Reviewerly/exHarmony huggingface dataset:
Comparing the Accuracy of Hierarchical Agglomerative and K-means Clustering on Mobile Augmented Reality Usability Metrics

This article presents the experimental work of comparing the performances of two machine learning approaches, namely Hierarchical Agglomerative Clustering and K-means Clustering on Mobile Augmented Reality Usability datasets. The datasets comprises of 2 separate categories of data, namely performance and self-reported, which are completely different in nature, techniques and affiliated biases. This research will first present the background and related literature before presenting initial findings of identified problems and objectives. This paper will the present in detail the proposed methodology before presenting the evidences and discussion of comparing this two widely used machine learning approach on usability data.
Design of Ride Sharing Service “ZOUGAME” in Neighborhood Community

This paper reveals a ride sharing service concept “ZOUGAME” which provides a novel mobility experience with a pet-like slow-moving mobility vehicle limited in a certain neighborhood town. Users can pick any particular ZOUGAME vehicle circulating around the neighborhood, and can enjoy chatting during the carpool ride among other riders. The researchers aim to bond the community together through this service. This service concept was created through ethnography research of how a home-visiting physical therapist uses a e-bike in a neighborhood as a maneuverability master. The researchers validate the possibilities that industries can contribute and benefit from this service, through a collaborative value co-creation design process.
Secure Key Encapsulation Mechanism with Compact Ciphertext and Public Key from Generalized Srivastava Code

Code-based public key cryptosystems have been found to be an interesting option in the area of Post-Quantum Cryptography. In this work, we present a key encapsulation mechanism (KEM) using a parity check matrix of the Generalized Srivastava code as the public key matrix. Generalized Srivastava codes are privileged with the decoding technique of Alternant codes as they belong to the family of Alternant codes. We exploit the dyadic structure of the parity check matrix to reduce the storage of the public key. Our encapsulation leads to a shorter ciphertext as compared to DAGS proposed by Banegas et al. in Journal of Mathematical Cryptology which also uses Generalized Srivastava code. Our KEM provides IND-CCA security in the random oracle model. Also, our scheme can be shown to achieve post-quantum security in the quantum random oracle model.
Research on the Application of Artificial Intelligence and Distributed Parallel Computing in Archives Classification

In the traditional mode, the classification of archives often has large workload, long time consumption and high labor cost. With the development of archives management towards informationization and paperlessness, it has become an important research topic to classify archives by using artificial intelligence classification method based on machine learning. In this paper, an archives classification method based on XGBoost and Spark distributed parallel computing is proposed. XGBoost algorithm can continuously improve the classification accuracy of small class samples during training rounds, which makes XGBoost algorithm has certain advantage in the classification of archives data with large samples and unbalanced class distribution. Spark distributed parallel computing system can greatly improve the computational efficiency of XGBoost algorithm and reduce the training time of the archives classification model. The simulation results show that our method has the advantages of faster model training and higher classification accuracy than the traditional method, and can satisfy the complex and huge archives classification task.
Prioritize When Patching Everything is Impossible!

Vulnerable critical networks are attractive targets for remote adversaries with different intentions. Towards enhancing resilience against extreme risks, such networks need to continuously assess their security posture and prioritize possible remediation actions based on security risk. The contribution of this work is to provide an integrated risk-based decision-support methodology for prioritizing risk remediation activities. Our methodology leverages the Time-To-Compromise security metric to quantitatively assess the compromise risk. Furthermore, it employs game-theory principles to model the strategic behaviour of the involved players (e.g., defender and attacker). The novelty of this approach lies in the way it integrates the risk attitude of the decision makers involved in the patch management operations across critical organizations into the prioritization process.
Odia Character Recognition Using Curvelet Transform with DWT Feature Extraction

Developing of OCR for Indian scripts is an increasing area of research. This research work is an attempt to make an OCR system for Odia characters and numbers which is official language of ODISHA. In this work we proposed a new approach for Odia character recognition using digital curvelet transform. It presents characters and numbers recognition scheme for Odia characters. The curvelet transform although heavily utilized in various areas of image processing. The curvelet coefficients of a unique picture just as its morphologically adjusted renditions are utilized to KNN classifiers. The yield estimations of these classifiers are combined utilizing a straightforward dominant part casting a ballot plan to touch base at an official choice utilizing one of a kind structure of some character we have discovered better outcome as correlation with different techniques.
Blind deblurring of Gaussian blurred images by blurred edge image

