Reviewerly/exHarmony huggingface dataset:
Mobile Based Augmented Reality to Improve Learning of Volcanology for High School Students

Augmented Reality (AR) has become popularly used in various fields nowadays. One of the areas that utilized this recent technology is education. This paper presents a study on the use of Augmented Reality to improve students' performance in learning a complicated subject. In this paper, a proposed application was created as a tool for teaching and learning volcanology as part of Geography subject where many students showed weak performance in it. An experimental study and user-based testing were performed to see whether the proposed AR application could really benefit the teaching and learning toward increasing students' understanding of the subject. Pre-test and post-test were conducted and the results are encouraging. The tests' results revealed that the proposed mobile-based AR application is more proficient in improving students' performance than the traditional method.
A Truthful Auction Mechanism for Cumulative Resource Allocation in Mobile Edge Computing

Task offloading is a major problem in edge computing. In existing research, tasks are generally portrayed as requiring specific resources and time, and the task owner provides the value that he is willing to pay. The goal of the resource provider is to obtain the maximum social welfare and profit. However, this approach cannot cover all task offloading scenarios. 1) This paper innovatively considers a continuous task offloading problem in mobile edge computing in which the task can be partially executed and the task owner provides a nonlinear value function to pay for the executed task. The resource provider needs to decide which tasks to execute at each moment in order to obtain the maximum social welfare and profit. We represent the problem as a nonlinear integer programming model with multiple resource constraints. 2). We design an auction mechanism to solve the continuous task offloading problem in a competitive environment. Specifically, we propose a resource allocation algorithm based on the remaining value strategy and a payment price algorithm based on the critical value theory to guarantee truthful task information. Our approach is experimentally compared with existing research in terms of execution time, social welfare, and resource utilization.
VIDE - Vault App Identification and Extraction System for iOS Devices

Content hiding (or vault) apps are a class of applications that allow users to hide photos, videos, documents and other content securely. A subclass of these applications called decoy apps further supports secret hiding by having a mode which mimics standard apps such as calculators but can turn into a vault app through entering a specific input. In this work we focus on iOS devices and first describe how to identify content hiding applications from the App Store. We consider not only the US Store but also give results for App Stores in Russia, India and China. We show an effective and very fast identification of content hiding apps through a two-phase process: initial categorization using keywords followed by more precise binary classification. We next turn to understanding the behavior and features of these vault apps and how to extract the hidden information from artifacts of the app's stored data. Based on this work, we have designed and built a fully automated vault app identification and extraction system that first identifies and then extracts the hidden data from the apps on an iOS smartphone. Using our vault identification and data extraction system (VIDE), law enforcement investigators can more easily identify and extract data from such apps as needed. Although vault apps are removed regularly from the App Store, VIDE can still identify removed apps as our system continues to maintain information on such apps in our vault database.
SMARTSEC4COP: Smart Cyber-Grooming Detection Using Natural Language Processing and Convolutional Neural Networks

This paper aims to present the design and implementation of a prototype that recognizes grooming attacks in the context of COP (child online protection) using Natural Language Processing and Machine Learning hybrid model, via Convolutional Neural Networks (CNN). The solution uses a vector representation of words as the semantic model and the implementation of the model was made using TensorFlow, evaluating the classification of grooming for a text (dialogue) prepared asynchronously in a controlled environment according to methodologies, techniques, frameworks and multiple proposed techniques with his development described. The model predicts a high number of false positives, therefore low precision and F-score, but a high 88.4% accuracy and 0.81 AUROC (Area under the Receiver Operating Characteristic).
Maximal α-Leakage and its Properties

Maximal α-leakage is a tunable measure of information leakage based on the quality of an adversary's belief about an arbitrary function of private data based on public data. The parameter α determines the loss function used to measure the quality of a belief, ranging from log-loss at α = 1 to the probability of error at α = ∞. We review its definition and main properties, including extensions to α <; 1, robustness to side information, and relationship to Rényi differential privacy.
Global sustainable development and academic mobility

Abstract Academic mobility is an important indicator of a country’s competitiveness in innovations and research. Innovations play an important role for sustainable development as defined by UN 2030 goals. In this research we have studied how academic mobility is correlated with national economies.
Graphic process units‐based chicken swarm optimization algorithm for function optimization problems

