Reviewerly/exHarmony huggingface dataset:
QR Code Applications in Tourism

With the development of technology, new innovative approaches emerge day by day. With the internet and mobile devices being used more actively, many applications provide great convenience to our lives. This situation has brought about many changes and developments when it is evaluated in terms of both businesses and consumers. Enterprises are using applications that will increase customer satisfaction and loyalty to be different from competitors, adopt in the market, interiorize a sustainable policy, and make more profit. Some technological applications will save customers time and increase satisfaction and loyalty in tourism enterprises and all other businesses. One of these applications is the use of QR codes.
Diagnosis of Tuberculosis by Using a Fuzzy Logic Expert System

Tuberculosis (TB) is one of the diseases that causes high mortality in humans. The prevention of this disease has been sought by medical professionals and researchers. Unfortunately, the handling of TB is still manual and very dependent on medical experts who are very limited in number. In this study we propose an alternative information technology to overcome this problem. To overcome this problem a TB diagnostic system is developed using a fuzzy expert system. Input data on this system are the symptoms suffered by the sufferer, which consists of cough, weight loss, breathless, loss of appetite, and fever. The input data is then processed using fuzzy logic which consists of a process of fuzification, inference and defuzification. The output of the system displays the disease diagnosis interface on the web. Disease rules are given by experts who are experts in their fields and from journal sources. The results of the study are information systems that can provide the results of disease diagnosis to the user. The calculation of the accuracy value is also done to find out how accurate the fuzzy logic is in this system, and from the results of these calculations it is found that the accuracy value is 90% which shows that fuzzy logic is good for the diagnostic process.
Full hardware implementation of the Post-Quantum Public-Key Cryptography Scheme Round5

We describe a generic high-speed hardware architecture for the lattice-based post-quantum cryptosystem Round5. This architecture supports both public-key encryption (PKE) and a key encapsulation mechanism (KEM). Due to several hardware-friendly features, Round5 can achieve very high performance when implemented in modern FPGAs.
Application of Naive Bayesian Algorithms in E-mail Classification

In the context of the rapid development of today's society, high-tech information technology such as machine learning has played a very important role in promoting social, economic and technological development in all countries of the world. Bayesian classification algorithm is an important algorithm in the field of machine learning and data mining. Naive Bayesian algorithm is a basic and simple classification algorithm in Bayesian classification algorithm. The naive Bayesian algorithm has the advantages of high stability, simplicity, high efficiency and strong theoretical foundation. The classification quality of the naive Bayesian algorithm much depends on the choice of construction methods and the nature and quantity of the classification data. In fact, many problems in our life can be attributed to classification problems, such as risk prediction, medical diagnosis, e-mail classification, fraud detection, etc. [1]. Here, I will mainly explain the principle of naive Bayesian algorithm, classification process, the evaluation of classification results, and apply this algorithm to e-mail classification tasks. At the same time, I list some related examples. Finally, I will summarize the naive Bayesian algorithm.
Monte Carlo Tree Search with Variable Simulation Periods for Continuously Running Tasks

Monte Carlo Tree Search (MCTS) is widely used for planning in domains where the potential actions can be represented as a tree of sequential decisions. To efficiently select an action, MCTS usually needs to perform many simulations to build a reliable tree representation of the decision space. As such, a bottleneck to MCTS arises when enough simulations cannot be performed between action selections. This is particularly highlighted in continuously running tasks, for which the time available to perform simulations between actions tends to be limited due to the environment's state constantly changing. In this paper, we present an approach that extends the time available for Monte Carlo simulations when allowed. Our approach is to effectively balance the prospect of selecting the right action with the time that can be spared to perform MCTS simulations before the next action selection. For that, we considered the simulation time as a decision variable to be selected alongside an action. We extended the Hierarchical Optimistic Optimization applied to Tree (HOOT) method to adapt our approach to environments with a continuous decision space. We evaluated our approach on tasks with a continuous decision space using OpenAI gym's Pendulum and Continuous Mountain Car environments and on those with discrete action space using the arcade learning environment (ALE) platform. The evaluation results show that, with variable simulation times, the proposed approach outperforms the conventional MCTS in the evaluated continuous decision space tasks and improves the performance of MCTS in most of the ALE tasks.
Enhanced Process Model and Analysis of Risk Integration in Software effort estimation

