Reviewerly/exHarmony huggingface dataset:
Automating a Part of the Assessment of a University using Excel

A process of establishing an assessment policy for a university and automating a part of the assessment is discussed. Excel was the tool used to carry out the automation. The form the assessment policy should take was discussed for three years, and was partially implemented in two months. We collected grades of all students of all classes from our university, using our academic affairs system. After the grade data were downloaded from the academic affairs system, we produced radar charts for each student, for each department, and for the university as a whole. Then, by clicking some buttons in the Excel book, the data were partitioned into the grade lists of all departments.
Target Tracking Based on Particle and Kalman Filter Combined with Neural Network

Wide variations of the maneuvering targets must be track by radar systems. One of the main problems is adaptation of the tracking filter to real behavior of the target. Combining real-time of Kalman filter with robustness of particle filter and neural network, proposes a method for target movement with non-linear motion. The perspective system of adaptive radar data processing for 2D tracking system based on particle filtering is described in this paper. The proposed system has better performance of state estimation (converges to true flight position) then linear Kalman filtering or non-linear adaptive Kalman filtering with Neural Network adaptation. The system can track or estimate object position in environment with interference and noise. The results of simulation show that the method can effectively improve the target tracking process. Final estimated track of object is given by coordinates represented by particles with the highest value of their weights. The modern system of adaptive radar data processing for 2D coordinate system based on particle filtering is designed in this paper.
Survey of Data Locality in Apache Hadoop

One of the key challenges in big data technology is the velocity at which the data is processed. Hadoop, an open-source software framework, is the dominant technology to support big data analytics. So, the researcher has tried to increase the performance of the Hadoop system. One of the Hadoop performance research is data locality. Recently, the data locality research receives attention to increasing the performance of Hadoop. Using the updated Hadoop software, the researchers can investigate data locality using the Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce, and other features. Data locality research has potential to increase performance of big data processing by scheduling, data placement framework and service. Here we introduced data locality in the Hadoop system including data-local, rack-local, and off-rack. We studied the data locality research such as scheduling, data placement, networking, partition/key, framework and so on. We categorized prior research using MapReduce and found some of this research overlapped some MapReduce steps. Also, we graphed the data locality research to identify trends. This analysis showed different effects depending on the applications. Specifically, the number of taskers and data locations affected performance of MapReduce. We also tested Terasort Benchmark and WordCount using CloudLab and physical environment to show the effect of data locality in Hadoop.
Optimization-based extrinsic calibration of a three-dimensional sensor composed of an array projector and a single camera

We previously presented a technique calibrating a multiaperture array projector (MAAP) for performing high-speed three-dimensional (3-D) surface topology measurements with aperiodic sinusoidal fringe structured illumination. Although a stereo-camera setup used to be required for triangulation, the presented MAAP calibration technique re-enables photogrammetric 3-D measurements to be made using a single CCD or CMOS camera. For such a monocular-view photogrammetric system, intrinsic camera calibration, intrinsic projector calibration, and extrinsic system calibration are vital to obtain high-measurement performance. However, there is currently no such technique that can comprehensively perform the required extrinsic system calibration when using an MAAP and a single-camera setup. A comprehensive optimization-based method is proposed and the resulting measurement performance is evaluated. With the new calibration technique, we are able to achieve a surface standard deviation of ∼50 μm.
Business-Science-Education: a Collaboration for Competitive and Sustainable Growth of the Wine Industry

Abstract Optimization of the collaboration “business-science-education” is a prerequisite for the increase of the competitiveness of the enterprises. The article introduces a scientific solution of a practical problem through the creation of a model for informal education of specialists in the wine industry. The presented models are a part of the Virtual Educational Space (VES) as a smart space, context-dependent, based on a script and a controlled infrastructure. The results of the approbation of the model prove its significance for the practical processes and the increase of the competency of the specialists in connection to the implementation of innovation. Particularly to the wine industry, the suggested model for informal education represents a specific innovation, which approbates the known “good practices” in the strategy “Lifelong Learning”.
An Analysis of Assessment Approaches and Maturity Scales used for Evaluation of Information Security and Cybersecurity User Awareness and Training Programs: A Scoping Review

