solar Photovoltaic Installation

This document details the design and implementation for an off-grid PV energy management system with key objectives being battery monitoring and energy forecasting. Coulomb counting is used to estimate the state of charge of a 24 V, 100 Ah lead-acid battery bank. An ACS712 hall effect current sensor module is used to implement the coulomb counter. A random forest regression model is used to predict solar irradiance with a root mean square error of 15.4%, when comparing actual Johannesburg irradiance data to predicted data. Energy forecasting is successfully done with a root mean square error of 6.2% for a three-day continuous test. It is concluded that the proposed solution successfully demonstrates a working principle for an energy management system, however, further improvements are needed to make the forecast model as accurate as possible.
How do you Perceive Differently from an AI — A Database for Semantic Distortion Measurement

Artificial intelligence (AI) is enabling the automated analysis of large amounts of image/video data, boosting the speed of multimedia data processing remarkably. Meanwhile, Image Quality Assessment (IQA) plays an important role in developing automatic analysis methods. To ensure the effectiveness of AI, images in multimedia applications should be considered for visual examination by both human and machine. Therefore, it is significant to understand the differences between human's and AI's perception of semantic distortion. However, little work has been done due to the lack of data from human on the semantic level. In this paper, we first propose a semantic database (SID) based on the surveillance scenarios, by collecting subjective average recognition rates of 3 semantic targets (face, pedestrian, license plate) with 3 types of distortion (JPEG Compression, BPG Compression, Motion Blur). Then, we present a detailed analysis of how human and AI perceive semantic distortion differently. Experimental results show that AI is stronger in tolerance to distortion than human beings on average, while weaker at generalization and stability. It is also implied in the experiments that existing IQA methods are not effective enough at judging the semantic distortion.
Preselection of documents for personalized recommendations of job postings based on word embeddings

In the search for matching jobs, more and more people rely on online services such as job search engines. Job search engines provide the possibilities of searching for certain keywords and maintaining domain related filters like the location or the seniority of a job posting. A job recommendation system can support the users on such platforms by finding relevant jobs that match their profile. When it comes to job postings, the platform often has no information about whether a user actually applied for a certain job or whether the application was successful. In this paper, we propose a method to use the implicit information that users provide on the platform to recommend matching job postings in real time. We provide a solution by applying the doc2vec method on the job descriptions to cluster them. This allows us to preselect certain job postings and reduce the target space to implement a personalized classifier for recommendation. Both the quality of recommendations and the runtime of the according algorithms are improved. Our evaluation with domain experts shows, that at least 55% of these recommendations are relevant to the respective user.
Speeding-Up P-256 ECDSA Verification on x86-64 Servers

ECDSA is a frequently used signature scheme that has attracted a great deal of software and hardware optimization efforts. In particular, the NIST P-256 curve is currently used for most of the TLS communication worldwide. This paper proposes some observations that lead to additional optimizations. The ECDSA verification includes two main bottlenecks: (a) modular inversion (modulo the group order); (b) two scalar-point multiplications on the underlying elliptic curve (the points are the group's generator and the signer's public key). One observation is that when one point is multiplied by more than one scalar, the multiplications can be accelerated by precomputing some intermediate values. For example, OpenSSL stores a hard-coded multiplication table for the generator of the NIST P-256 curve. Another observation leveraged here is the fact that ECDSA verification uses only public information, and therefore side channel mitigation techniques are not necessary. We show how these observations lead to a speedup of 3.4× over the NIST P-256 ECDSA verification of OpenSSL [1], using ~150 Kb of additional memory space. Some of the optimizations that are offered here, have been recently integrated into BoringSSL [2], [3] achieving a 1.15× speedup over its previous implementation.
Adaptive Prediction Methods for Medical Image/Video compression for Telemedicine Application

Due to rapid development of multimedia communication and advancement of image acquisition process, there is a crucial requirement of high storage and compression techniques to mitigate high data rate with limited bandwidth scenario for telemedicine application. Lossless compression is one of the challenging tasks in applications like medical, space, and aerial imaging field. Apart from achieving high compression ratio, in these mentioned applications there is a need to maintain the original imaging quality along with fast and adequate processing. Predictive coding was introduced to remove spatial redundancy. The accuracy of predictive coding is based on the choice of effective and adaptive predictor which is responsible for removing spatial redundancy. Medical images like computed tomography (CT) and magnetic resonance imaging (MRI) consume huge storage and utilize maximum available bandwidth. To overcome these inherent challenges, the authors have reviewed various adaptive predictors and it has been compared with existing JPEG and JPEG LS-based linear prediction technique for medical images.
Enhancing edge-based image descriptor models through colour unification