Summary This article focuses on how to design an efficient GPU‐based chicken swarm optimization (CSO) algorithm (GCSO), so as to improve diversity and speed up convergence by running a large number of populations in parallel. GCSO mainly improves the sequential CSO in three aspects: (i) GCSO modifies the location updating equation of the rooster and proposes a parallel iterative strategy to transform the sequential iteration process into a parallel iterative process, thereby achieving fine‐grained parallelism and improving the convergence speed. (ii) A multirange search strategy is proposed to build different neighborhoods for each flock on the graphic process units (GPU), so that each flock searched in their respective neighborhoods, thus increasing the density and diversity of the search, and making it not easy to fall into a local optimum. (iii) A new column storage structure is designed to meet the requirement of coalescent access on GPU. Twelve benchmark functions are selected to compare GCSO algorithm with some sequential intelligence optimization algorithms and the GPU‐based particle swarm algorithm. The results show that the GCSO is able to obtain a speedup up to 163.09× compared with the CSO and achieve better optimization results in terms of both optimization accuracy and convergence speed than some intelligence optimization algorithms.
Data-Driven I/O Structure Learning With Contemporaneous Causality

In the era of big data, industry and public policy are able to make use of large amounts of data for policy decisions. The proliferation of cheap sensors and fast communication enables policy makers to consider complex networks as a whole, using time series data from many sources to model the system. The input/output structures of such systems are helpful in understanding how they work and designing new control laws. This article introduces the causal dynamic graph (CDG) model, which defines this structure explicitly. We provide a data-driven method for recovering the input/output structure of a CDG when every process is measured. We then discuss some of the implications of incomplete measurements on the graphical modeling and structural identification problem; we show that many relevant cases are equivalent to the simpler case where sensors are either perfect or completely missing. This will make the problem of graphically modeling such systems more tractable.
A CONVblock for Convolutional Neural Networks

The reduction in the size of convolution filters has been shown to be effective in image classification models. They make it possible to reduce the calculation and the number of parameters used in the operations of the convolution layer while increasing the efficiency of the representation. The authors present a deep architecture for classification with improved performance. The main objective of this architecture is to improve the main performances of the network thanks to a new design based on CONVblock. The proposal is evaluated on a classification database: CIFAR-10 and MNIST. The experimental results demonstrate the effectiveness of the proposed method. This architecture offers an error of 1.4% on CIFAR-10 and 0.055% on MNIST.
A Model for prediction of consumer conduct using machine learning algorithm

The machine learning algorithm has become important because of their accuracy in forecasting. It is very difficult to predict a customer's performance due to an unexpected customer situation. Many algorithms are designed for the same purpose. In this paper, we have studied and analysed three Bays algorithms such as AODE, Naive Bayes and AODEsr. We implemented these algorithms in the WEKA tool and built a new model that provides better accuracy than the existing one. During development we have tried to reduce the noise and error in the data and we also need to filter the information. This process will allocate W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> weight to the new filtered data. The error can be demarcated as: E (j, k), Where j ∈ J or it's an assumption. k is a function of purpose. Similarly noise can be defined by another function N = E + W <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> .
Delta-DNN: Efficiently Compressing Deep Neural Networks via Exploiting Floats Similarity

Deep neural networks (DNNs) have gained considerable attention in various real-world applications due to the strong performance on representation learning. However, a DNN needs to be trained many epochs for pursuing a higher inference accuracy, which requires storing sequential versions of DNNs and releasing the updated versions to users. As a result, large amounts of storage and network resources are required, significantly hampering DNN utilization on resource-constrained platforms (e.g., IoT, mobile phone).
Multi-channel precision-sparsity-adapted inter-frame differential data codec for video neural network processor

Activation I/O traffic is a critical bottleneck of video neural network processor. Recent works adopted an inter-frame difference method to reduce activation size. However, current methods can't fully adapt to the various precision and sparsity in differential data. In this paper, we propose the multi-channel precision-sparsity-adapted codec, which will separate the differential activation and encode activation in multiple channels. We analyze the most adapted encoding of each channel, and select the optimal channel number with the best performance. A two-channel codec hardware has been implemented in the ASIC accelerator, which can encode/decode activations in parallel. Experiment results show that our coding achieves 2.2x-18.2x compression rate in three scenarios with no accuracy loss, and the hardware has 42x/174x improvement on speed and energy-efficiency compared with the software codec.
Build a Microcontroller Based Interactive Module as A Learning Medium in Vocational Secondary Schools

