Reviewerly/exHarmony huggingface dataset:
Treading water: why organisations are making no progress on cyber security

Business leaders are acutely aware of the importance of cyber security and recognise the pressing need to address it, according to a recent report. 1 However, many of the organisations surveyed as part of the research are failing to make any headway in their efforts to protect their data, infrastructure, assets, people and customers from cyber attacks.
Fog computing context analytics

Developments in measurement science have enhanced the quality of measurement procedures, improving their efficiency and increasing their accuracy [1]. The implementation of intelligent and adaptive distributed systems using the Internet of Things (IoT) has recently seen a rapid increase. The synergy of these developments requires an extensive use of sensing systems that can measure, analyze and provide accurate results in real-time [2]. However, an increase in the number of sensors leads to challenges such as a necessity for complex processing strategies to handle multivariate data [3]. It also provides opportunities for analyzing multivariate, sensor-generated data to infer contextual knowledge that can be useful in decision making. Multivariate context information can offset the ambiguities that arise when single sensor data are used for inference and can uncover relationships previously unknown or difficult to confirm through conventional approaches [4].
Relevance Vector Machine Optimization in Automatic Text Summarization

Abstract This study aims at optimizing the Relevance Vector Machine (RVM) algorithm in automatic text summarization. This research begins by studying various studies on automatic text summarization to find out what features are commonly used in the automatic text summarization process. Each feature value will be calculated as a correlation with target. The composition of features is determined by obtained correlation value, when correlation value between features and targets is grater, the feature will take precedence. The results in this study are obtained by using 4 or 6 features that obtains highest accuracy, which is 55.84%. The conclusion of this study is that the correlation coefficient can be used to determine the order of extraction features.
A comparison of continuous genetic algorithm and particle swarm optimization in parameter estimation of Gompertz growth model

Genetic algorithm and Particle Swarm Optimization are heuristic optimization methods inspired by genetic principles and swarm behavior phenomena, respectively. Those two methods are initiated by random generation of initial populations (initial solutions), fitness evaluation of every solution, solution updating until a termination condition are met. It is well known that those two methods are not always converge to an optimal solution. Those methods sometimes converge to suboptimal solutions, solution near the optimal solution. In this paper, continuous genetic algorithm and particle swarm optimization were implemented to estimate parameters in the Gompertz growth model from rooster weight data cited from literature. Although the best results of the two models were not significantly differs, we found that the particle swarm optimization method was more robust than the continuous genetic algorithm. Hence, the particle swarm optimization method is more recommended than the continuous genetic algorithm.
Processing of association rules with ontology in distributed NoSQL systems

Nowadays, many NoSQL systems are developed to deal with data elasticity in distributed environments. This is very useful for Data mining such as association rules technique which generates a huge number of rules. To avoid any manual post-processing for selecting the interesting rules, many research ers suggest integrating expert users’ knowledge by using ontology and rule patterns. Nevertheless, with NoSQL Big Data that contain very large data, the number of generated rules is so huge that any post-processing becomes complicated especially in industrial areas. Also, any solution and results have to be tested and checked with a real Big Data context. In order to deal with this issue, we use an adjusted approach with ontology and rule patterns to reduce database NoSQL context before generating any rule. After that, we conduct a real experiment on distributed industrial MongoDB database to calculate execution time and generated rules. This work proves the gain in performance for using association rules with ontology in the NoSQL systems.
Hypergrid: A Hyper-Fast ROS-Based Framework for Local Map Generation

One of the main tasks of an autonomous vehicle is being able to navigate from one point to another, while constantly perceiving and considering every element in the environment. Path planning techniques are divided into global and local planning, being the latter the ones responsible for tracking the dynamic obstacles presented around the vehicle. These methods often rely on the occupancy grid map (OGM) data structure to represent the environment due to its low complexity, which the Robotic Operating System (ROS) provides through the costmap_2d package in the navigation stack. Although this package is widely used in the robotics community, it struggles to keep up with the information provided by a multisensor setup from an autonomous vehicle, which provides a large amount of three-dimensional data. For this reason, this paper presents Hypergrid, a ROS-based framework with GPU and CPU acceleration to process raw sensor data and generate local OGMs useful for navigation tasks. Furthermore, real-world experiments on different autonomous driving platforms are performed, in order to validate the proposed approach and compare it with the performance of costmap_2d. The source code of Hypergrid is available in github.com/lsi-uc3m/hypergrid under GPLv3 license.
Exploration and Practice of Informatization Means in the Quality Supervision of College Classroom Teaching