The lack of suitable robust appearance models hinders the performance of most image descriptors. Descriptors often rely on pieces of information in images called image features to discriminate the contents of images. Most successful descriptors use gradient images for determining the overall shapes of objects. Consequently, the inferred features are often susceptible to the noise caused by shadows, reflections and inner textures within the object. Significant efforts have been made towards improving the performance of image classifiers, yet generic object detection remains an open problem. In this paper, a method aimed at improving existing appearance models is proposed. The focus is on enhancing the acquired information at fundamental stages to improve the robustness of common statistical learning classifiers, as seen with the work of Holger Winnemoller et al. with human subjects. The selective Gaussian blur filter was applied to several human classification datasets to reduce the amount of ambiguous low-frequency noise. Experiments were then conducted to determine whether the unification of similar colours in local image regions could improve the acquired image features. The classification results that were obtained with the processed images were competitive to the results obtained with the original images, however inconclusive for demonstrating the benefits of image smoothing.
Modeling on clinical group decision support system for screening and working diagnosis acute respiratory infections

There are cases of patients with Acute Respiratory Infection (ARI) which are not handled quickly and appropriately at emergency department (ED), thus there should be a medium that can assist medical personnel in making decisions. This research proposes the development model of Clinical Group Decision Support Systems (CGDSS) named KLISPA models. KLISPA model can accommodate a screening process to the working diagnosis process of ARI by integrating Case Based Reasoning (CBR) and Groups Decision Support System (GDSS). Screening process completed by CBR using Nearest Neighbor method. This process helps medical personnel such as nurses, co asst doctors, and general practitioners to do screening to patients for determining the early solution to the disease. Meanwhile, the working diagnosis solved by GDSS uses the methods of Eckenrode, Extended TOPSIS and BoostVote. This process accommodates the needs of joint decision-making which involves groups of Decision Maker (DM) consisting of pediatrician, child lung specialist doctor, and the radiologist doctor.
The Governance of Digital Forensic Investigation in Law Enforcement Agencies

The volume of data for cybercrime investigation keeps growing at unprecedented rates and creating a quandary for law enforcement agencies. This brings a great challenge for law enforcement agencies. It requires the sincere examination of all available data volumes at crime scene or in lab to present digital evidence in a court of law. In order to maintain the integrity and validity of digital evidence, investigators must establish a process model that can provide a quick response at scene. This paper illustrates the novel TEAR-phase application of THOR dimensions to digital forensics. It facilitates the efficiency and effectiveness of constructing a clear investigation.
Exploring lexico-semantic patterns for aspect-based sentiment analysis

Web 2.0 has caused a boom in user-generated content, which contains a lot of valuable information. Analysis of these natural language data requires advanced machine learning techniques. This research focuses on determining aspect-based sentiment in consumer reviews using lexico-semantic patterns. We propose a method using a Support Vector Machine with 6 different pattern classes: lexical, syntactical, semantic, sentiment, hybrid, and surface. We show that several of these patterns, including synset bigram, negator-POS bigram, and POS bigram, can be used to better determine the aspect-based sentiment, using two widely used real-world data sets on consumer reviews. Our approach achieves 69.0% and 73.1% F1 score for the two data sets, respectively, an increase of 15.3% and 16.1% respectively compared to the considered baseline.
Cryptographic Formula Obfuscation

Under intractability assumptions commonly used in cryptography, we show an efficient program obfuscator for large classes of programs, including any arbitrary monotone formula over statements expressed as equalities to a secret. Previously, only a handful set of individual functions were known to have such program obfuscators. This result has both theoretical and practical relevance. On the theoretical side, it significantly increases the class of functions that are known to have a cryptographically secure program obfuscator, and it shows that general-purpose program obfuscation results do exist with at least some level of generality, despite the likely impossibility, proved in [2], to achieve a related notion of obfuscation for any arbitrary polynomial-time program. On the practical side, there are many computational programs that can be expressed as monotone formulae over equality statements, and can now be securely obfuscated. Our most foundational contribution is a new type of obfuscation: protecting the privacy of the formula gates, and thus of much of the computation carried out by the program, in addition to the privacy of secrets used by the program. Previous program obfuscators only targeted the privacy of secrets used by the program.
Environment for Planning Unmanned Aerial Vehicles Operations