Abstract The purpose of this research is to build an interactive and complete module to be used as a learning medium at the vocational secondary school level. The prototype method is one of the ways that can be used to build microcontroller modules to suit your needs. The prototype methods include communication, planning, modeling, construction, and development. Microcontrollers are one of the learning media that has a high level of difficulty in understanding each device’s usage. The trainer module that has been circulating in the market and the students are still not complete and not interactive. During this time, the microcontroller module used focuses on the creation of microcontroller trainer modules using dot matrix, keypad, LCD, digital I/O, and traffic light. In building a complete and interactive microcontroller module, it takes an appropriate method to construct it. Meanwhile, the media needs learning with the development of technology requires a more complete module in addition to the components that are already used in general. In the design of an interactive module, the microcontroller used to have more complete devices including additions to servo motors, potentiometer, temperature sensors, proximity sensors, DC motors, and the addition of WiFi modules as a complete facility of interactive microcontroller modules. The results showed that the features offered had a 50% more complete compared to previous microcontroller modules, and more interactive could use WiFi networks. With the more complete a microcontroller module, it can be easier for students to understand each implementation and can improve the competency of the students in the use of microcontrollers.
Blockchain for public health: Technology, applications, and a case study

Blockchain is a technology that provides a secure and distributed mechanism to record transactions. The technology has been applied in various domains, most notably in cryptocurrencies. This chapter outlines the potential of blockchain technology in the public health domain. Privacy and security, interoperability, and immutable audit trails are some of the key benefits of blockchain when applied to electronic health records management. The benefits are made possible by the use of public key encryption and P2P architecture, using public or private blockchain networks. The chapter also presents a use case from Estonia where the health service is using blockchain to keep track of all access/changes made to electronic health records. The chapter also includes potential challenges of blockchain implementation in public health.
GraBi: Communication-Efficient and Workload-Balanced Partitioning for Bipartite Graphs

Machine Learning and Data Mining (MLDM) applications, such as recommendation and topic modeling, generally represent their input data in bipartite graphs with two disjoint vertex-subsets connected only by edges between them. Despite the prevalence of bipartite graphs, existing graph partitioning frameworks have rarely sufficiently exploited their unique structures, especially the highly lopsided subset sizes and extremely skewed vertex degrees. As a result of poor partitioning quality, problems, particularly of high communication cost and severe workload imbalance, arise during subsequent computation over these bipartite graphs in distributed environments such as datacenters or HPC systems, significantly hampering the performance of MLDM applications.
Strategies for Improving the Quality of Logistics Courier Services Through Priority Problem-solving Based on Multiclass Classification

Abstract PT Tiki Jalur Nugraha Ekakurir (JNE) is the most popular courier service in the country and ranked the first in the category of courier service companies in Indonesia Top Brand 2018. Despite being a top brand, JNE still faces a variety of complaints posted by customers on the internet and social media. This makes it interesting to study as a means of evaluating and finding strategies to improve the quality of logistics services using a sentiment analysis method with classification models of Naive Bayes Classifier and Support Vector Machine as well as multiclass classification. This study aims to review and determine which classification model is the most appropriate for the dataset, find out the rating of sentiments and dimensions in order to measure the quality of logistics company services and identify what problems need to be prioritized. The population in this study are all the tweets mentioned to JNE’s Twitter accounts. Tweet samples in this study were collected over a 30-day period, from 1 September 2019 to 30 September 2019, totalling 11,134 tweets. This study revealed that JNE is dominated by negative sentiment of 97.82% and positive sentiment of 2.18%. The main problems (high priority) faced by JNE are the dimensions of Personnel Contact Quality and Timeliness. Then, the dimensions of Information Quality, Order Discrepancy Handling and Order Accuracy are problems with medium priority. Meanwhile, JNE’s low priority issues are the dimensions of Order Conditions, Ordering Procedures, Ordering Release Quantities, and Order Quality. Therefore, JNE needs to improve the examination of personal messages and provide a quick response to complaints about customer problems and increase commitment in every delivery service to match the estimated delivery time. It is recommended in future studies to apply the Naive Bayes Classifier classification model in Twitter dataset processing because it has fast, good, and quite precise processing results. Future research can also use the application of ABC analysis which can be used as a basis for determining the number of priority dimensions of a service’s problem solving.
Mapping gold pathfinder metal ratios in Northern Nevada, USA: A compositional analysis approach