In this paper, we propose a learning method for deblurring Gaussian blurred images blindly by exploiting edge cues via deep multi-scales generative adversarial network: DeepEdgeGAN. We proposed the edges of the blurred images to be incorporated with the blurred image as the input of the DeepEdgeGAN to provide a strong prior constraint for the restoration, which is beneficial to solve the problem that gradients of the restored images with GANs methods tend to be smooth and not clear enough. Further, we introduce the perceptual and edge as well as scale losses to train the DeepEdgeGAN. With the trained end-to-end model, we directly restore the latent sharp images from blurred images and avoiding the estimation of pixel-kernel. Qualitative and quantitative experiments demonstrate that the visual effect of the restored images significantly improves better.
GLmser: A GAN-Lmser Network for Image-to-Image Translation

We present a GAN-Lmser network for the problem of transforming an image from one domain A to another B. The proposed network is based on CNN-Lmser, a recent further extension to deep convolutional layers from least mean square error reconstruction (Lmser) network, which was originally proposed in 1991. Specifically, in GAN-Lmser, the two directions, A-to-B and B-to-A, share the same architecture and symmetrically the same weights, by following the duality in bidirectional architecture (DBA) and duality connection weights (DCW) of Lmser, and an adversarial loss from GAN(generative adversarial network) was added to Lmser. Compared with the famous image-to-image translation model CycleGAN, the GAN-Lmser is compact with a significantly reduced number of parameters and is able to transfer learning through weight sharing between the two directions. Experiments demonstrate that GAN-Lmser is at least comparable to CycleGAN in benchmark datasets, and is robust when the training sample size is small.
Voice Interactive Games

Computer games are being controlled by joysticks or the keys of the computer keyboard, especially the four arrow keys, or the WASD, or both, and other keys of the keyboard. In this research paper we describe a new algorithm that replaces control with joystick or the arrow keys with voice commands. Thus, we replace the left arrow key with the voice command “left”, the right arrow key with the voice command “right”, the up-arrow key with the voice command “up”, and the down arrow key with the voice command “down”. In order to do that we first develop a new convolutional neural network architecture, then we teach the architecture how to recognize the words “lef”, “right”, “up”, “down”, with extremely high accuracy and very low probability of misclassification. Once the Convolutional Neural Network (CNN) is taught to recognize these words, we use the feedforward part of the network in our game programs so that they can capture, real time, the voice input commands of the player and play the game. The advantage of using the voice commands is that for many players it is easier, faster, eliminates the chance of pushing the wrong key by mistake, and provides a better player experience. We also present a pong game in Unity where the paddle controller uses our algorithm to control the two paddles of the game.
An Implicit Authentication Solution based on User's Keystroke Behavior of Smartphone Usage

With the popularity of smartphones, more and more users are accustomed to storing sensitive information related to personal privacy, which puts forward higher requirements for the current authentication system. In this paper, an implicit authentication solution based on user's keystroke behavior of smartphone usage is proposed. Firstly, a user's behavior data are collected by three types of built-in sensors in a smartphone. After performing smoothing and normalization processing, we extract temporal features in time domain by using statistical methods. Secondly, various classifiers are used to classify whether a user reflected by features are legal or not. Finally, we do simulations to verify the classification performances of these classifiers, and draw a conclusion that the classification performances of GentleBoost and LogitBoost classifiers are superior to those of others with our experimental dataset.
A Robust Visual Tracker Based on DCF Algorithm

Since Correlation Filter appeared in the field of video object tracking, it is very popular due to its excellent performance. The Correlation Filter-based tracking algorithms are very competitive in terms of accuracy and speed as well as robustness. However, there are still some fields for improvement in the Correlation Filter-based tracking algorithms. First, during the training of the classifier, the background information that can be utilized is very limited. Moreover, the introduction of the cosine window further reduces the background information. These reasons reduce the discriminating power of the classifier. This paper introduces more global background information on the basis of the DCF tracker to improve the discriminating ability of the classifier. Then, in some complex scenes, tracking loss is easy to occur. At this point, the tracker will be treated the background information as the object. To solve this problem, this paper introduces a novel re-detection component. Finally, the current Correlation Filter-based tracking algorithms use the linear interpolation model update method, which cannot adapt to the object changes in time. This paper proposes an adaptive model update strategy to improve the robustness of the tracker. The experimental results on multiple datasets can show that the tracking algorithm proposed in this paper is an excellent algorithm.
The Role of Paper Prototyping in Designing Visual Novel Game as Learning Media for Children