This study aimed to ascertain the types of approaches that are applied in the assessments of information security and cybersecurity user awareness and training programs. The study focused on achieving two objectives. The first objective focused on to find out what measurements are used in the assessment of the effectiveness of information security and cybersecurity user awareness and training programs. The second objective focused on studies that made use of maturity models to measure the progress of these programs. A Scoping Literature Review process was followed to achieve these objectives. The study found that there is a gap in current literature with regards to the assessment of these programs, as only five papers and two maturity models focused on the assessment of these programs. The study further recommends that more studies be conducted in the assessment of these programs, as most researchers are encouraging the use of the programs in the fight against cyber-attacks.
Cyberbullies in Twitter: A focused review

This literature review focuses on recent techniques and studies regarding cyberbullying on Twitter. The purpose of these studies is to find smart and sophisticated ways and methods to detect these cyberbullying incidents and in case that it is not possible to fully eliminate them to provide the means to vastly reduce them. We present the theoretical roots of the term cyberbullying and we discuss thoroughly some influential studies to motivate new researchers that work in the general area of cybersecurity and privacy.
Identification of iron-bearing minerals based on HySpex hyperspectral remote sensing data

Our research group built a super-low altitude detection platform with power delta wings and mounted it with a HySpex hyperspectral sensor. This platform has great potential for extracting alteration information as it can provide high spectral and spatial resolution images. The aim of this study is to verify the reliability and accuracy of alteration information extraction using this platform. The seven-scene hyperspectral images of the Karatagh region in Hami of Xinjiang obtained by the platform in September 2017 were used to identify iron-bearing minerals. First, these images were preprocessed by splicing, atmospheric correction, and geometric correction. Second, the images were compared with the United States Geological Survey (USGS) standard spectra of similarities between the end-member spectra of goethite-, limonite-, and jarosite-altered minerals. Third, the three altered minerals were mapped in the test area based on the end-member spectrum. The results show that the correlations between the end-member spectra of three altered minerals, including goethite, limonite, and jarosite, obtained from the HySpex image and corresponding spectra in USGS reach 0.92873, 0.95098, and 0.8875, respectively. The end-member spectral reflectance values were higher than those in the spectral library. The original spectral data after the continuum-removal and the local quantitative display show that the spectrum at the feature absorption location was similar to the reference spectrum. On this basis, the distribution maps of goethite, limonite, and jarosite iron-stained altered minerals in the Hami Gobi area were produced and field verified. The data show that the information of identified minerals from the HySpex hyperspectral data were in accordance with the actual situation. Both methods can recognize jarosite well, the spectral angular mapping method is better than the back propagation (BP) neural network for goethite recognition, and the BP neural network of limonite recognition is better than the spectral angular mapping method. The above results prove that the alteration information can be effectively identified using the HySpex hyperspectral data integrated on a super-low altitude detection platform, which is of great significance to the rapid delineation of minerogenic prospects area.
Ant System for Face Detection

In previous studies, we have developed a face detection system. Even though, we have not been able to overcome the face searching. In addition, there are many possibilities for various facial positions. The sliding window technique that gained popularity as well-known techniques. It searches the face model in the entire image quickly by evaluating the information heuristic each window. In order to prevent the dissipation exploring of the non-face candidate, we propose an ant system which can choose routes based on heuristic information to optimize the sliding window technique in face detection. Ant colony algorithm and the Viola-Jones algorithm achieve an iteration average of up to 14150 and 19200. In addition, they both reach computational time up to 50.699 FPS and 50.358 FPS. Although the results are almost the same and the test data is limited, our method is worthy of being excellence
Evaluation of Transfer Learning for Human Activity Recognition Among Different Datasets

Human activity recognition is a potential area of research. For better performance, it requires significant amount of labelled data. Collecting labeled activity data is expensive and time-consuming. To solve this problem, transfer learning has been demonstrated very effective as it gathers knowledge from labeled train data of source domain and transfers that knowledge to target domain, which has little or no labeled data. In this paper, we propose unsupervised transfer learning from source dataset to target dataset, which are completely different in terms of number of users and samples. We have used Maximum Mean Discrepancy (MMD) based transfer learning model and compared with base Convolutional Neural Network (CNN) model. We have used 4 datasets for experiment. We have trained the model on a source dataset and then transferred the model to a target dataset, which has no labels to classify activities. We have found that transfer learning model has achieved better performance compared to the base model.
Application of Visual Servoing and Eye Tracking Glass in Human Robot Interaction: A case study