Abstract Constructing the monitoring system of teaching quality in colleges and universities is an effective and effective means of reforming higher education. The monitoring of classroom teaching is an important part of the monitoring system of teaching quality in colleges and universities. In this paper, the artificial intelligence technology at the forefront of information technology in recent years is applied to the classroom teaching monitoring in colleges and universities. From pre-class attendance to classroom state, information and means are used to monitor students and teachers in all aspects. Using machine learning related algorithms to analyze attendance and classroom state data and final grades, and improve the quality of classroom teaching quality monitoring. This expectation provides a certain reference for China’s higher education reform.
Fault Diagnosis of Railway Turnout Based on Fuzzy Cognitive Map

Turnout is an important part of the railway signal system and also an important equipment to ensure the safety of traffic. In view of the current situation of low efficiency of using computer monitoring system to locate faults, this paper combines the theory of fuzzy cognitive map with the fault diagnosis of the turnout, and uses real-coded genetic algorithm which tells the initial weight of the network to construct a classifier model for fuzzy cognitive map to classify the faults of turnouts. The simulation experiments show that fuzzy cognitive map classifier model based on real-coded genetic algorithm can effectively classify the faults of the turnout. Compared with the commonly used classifiers such as BP and SVM, it can achieve better classification performance.
Timed-Release Encryption with Master Time Bound Key

Timed-release encryption allows senders to send a message to a receiver which cannot decrypt until a server releases a time bound key at the release time. The release time usually supposed to be known to the receiver, the ciphertext therefore cannot be decrypted if the release time is lost. We solve this problem in this paper by having a master time bound key which can replace the time bound key of any release time. We first present security models of the timed-release encryption with master time bound key. We present a provably secure construction based on the Weil pairing.
Information Technology for Japanese Learning

Abstract The purpose of this study is to find out the use of social media for Japanese language learning. The study used a survey method for 24 students of Japanese Program of Universitas Komputer Indonesia. The results show that social media has a great potential to be used as an effective medium of Japanese language learning for learning or improving the ability of Japanese language because the average level of usage time is quite high. This study discusses how often social media is used. This shows that social media is one of the effective media for learning Japanese. This learning technique is expected to help make it easier for students in the process of learning and understanding Japanese language.
Unified Symbol Framework to Improve UI Comprehension

The objective of this research is to develop a method to serve a generic and unified set of symbology (or words) to software UI elements where each symbol is backed by multi-view explanations to support comprehension. The problem of selecting appropriate symbols and words for the UI is solved by reusable components that are connected to a dictionary of common symbols and words. It is unique from other approaches such that the meaning of symbols is explained by a set of pictures that provide multiple explanations of the context, in other words, Multi-view Symbols. The method is realized by an online dictionary of Multi-view Symbols and a UI framework that provide seamless on-demand access to the dictionary. The design and implementation of the symbol and dictionary system is described and an example of embedding it into a client application is provided.
Mineral Resources classification of a nickel laterite deposit: Comparison between conditional simulations and specific areas

Classification of Mineral Resources as Measured, Indicated, or Inferred depends on the level of confidence the resource geologist has in the estimation of the deposit.This is based on different factors such as the geological or geometrical model, the sampling quality and, from the geostatistical point of view, the distance between drill-holes.However, many methods or criteria used for classification, geometrical ones for instance, are not based on an actual measure of uncertainty.In the present case, which corresponds to a nickel laterite deposit studied in two dimensions, Mineral Resources are classified based on the drilling mesh, and associated probabilities that nominal productions do not deviate from estimations by more than 15%.In this paper we present two methods to assess such probabilities: conditional simulations and the specific areas method.Both methods include the drilling mesh and the spatial variability as principal components for classification and both yield similar results, which allows the validation of one with the other.Benefits and limitations of these two methods are also given.Simulations are timeconsuming, but they are the most accurate; specific areas are time-saving and less restrictive for testing several drilling meshes, but the results are more approximate.
The Development of a Forum Mobile Application for Students

It is found that students tend to be shy when they first enrolled in their university, causing them to have limited friends upon commencement of classes. This is because they are unfamiliar with the new environment or language, making them feel somewhat cultural shocked. This causes them to have a greater chance in getting lost and refuse to enquire help. The feeling of shyness prevents them from breaking the ice with people among them. This then forms another barrier for them to socialize. It is also found that people feel insecure using online forums because malicious users exist on those forums. To avoid being conned, they sometimes choose not to believe what they see in forums. Also, it can be difficult to search online for solutions to a student’s problem. Searching for specific information on search engines may be easy. However, the information provided may not be what the student needs as the search engine only provides posts created by others on different sites, but not tailored to the location and situation of the student. It is also found that some forums do not properly categorize posts, making it hard for searching. Therefore, it is ideal to propose the development of a forum mobile application for students to resolve all the problems found. The methodology adopted in this project is Rapid Application Development. The prototype mobile application is built in Android OS using Android Studio with Java Programming.
Architecting Success in Model Based Systems Engineering Pilot Projects