Currently, the game has been used as learning media in educational field. In developing educational media, it is necessary to match the content and user interface with the target user and match the content with the goals of educational media. The development process is more effective and the chances of success are greater if tested as early as possible. Testing at this design stage uses a paper prototype to get user feedback about the content and the user interface. In this paper, we share experiences where a paper prototype is used to test the suitability of the content and UI before entering the stage of making visual novel games. We identify the problems encountered when applying this method to users, analyze the results, and describe how this problem is solved.
Requirements Engineering Research: News From the Trenches

Presents information on the 2019 International Conference on Requirements Engineering.
A Survey Paper on Mean Shift Algorithm to Improve Efficiency using Blurring Mean Shift Technique

A Mean shift algorithm is a clustering & unsupervised algorithm. It is also called as “Mode seeking Algorithm”. A mean-shift algorithm used for image processing and also in computer vision. It doesn't require specified clusters like k-mean. Basically it has two types BMS & MS. By studying these two, it looks like BMS is slightly better than MS because it changes the dataset in each iteration with computed value. It's nonparametric algorithm and it can control no. of the cluster because the mean shift is much slower and much time complex and output depend on windows size. (Windows size is trivial). The BMS is an accelerated version which uses the original data only in the first step, then re-smoothes previous estimates. A BMS does not predefined shape on data cluster. To overcome such a situation, we can use the BMS and K-Medoid algorithm because k-medoid is partitioning technique of a cluster. It has better scalability for the larger datasets and much efficient so using these two we can build mean shift algorithm faster than before.
Hybrid Solar Power System Optimization based on Multi-Objective PSO Algorithm

Nowadays, with the rapid development of related technologies, the solar power become an important component of the whole energy system. At present, there are two main forms of solar power generation: photovoltaics (PV) and concentrated solar power (CSP). Among them, PV is much cheaper, but more susceptible to resource conditions, while CSP is more controllable based on the thermal storage section, but still expensive. To integrate the advantages of these two kinds of technologies, the hybrid solar power system is proposed by many researchers. Some single objective optimization algorithms are used in the process of design such systems. However, it is still a dilemma to deal with the cost and stability in the hybrid system. In this paper, we try to propose an Multi-Objective Particle Swarm Optimization (MO_PSO) algorithm to solve this problem, which can consider the performance and cost of the project at the same time. The experimental result based on the real data shown that this algorithm can provide a feasible solution of the hybrid power system with stable output and acceptable cost. Furthermore, this method based on artificial intelligence can be used in other hybrid systems optimization in the smart grid.
SaberX4: High-Throughput Software Implementation of Saber Key Encapsulation Mechanism

Saber is a module lattice-based CCA-secure key encapsulation mechanism (KEM) which has been shortlisted for the second round of NIST's Post Quantum Cryptography Standardization project. To attain simplicity and efficiency on constrained devices, the Saber algorithm is serial by construction. However, on high-end platforms, such as modern Intel processors with AVX2 instructions, Saber achieves limited speedup using vector processing instructions due to its serial nature. In this paper we overcome the above-mentioned algorithmic bottleneck and propose a high-throughput software implementation of Saber, which we call 'SaberX4', targeting modern Intel processors with AVX2 vector processing support. We apply the batching technique at the highest level of the implementation hierarchy and process four Saber KEM operations simultaneously in parallel using the AVX2 vector processing instructions. Our proof-of-concept software implementation of SaberX4 achieves nearly 1.5 times higher throughput at the cost of latency degradation within acceptable margins, compared to the AVX2-optimized non-batched implementation of Saber by its authors. We anticipate that both latency and throughput of SaberX4 will improve in the future with improved computer architectures and more optimization efforts.
The Impact of Features on Feature Location

Presence of large number of Feature Location Techniques (FLTs) poses difficulties when selecting an appropriate FLT, given a software maintenance task. This problem is aggravated by extensive heterogeneity in empirical designs employed to evaluate the FLTs and one such heterogeneity that may feed into conflicting findings across studies, is the feature set sought in evaluations. An analysis of the empirical findings of FL studies suggests that (sought) feature characteristics can have a stronger impact on FLTs performance than differing FLTs. Towards understanding their impact, this paper proposes two feature metrics that are hypothesized as affecting FLTs performances. To evaluate the presented metrics, a controlled experiment on 461 features gathered from four software systems was performed. The focus was to establish the relationship between the metrics and FLT performance. Results of the empirical evaluation suggest that the presented feature metrics strongly impact performance of different FLTs, as measured using established evaluation measures. Thus, this paper facilitates a more standard, transparent selection of feature benchmarks towards fair comparison of FLTs.
Fair Kernel Regression via Fair Feature Embedding in Kernel Space