Human gazes reveal a lot of information about their intention, which could be used for intuitive Human Robot Interaction(HRI). Using gaze as a modality to interact with robots has huge value in increasing freedom for human. It has a strong application potential in different domains. In this paper, we propose a system for eye tracking based HRI with which a human can select an object by his/her gaze and a robot manipulator receives the intended object and waits for the confirmation from the human before it starts the grasping task. We use Image-based Visual Servoing (IBVS) to guide the robot manipulator to perform the grasping automatically which can also bring more robustness to the system. The first hand results indicate that the use of both visual servoing features and eye tracking technique can provide successful results in term of human robot interaction.
Adaptive Attention Model for Lidar Instance Segmentation

Detecting and categorizing the instances of objects using Lidar scans are of critical importance for highly autonomous vehicles, which are expected to safely and swiftly maneuver through complex urban streets without the intervention of human drivers. In contrast to recent detection-based approaches [6, 10], we formulate the problem as a point-wise segmentation problem and focus on improving the recognition of small objects, which is very challenging due to the low resolution of commercial Lidar systems. Specifically, we propose a novel end-to-end convolutional neural network (CNN) that encapsulates adaptive attention information, and achieve instance segmentation by fusing multiple auxiliary tasks. We examined our algorithms on the 2D projection data derived from KITTI 3D object detection dataset [8] and achieved at least 14.6% improvement in Intersection over Union (IoU) with faster inference time (25.3 ms per Lidar scan) than the state-of-the-art algorithms.
Aurum: A blockchain based decentralized video streaming platform

Aurum is a new media-streaming platform hosting user created channels having content authenticated through use of an innovative blockchain driven process. The media platform is fully decentralized, crypto token incentivized, and hosts verified content through application of trusted third party verification algorithms that fit the pre-defined characteristics of a particular content channel. Unlike other state-of-the art media curation platforms that use blockchain for transaction verification alone, Aurum mandates public content verification, making it a truly community driven model. The platform supports channel owners, creators, developers, authenticators, and subscribers through use of frictionless micro-payments, patronage, sponsorships and ad-revenue. By skipping intermediaries, returns get maximized. Aurum, as a decentralized platform, integrates peer-peer content sharing technology, eliminating the need for fixed infrastructure and associated costs while employing efficient support protocols for guaranteed QoS.
Multiagent Narrative Experience Management as Story Graph Pruning

In interactive narratives, experience management is used to control the world and the NPCs a player interacts with, encouraging particular types of stories or discouraging others. The space of all stories in a narrative can be understood as a story graph, with story states as nodes and actions in the story as directed edges. In this paper, we present experience management as a graph pruning problem. Starting with the full story graph, edges representing NPC actions may be pruned until there is at most one action per NPC per state. With the full graph available, the choice of what to prune may consider all possible futures, and we can ensure that undesirable stories are not reachable. By never pruning player actions, we ensure the player may make any choice and still be accommodated in the story. When this method was used to manage the story of an adventure game, players found our technique generally produced higher agency and more-believable NPC behaviors than a control. Finally, we discuss scaling the results of this method for practical use.
Applying Multiresolution Analysis to Vector Quantization Features for Face Recognition

In this paper, an approach of Facial Parts Detection (FPD) followed by the Discrete Wavelet Transform (DWT) in conjunction with Vector Quantization (VQ) algorithm for Facial Recognition (FR) are proposed. The FR system contains two modes: Training, and Classification. The proposed FR modes contain Preprocessing step followed by the Feature Extraction. The Classification mode yields the identification. The FPD detects nose, both eyes, and mouth for each pose in the Preprocessing step. Then, DWT is employed for each part that is detected for feature selection and data reduction. Thereafter, for further compaction and discrimination, the VQ, with the Kekre Fast Codebook Generation (KFCG) initialization method, is employed to form the final model that contains four feature groups per person. The DWT and VQ are utilized to reduce final feature dimensions without affecting discrimination. The recognition accuracy is calculated using the Euclidean distance. The four databases that are utilized to test the performance of the proposed FR system are: Georgia Tech, YALE, FEI, and FERET. The poses in these databases have various illumination conditions, face rotation, facial expressions, etc. The results, from which samples are presented here, of the FR system and other techniques are obtained and then examined using the Cross Validation based on K-fold method. The proposed FR is shown to improve the recognition accuracies while significantly reducing the storage requirements with comparable computational complexity.
Adversarially Learned Total Variability Embedding for Speaker Recognition with Random Digit Strings