Model-Based Systems Engineering (MBSE) is an emerging paradigm for improving the effectiveness and efficiency of the practice of systems engineering that infuses integrated descriptive representations of a system to capture knowledge about the system and serve as a medium of communication of that knowledge for the benefit of all stakeholders. This paper describes some of the key lessons learned over the course of several MBSE pilot projects over the past several years and extracts recommended best practices based on those experiences, focused primarily on the proper application of system architecting and engineering principles and methods to improve the planning and execution of pilot implementations of MBSE.
Flexible group-level pruning of deep neural networks for fast inference on mobile CPUs

Network pruning is a promising compression technique to reduce computation and memory access cost of deep neural networks. In this paper, we propose a novel group-level pruning method to accelerate deep neural networks on mobile GPUs, where several adjacent weights are pruned in a group while providing high accuracy. Although several group-level pruning techniques have been proposed, the previous techniques can not achieve the desired accuracy at high sparsity. In this paper, we propose a unaligned approach to improve the accuracy of compressed model.
NON–WELL-FOUNDED DERIVATIONS IN THE GÖDEL-LÖB PROVABILITY LOGIC

Abstract We consider Hilbert-style non–well-founded derivations in the Gödel-Löb provability logic GL and establish that GL with the obtained derivability relation is globally complete for algebraic and neighbourhood semantics.
Differential Approximation and Sprinting for Multi-Priority Big Data Engines

Today's big data clusters based on the MapReduce paradigm are capable of executing analysis jobs with multiple priorities, providing differential latency guarantees. Traces from production systems show that the latency advantage of high-priority jobs comes at the cost of severe latency degradation of low-priority jobs as well as daunting resource waste caused by repetitive eviction and re-execution of low-priority jobs. We advocate a new resource management design that exploits the idea of differential approximation and sprinting. The unique combination of approximation and sprinting avoids the eviction of low-priority jobs and its consequent latency degradation and resource waste. To this end, we designed, implemented and evaluated DiAS, an extension of the Spark processing engine to support deflate jobs by dropping tasks and to sprint jobs. Our experiments on scenarios with two and three priority classes indicate that DiAS achieves up to 90% and 60% latency reduction for low- and high-priority jobs, respectively. DiAS not only eliminates resource waste but also (surprisingly) lowers energy consumption up to 30% at only a marginal accuracy loss for low-priority jobs.
Image Registration Using Single Swarm PSO with Refined Search Space Exploration

Image registration is an elementary task in Computer Vision, which geometrically aligns multiple images of a scene, captured at different times, from various viewpoints, or by heterogeneous sensors. The optimisation strategy we employ for achieving the optimal set of transformation vectors is a major factor that determines the success and effectiveness of an automatic registration procedure. This paper discusses a scheme to modify the conventional Particle Swarm Optimisation (PSO) algorithm for better search space exploration and for faster convergence. While PSO is running, after half of the total number of iterations, find the particle which is in worst position in space, then reposition that particle by mean value of its current position and the global solution. It is observed that re-positioning the worst particle in space helps that particle from premature convergence to a local optimum solution and motivates the particle to generate unique search directions, which increased the possibility of finding the globally best solution. An image registration algorithm using this modified PSO method is also presented. From the experimental results presented here, it is visible that the proposed algorithm guarantees superior results in terms of registration accuracy and reduced execution time, even in the case of large deformations between the reference and float images.
The comparative study of flood modelling with the unsteady and the steady flow on Ngotok river

Abstract The Flood modeling in a river is usually modeled with the one-dimensional flow. The Flood is one form of unsteady flow that can be simulated with the HEC-RAS program. The unsteady flow modeling results often occur error due to unstable program analysis. The stability of the model is influenced by the river channel, the slope of the river bed and the cross-sectional of the river. In flood forecasting required by maximum discharge data and maximum flood elevation, then the steady flow model is often used as an alternative to the calculation of flood forecasting. This study aims to compare the advantages and disadvantages of flood modeling with a steady flow and unsteady flow. The location of study in Ngotok River, Mojokerto Regency, Indonesia. Hydrological modeling uses HEC-HMS and hydraulic modeling uses HEC-RAS. The resulting modeling shows that the water elevation profile of the steady flow model has a higher than the unsteady flow model. The time required for the simulation of the unsteady flow model is longer than the steady flow model. The average water level elevation produced by the unsteady flow model is +27.17 m while the steady flow model is +29.62 m.
Comparison Of Classification Methods On Sentiment Analysis Of Political Figure Electability Based On Public Comments On Online News Media Sites