In recent years, there have been significant efforts on mitigating unethical demographic biases in machine learning methods. However, very little work is done for kernel methods. In this paper, we propose a novel fair kernel regression method via fair feature embedding (FKR-F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> E) in kernel space. Motivated by prior works feature processing for fair learning and feature selection for kernel methods, we propose to learn fair feature embeddings in kernel space, where the demographic discrepancy of feature distributions is minimized. Through experiments on three public real-world data sets, we show the proposed FKR-F^2E achieves significantly lower prediction disparity compared with the state-of-the-art fair kernel regression method and several other baseline methods.
Detection of Near Field Communication (NFC) Relay Attack Anomalies in Electronic Payment Cases using Markov Chain

Near Field Communication (NFC) is a short- range wireless communication technology that supports several features, one of which is an electronic payment. NFC works at a limited distance to exchange information. In terms of security, NFC technology has a gap for attackers to carry out attacks by forwarding information illegally using the target NFC network. A relay attack that occurs due to the theft of some data by an attacker by utilizing close communication from NFC is one of them. Relay attacks can cause a lot of loss in terms of material sacrifice. It takes countermeasures to overcome the problem of electronic payments with NFC technology. Detection of anomalous data is one way that can be done. In an attack, several abnormalities can be detected which can be used to prevent an attack. Markov Chain is one method that can be used to detect relay attacks that occur in electronic payments using NFC. The result shows Markov chain can detect anomalies in relay attacks in the case of electronic payment.
The Implementation of AES-CMAC Authenticated Encryption Algorithm on FPGA

The advancements in communication technology have evolved the algorithms used for communication security. Recently, the Authenticated Encryption (AE) algorithms are employed for providing security services for data communication. The Advanced Encryption Standard in Counter mode (AES-CTR) with Cipher-block Chaining Message authentication code (AES-CCM) algorithm is used for providing the data security for various applications. But the AES-CCM algorithm provides limited throughput for data communication. Therefore, in this work, new architecture is proposed for increase in throughput for provision of data security for communication application. The proposed algorithm uses the AES-CTR algorithm with the Cipher-based Message Authentication Code (CMAC) algorithm for providing AE. The proposed AE algorithm is implemented on FPGA and it's compared with the FPGA implementation of AES-CCM algorithm. The comparison results of the proposed algorithm and the AES-CCM algorithm shows that the proposed algorithm provides improvement in the consumption of the area, processing time, and throughput. The implementation of proposed AES-CMAC AE algorithm on FPGA provides a throughput of 4.30 Gbps.
Mapping the extent and methods of small-scale emerald mining in the Panjshir Valley, Afghanistan

Emerald mining in the Panjshir Valley, Afghanistan, has occurred for thousands of years, yet few records exist documenting the detailed spatial extent, techniques, or productivity of small-scale miners. This study proposes new methods to map and monitor the extent and changes in small-scale mining in remote and inaccessible terrain by integrating multispectral remote sensing analysis with archival geologic data and three-dimensional topographic change detection to examine emerald deposit zones and mining activity in the Panjshir Valley. Specifically, previously mapped geologic units known to host emeralds were re-analyzed using Landsat multispectral analysis to investigate the potential distribution of mining activity. Interpretation of very fine-resolution satellite imagery showed that mining activity is becoming more concentrated and transitioning from traditional tunneling methods to mechanized surface excavation. Finally, topographic change analysis of mechanized mine sites was combined with archival grade data to estimate production and consider improved recovery methods by small-scale miners.
Where Did the Political News Event Happen? Primary Focus Location Extraction in Different Languages

Political news reports are populated all over the world in various languages. It has a great value to automatically detect the geolocation from these reports for a better understanding of the associated events. Although various open-source and commercial tools exist to identify geolocation, they fail to identify at a granular level such as locality or city and they do not support most languages. Most of the techniques view the problem in terms of Named Entity Recognition (NER) and identify geolocation information at the country level for a given text. In this paper, we consider English, Spanish and Arabic news articles from different publishers. We define primary focus location as the actual location where the event occurred amongst other focus locations mentioned in the report. Our aim is to extract the primary focus location regardless of the language from articles belonging to different news agencies. We propose a mechanism to identify potential sentences containing focus locations using NER. After that, we perform sentence embedding over words from different languages and then employ a supervised classification mechanism to predict the primary focus location. We also perform bias correction over the training data using a suitable adaptation mechanism to reduce the sampling bias in training data. Our method trains a classifier using bias-corrected training data from news articles published by an agency in one language, while testing the model on news articles published by another agency in a different language. Our empirical results when compared to baseline approaches show superior performance on real-world English, Spanish and Arabic news articles.