Planning and executing missions in terms of trajectory generation are challenging problems in the operational phase of unmanned aerial vehicles (UAVs) lifecycle. The growing adoption of UAVs in several civil applications requires the definition of precise procedures and tools to safely manage UAV missions that may involve flight over populated areas. The paper aims at providing a contribution toward the definition of a reliable environment, called FLIP (flight planner) for route planning and risk evaluation in the framework of mini- and micro-UAV missions over populated areas. The environment represents a decision support system (DSS) for UAV operators and other decision makers, like airports authorities and aviation agencies. A new ICT tool integrating an innovative procedure for evaluating the risk related to the use of UAV over populated areas is proposed.
As If I Am There

We introduce a novel video chat interface with a 360° photo as background in order to offer richer contextual and background information. We conduct a preliminary user evaluation in a lab setting. Paired participants were randomly assigned to two conditions, using regular interface or 360° photo interface. Each pair video chatted in pairs, then completed a post-study survey and answered several questions about their experience. Participants reported to have less behavioral interdependence and more inclusion using 360° photo video chat interface. They also reported having strong interest in employing it in long-distance intimate relationship, and made some suggestions for design iterations.
Presenting a Simple Method of Brain Shift Estimation for Neuronavigations and Considering its Practicality

In neurosurgeries, even a small mistake may pose a serious risk to a patient. Hence, a neuronavigation has been used in neurosurgeries to find accurate spatial position of lesion. However, the neuronavigation localizes the lesion based on preoperative medical images, therefore the brain deformation during a surgery, which is referred to as brain shift, has been a serious problem in the neuronavigation. Brain shift occurs in craniotomy and it is caused by gravity, cerebrospinal fluid (CSF) leakage, and so on. In order to present accurate information on a neuronavigation during surgery, a simple brain shift estimation method is proposed to predict and reproduce intraoperative brain deformation. The proposed method is validated by using a Finite Element (FE) brain model generated from medical images of a real patient. A brain shift simulation using a patient brain model is conducted. The comparison of the simulation result and the intraoperative medical images of the patient show that the simulation can reproduce the brain shift accurately. Most earlier studies have verified their brain shift estimation methods in 2D coordinate by using the 2D medical images. In this study, the accuracy was verified in x-y-z coordinate by using the 3D models.
Advanced template matching prediction using a motion boundary

In this paper, an advanced template matching prediction is proposed to improve the coding efficiency gain. Conventional template matching prediction in joint exploration model (JEM) finds a motion vector for the current coding block by using adjacent template in reconstructed region although some coding blocks may have complicated motion coming from two moving objects or backgrounds. In these case, conventional template matching prediction mode could not be efficient. In order to overcome the complicated motion problem, we propose a new template matching prediction which applies two motion vectors to predict the coding block. In the proposed template matching prediction, a split mechanism is used to divide the coding block along the motion boundary. The proposed scheme is implemented on the JEM reference software 6.0 version and shows that maximum coding efficiency gain is up to 0.82% in luminance component.
Measurement of asymmetrical homogeneous object by using chain code algorithm and virtual center of gravity

Current methods of measurement of asymmetrical homogenous objects still largely involve various kinds of experimental analysis that are complicated and time-consuming. One of the experiments to measure an object's center of mass is carried out by hanging the object by using pieces of rope in different positions. The point where the rope meet is taken as the object's center of mass and weight. This research is purposed to develop a system to measure area, center of mass, and center of weight of an asymmetrically-shaped object by using digital image processing. The process is initiated by doing a pre-processing method that is purposed to increase the image quality. Then, a method of feature extraction of edge detection and chain code algorithm are used to measure the peripheral size and area of each side of the object. The result and analysis of the area are used to determine the object's center of mass and weight. This digital application will determine the virtual center of mass and center of gravity that are needed to measure an asymmetrically-shaped object in a real-time mode. This application can also be used to measure an object indirectly if the object is not present physically.
Spoken Letter Recognition using Deep Convolutional Neural Networks on Sparse and Dissimilar Data