Elections are an important part of the political process so that not a few figures from political parties begin preparing to participate in the process. Electability is one of the issues of concern, various things are done to be able to increase the electability of political figures who participated in the general election. Media has become one of the important tools used to increase electability, one of which is online news media. News coverage in online news media with its real-time nature will very quickly get comments from readers and can be used as an assessment of political figures in the form of sentiment analysis. However, it is not easy to analyze sentiments from various comments on online news media, because comments that contain text have irregularities, especially in Indonesian texts. Text mining is one way that can be used to overcome this. Pre-processing of text in text mining is an important part of getting basic information contained in comments. This study uses the Indonesian text pre-processing using the Gata text-mining framework. Then proceed with extracting more in-depth information using the Naïve Bayes classification method and Support Vector Machine which is optimized using Particle Swarm Optimization. The tests carried out with both methods get the results that, the Particle Swarm Optimization based Support Vector Machine method is the best method in the process of classifying sentiments analysis of political figures with an accuracy of 78.40% and AUC 0.850. The results of this study get an effective algorithm in classifying positive and negative comments related to political figures from online news media.
A Digital Image Encryption Algorithm Based on Bit-Planes and an Improved Logistic Map

In digital image encryption (DIE) procedure centered on bit-planes and an improved arrangement map is offered by this paper. By the enhanced logistic map, a chaotic series is produced primarily and in the original image the pixels are scrambled. Further into a high and undersized 4-bit matrix, the scrambled image is divided correspondingly. To produce a chaotic series that is extremely connected with the image as the fundamental, the undersized 4-bit matrix is presented addicted to an enhanced logistic category and for location scrambling plus the XOR arrangement of the extreme 4-bit matrix and the basic is used. Lastly, to acquire the cipher text image, two fold matrices are joined into image matrix which is of 8-bit. One-time pad characteristic is the implication of this procedure. In the terminologies of the histogram, to evaluate the safekeeping of image encryption, plaintext sensitivity, data entropy, and pixel relationship index, MATLAB simulation investigates are added. Validating that the procedure compromises upright encryption, Investigational outcomes display that the number of pixel changes ratio (NPCR) is superior to Ninety percentages then the data entropy of the cipher text image extents 7.99.
Video Data Hierarchical Retrieval via Deep Hash Method

Video retrieval technology faces a series of challenges with the tremendous growth in the number of videos. In order to improve the retrieval performance in efficiency and accuracy, a novel deep hash method for video data hierarchical retrieval is proposed in this paper. The approach first uses cluster-based method to extract key frames, which reduces the workload of subsequent work. On the basis of this, high-level semantical features are extracted from VGG16, a widely used deep convolutional neural network (deep CNN) model. Then we utilize a hierarchical retrieval strategy to improve the retrieval performance, roughly can be categorized as coarse search and fine search. In coarse search, we modify simHash to learn hash codes for faster speed, and in fine search, we use the Euclidean distance to achieve higher accuracy. Finally, we compare our approach with other two methods through practical experiments on two videos, and the results demonstrate that our approach has better retrieval effect.
Efficient layer-wise feature incremental approach for content-based image retrieval system

Content-based image retrieval (CBIR) systems use multiple image features to represent an image. These systems suffer from curse of dimensionality since a high-dimensional feature vectors are formed to represent images in the target dataset. Reduction in length of feature vector can speed-up the retrieval process, but it reduces the retrieval accuracy. To overcome the aforementioned problem, an efficient layer-wise feature incremental approach for the CBIR system has been proposed. The proposed approach uses three primitive image features namely color, texture, and shape. The retrieval process is accomplished in three layers, at first a layer-complete dataset is searched but only single-feature space is used. Top 10% of images most similar to query image are retained in the second layer. The second layer uses two features for similarity computation, and only 50% of the most similar images are passed to the third layer. Finally, the third layer uses all three features to compute the similarity. Our aim is to reduce the search space at subsequent layers and use multiple features for a reduced dataset at the final layer. The proposed approach is evaluated on four publicly available image datasets. The retrieval results on the basis of precision, recall, and f-score show the performance improvement in comparison to state-of-the-art CBIR systems.