The use of pathfinder metals has been employed with relative success, to some extent, since low concentrations, weathering, deposit locations, among other factors, makes pinpointing targets harder. Classical methods, using multivariate statistics are limited and do not yield good results on harder-to-reach deposits, making the proper usage of the available information of utmost importance. In this work, compositional analysis is used to identify pathfinder elements for Carlin-type gold deposits more efficiently. Compositional geostatistics is then applied to obtain a variogram model and to krige the balance scores, producing heat maps showing possible gold prospects. The area of study focused on the northern part of the state of Nevada, which houses several Carlin-type gold districts. More specifically in the northern Carlin trend, where important mining complexes such as Meikle, Carlin and Bootstrap are located. The data was treated with the “compositions”, “Robcompositions” and “selbal” packages for R® and the basic steps were: 1. Exploratory analysis of the data; 2. Calculation of compositions; 3. Principal compositional component analysis (CoDaPCA) and Optimal balances analysis; 4. Selection of sub compositions; 5. Modeling the variogram; 6. Kriging; 7. Mapping the interpolation of compositions represented by the chosen balance score. The results have shown the importance of the balance (Sb)/(Ba, K, Na) as a gold pathfinder. The interpolation shows that strengthened signals from the balance are spatially close to the Carlin-type gold mining districts in both Area B and C. The methodology showed the potential to be used as part of a larger prospection study not just for gold, but for other commodities as well.
Research on Massive Image Classification Method Based on Parallel Hybrid Classifier Algorithm

With the advent of the era of big data, various types of image data have exploded. Traditional image classification algorithms have the problems of low efficiency and low accuracy, and cannot meet the processing requirements of massive image data. Aiming at the above problems, this paper proposes a massive image classification method based on parallel hybrid classifier algorithm. This algorithm combines the Adaboost algorithm with the RBF algorithm, combines multiple RBF classifiers into a strong classifier, and uses the MapReduce parallel programming model to parallelize the Adaboost-RBF algorithm. The performance test of the algorithm was performed. using the Caltech256 data set. The test results show that compared with the ordinary Adaboost-RBF algorithm, the parallel hybrid classifier algorithm takes less time to run and the average classification accuracy is increased by 26%. This algorithm can meet the needs of automatic classification of massive images.
A K-Means Grasshopper Optimisation Algorithm Applied to the Set Covering Problem

Many of the problems addressed at the industrial level are of a combinatorial type and a sub-assembly not less than these are of the NP-hard type. The design of algorithms that solve combinatorial problems based on the continuous metaheuristic of swarm intelligence is an area of interest at an industrial level. In this article, we explore a general binarization mechanism of continuous metaheuristics based on the k-means technique. In particular, we apply the k-means technique to the Grasshopper optimization algorithm in order to solve the set covering problem (SCP). The experiments are designed with the aim of demonstrating the usefulness of the k-means technique in binarization. Additionally, we verify the effectiveness of our algorithm through reference instances. The results indicate the K-means binary grasshopper optimization algorithm (KBGOA) obtains adequate results when evaluated with a combinatorial problem such as the SCP.
A Noise Reduction Approach Using Dynamic Fuzzy Cognitive Maps for Vehicle Traffic Camera Images

Noise is a generic term for data loss or corruption due to hardware or software causes on the signal. Since the images are two-dimensional signals, there are noises in this type of signal due different reasons. In addition, fuzzy cognitive maps (FCM) have a structure based on a graph theory that can produce many probing solutions today. Fuzzy cognitive maps can provide their iterations as static (fixed neighborhood values) or dynamic (variable neighborhood values) depending on the solution, which belong to interested problem. In this study, a method is presented using fuzzy cognitive maps for noise reduction in images and mean filter, which is a widely used method for noise reduction. The proposed method provide to minimize the loss of data in the noise reduction process with the average filter. In this work, FCM takes noisy and average filtered noisy image masks and accepts each pixel value in these masks as nodes. Then we update the neighborhood weights between these nodes in each iteration. The developed method has been tested primarily with different images and the performance obtained only by the method in which the average filter is applied is quite high. Then, the proposed method was tested on images of traffic monitoring systems taken from vehicle cameras. The results obtained are very successful.
An IoMT System for Healthcare Emergency Scenarios

Today and all around the globe there is a national emergency service in almost every country. These services typically offer through telephone calls access to medical services that ranged from advice to on-the-spot response to hazardous situations. These emergency call systems have become very popular and the proliferation of mobile devices and the mobile network have also facilitated the use of these services. Emergency responders face several challenges when trying to give an accurate and above all rapid response. One of the main problems is the collection of citizen's information and its location and context. This is a crucial process for understanding the severity of a citizen state and effectively determining what response should be given. This paper presents an IoMT solution for medical emergency call scenarios, proposing an ubiquitous approach of patient data collection and response. The solution consists in a mobile application for emergency calls that collects patient location and health data from a wearable device (smartwatch) and a web application for emergency responders that interacts with patients with the aid of voice analysis and recognition. The proposed IoMT system and integrated solutions were validated both in terms of features and communication through a series of experiments on real devices through Wi-Fi network.