The applications of Machine Learning and Neural Networks (NN) are nearly unlimited and the application of artificial intelligence has gained much interest in recent years, because of their great performance in various tasks. Deep Convolutional Neural Networks (CNNs) are a state-of-the-art technique for visual recognition in image- and video data. However, the application range of a specific CNN is very limited, because the CNN is adapted for a specific task with an exclusive dataset for training. It needs to be rebuilt from scratch when the input- or output parameters are just slightly changing, including the collection of a new dataset for training. To reduce those cost and time expensive issues, transfer learning can be beneficial, where the outcome of an already pre-trained Neural Network, the source data, is fitted to the target dataset of a new task. In the case of object recognition, there are several use cases where pre-trained Deep CNNs are applied. But those Deep CNNs can not only be used for visual recognition. In this work the approach is made, to use transfer learning on DCNNs for spoken letter recognition, although the target data is very dissimilar from the source data, to show the range of application for transfer learning. Moreover, this application is trained with a very small dataset.
Optimal Paths for a Mobile Manipulator using the Leapfrog Method

This paper deals with finding optimal paths for a mobile manipulator in the presence of obstacles. The motion planning of the robot kinematic system is formulated as an optimal control problem. Applying Pontryagin's minimum principle, indirect conditions of optimality are derived for the optimal motion planning problem and solved numerically using the Leapfrog method. Simulation results for the mobile manipulator are presented to demonstrate the effectiveness of the proposed method.
Game Based SBCA Cloud Resource Adjustment Method

The performance of SBCA (SOA Based Cloud Applications, a typical form of cloud applications) can be adjusted by changing the occupied amount of resources. How to adjust the amount of resources occupied by SBCA that can meet the SLA constraints with the minimum resource adjustment cost has become the focus of cloud computing research area. In the process of SBCA resource adjustment, SBCA providers and resource providers both want to choose the adjustment plan with maximizing profit for themselves, so there has a conflict of interest between the two providers. Aiming at the problem, this paper proposes a game based SBCA cloud resource adjustment method. The method presents a game based representation model of SBCA cloud resource adjustment problem, gives the utility calculation method of both sides of the game, and solves the problem by using fireworks algorithm. The experiments show the effectiveness of the proposed method.
New estimation in mixture of experts models using the Pearson type VII distribution

Assuming that the error terms follow a Pearson type VII distribution, we propose a new estimation in mixture of experts models by adding an l2−norm of the regression coefficients of the mixing proportions to the log-likelihood function. This l2-penalized maximum likelihood estimator is a root-n consistent estimator of the true parameter vector, and its finite sample behaviour is better than that of the ordinary maximum likelihood estimator. An efficient EM algorithm is suggested for the inference, and the methodology is illustrated through some simulation and comparison studies. An application of the proposed method to a data set is described.
Variable- and Fixed-Length Balanced Runlength-Limited Codes Based on a Knuth-Like Balancing Method

A novel Knuth-like balancing method for runlength-limited words is presented, which forms the basis of new variable- and fixed-length balanced runlength-limited codes that improve on the code rate as compared to balanced runlength-limited codes based on Knuth's original balancing procedure developed by Immink et al. While Knuth's original balancing procedure, as incorporated by Immink et al., requires the inversion of each bit one at a time, our balancing procedure only inverts the runs as a whole one at a time. The advantage of this approach is that the number of possible inversion points, which needs to be encoded by a redundancy-contributing prefix/suffix, is reduced, thereby allowing a better code rate to be achieved. Furthermore, this balancing method also allows for runlength violating markers which improve, in a number of respects, on the optimal such markers based on Knuth's original balancing method.
Learning Based Hybrid No-Reference Video Quality Assessment of Compressed Videos

A near real-time no-reference video quality assessment method is proposed for videos encoded by H.264/AVC codec. A fully connected neural network is trained with features extracted from both bit-stream and pixel domains along with their respective subjective quality scores. Feature selection procedure is designed in a manner to address spatial as well as temporal artifacts of the encoded sequences, while minimizing the overall run-time in order to adapt this work to applications in live streaming. The performance of our method is verified by applying it on H.264-encoded sequences from the LIVE video dataset and the correlation with the differential mean opinion scores (DMOS) from the subjective tests are presented. Our framework outperforms widely-used NR VQA methods and a number of state-of-art full-reference VQA methods.