Over the recent years, various research has been conducted to investigate methods for verifying users with a short randomized pass-phrase due to the increasing demand for voice-based authentication systems. In this paper, we propose a novel technique for extracting an i-vector-like feature based on an adversarially learned inference (ALI) model which summarizes the variability within the Gaussian mixture model (GMM) distribution through a nonlinear process. Analogous to the previously proposed variational autoencoder (VAE)-based feature extractor, the proposed ALI-based model is trained to generate the GMM supervector according to the maximum likelihood criterion given the Baum–Welch statistics of the input utterance. However, to prevent the potential loss of information caused by the Kullback–Leibler divergence (KL divergence) regularization adopted in the VAE-based model training, the newly proposed ALI-based feature extractor exploits a joint discriminator to ensure that the generated latent variable and the GMM supervector are more realistic. The proposed framework is compared with the conventional i-vector and VAE-based methods using the TIDIGITS dataset. Experimental results show that the proposed method can represent the uncertainty caused by the short duration better than the VAE-based method. Furthermore, the proposed approach has shown great performance when applied in association with the standard i-vector framework.
Non-Invasive Reverse Engineering of Finite State Machines Using Power Analysis and Boolean Satisfiability

In this paper, we present a non-invasive reverse engineering attack based on a novel approach that combines functional and power analysis to recover finite state machines from their synchronous sequential circuit implementations. The proposed technique formulates the machine exploration and state identification problem as a Boolean constraint satisfaction problem and solves it using a SMT (Satisfiability Modulo Theories) solver. It uses power measurements to achieve fast convergence. Experimental results using the LGSynth'91 benchmark suite show that the satisfiability-based approach is several times faster compared to existing techniques and can successfully recover 90%-100% of the transitions of a target machine.
SkipSJoin: A New Physical Design for Distributed Big Data Warehouses in Hadoop

Hadoop uses horizontal partitioning to improve the performance of a big data warehouse. A major challenge when horizontally partitioning the tables of a big data warehouse is to reduce network traffic for a given workload. A common technique to avoid this issue, when performing a join operation, is to co-partition the tables of the data warehouse on their join key. However, in the existing partitioning schemes, executing a star join operation in Hadoop still needs many MapReduce cycles. In this paper, we combine a data-driven and a workload-driven model to create a new scheme for distributed big data warehouses over Hadoop, called “SkipSJoin”. Our approach allows performing the star join operation in only one Spark stage, and allows skipping the loading of some unnecessary HDFS blocks. Our experiments show that our proposal outperforms some approaches in terms of query execution time.
Decision Support Systems Selection of Soang Superior Brood Using Weighted Product (WP) and Simple Additive Weighting (SAW) Method

Gurame Soang Latin name Osphronomus Labirynthici gouramy including fish, that as the fish have gills and breathing apparatus in the form of additional gills (Labyrinth). One important factor in fish farming Soang Gurame is holding elections. Superior brood has characteristics identical, to distinguish it needs to experience since it is quite difficult. Some of the assessment factors in selecting a superior brood that could be considered in the cultivation of the fish farmers Gurame Soang as ideal weight, physical disability, movement of fish, the shape of the head, and the body color. All of these factors combined to get an assessment that could help the decision making process fish Gurame Soang superior sires. This study used two methods that have selected the calculation accuracy of the decision-making process. The method used is the Weighted Product and Simple Additive weighting. The results of the research that has been done, it can be concluded the total number of alternative Gurame Soang superior sires data as many as 10 fish, and obtained the results of testing the level of accuracy by using the results of data from a breeder Gurame Soang superior sires, Method of Weighted Product got an accuracy of 80% and methods Simple Additive weighting got an accuracy of 60%.
Highlighting Techniques for 360° Video Virtual Reality and Their Immersive Authoring

Highlighting important elements is a fundamental task to guide the user’s attention in Virtual Reality (VR) applications. Besides the authoring process of a VR application, the creation of these cues for highlighting in 360° video VR already is a non-trivial task itself. It is even more challenging for laymen in the field of VR. This paper has three main contributions: (1) We investigate existing highlighting methods for VR scenes setting that are based on 3D models and explore on their suitability for a 360° video VR setting. We involve six highlighting methods. (2) We propose immersive authoring methods suitable for laymen to create these highlights within a 360° video VR application. (3) In a knowledge communication use-case that demonstrates a virtual laboratory, we evaluate the highlighting techniques and their authoring methods. In the user study we indicate that the “outlining” method is highly suitable for a 360° setting and that the according immersive authoring method we propose is appropriate for laymen authoring.