The development of software within the estimated effort is remaining as a challenging task. The process of effort estimation is a critical activity in a software project, where effort estimates are utilized to arrive at the schedule, resources, and cost. Though many software effort estimation techniques exist, effort overrun occurs in a project. Identification of risks and their consideration in software lifecycle activities play a significant role in the successful execution of a software project. It would be required to account for uncertainty and the key factors that contribute to it. This study focuses on the need to include project risk score in the software effort estimation process to arrive at better effort estimates. This paper depicts the standard and enhanced process frameworks for estimation of software development efforts. A multi-layer perceptron model was built and the results indicated the relevance of considering project risk score in the effort estimation process. The usage of an enhanced gradient boosting technique for predictive modelling revealed a decrease in standard deviation of the residuals, thus indicating a better fit for the effort estimation model through integration of risks.
Lightweight Ciphers in Automotive Networks: A Preliminary Approach

Nowadays, the growing need to connect modern vehicles through computer networks leads to increased risks of cyberattacks. The internal network, which governs the several electronic components of a vehicle, is becoming increasingly overexposed to external attacks. The Controller Area Network (CAN) protocol, used to interconnect those devices is the key point of the internal network of modern vehicles. Therefore, securing such protocol is crucial to ensure a safe driving experience. However, the CAN is a standard that has undergone little changes since it was introduced in 1983. More precisely, in an attempt to reduce latency, the transfer of information remains unencrypted, which today represents a weak point in the protocol. Hence, the need to protect communications, without introducing low-level alterations, while preserving the performance characteristics of the protocol. In this work, we investigate the possibility of using symmetric encryption algorithms for securing messages exchanged by CAN protocol. In particular, we evaluate the using of lightweight ciphers to secure CAN-level communication. Such ciphers represent a reliable solution on hardware-constrained devices, such as microcontrollers.
Feature Extraction and Facial Expression Recognition using Support Vector Machine

Facial expressions assume an important part in our everyday collaborations, and late generation has seen an awesome measure of exploring methods for dependable facial impressions identification frameworks. Different innovations of Facial Expression Recognition have been tested by analysts in the course of recent years. Changes in facial expression turn into a troublesome undertaking in perceiving faces. In this we dissect regional facial transformations and utilize various straightforward attributes to shape a compelling classifier. Finally, here exhibited an approach which utilizing an Active Appearance Model and Support Vector Machines. Active Appearance Model (AAM) is used to pull out the unique facial key points and also to consolidate their regional structure attributes to design a classifier. After extracting facial features, these facial coordinates are fed into a Support Vector Machine and the prepared framework classifies the expressions into six classifications specifically like Anger, Fear, Normal, S ad, Disgust and Happy. This framework accomplishes robust and superior expression classification which shows improved results than the existing methods by leading experiments.
Learning Spatial-Corrected Regularized Correlation Filters for Visual Tracking

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual tracking. DCFs utilizing a periodic assumption of the samples to train a correlation filter performs efficiently in tracking. However, the DCF framework is still an open-loop system prone to tracking failures caused by model drift. In this paper, we present a Spatial-Corrected Regularized Correlation Filters (SCRCF) which is a DCF-based tracker with a correction mechanism. SCRCF exploit the advantages of the spatial regularization method to design a corrective feedback mechanism. The mechanism calculates the reasonable offset to influence the spatial regularization coefficients by observing the output responses of multiple trackers, so as to correct the unreasonable tracking results. Compared to conventional DCF-based trackers, SCRCF is more robust to handle some complicated tracking scenes, such as occlusion and motion blur. Extensive experiments on OTB-2015 benchmarks demonstrate our tracker outperforms most state-of-the-art trackers.
Hunting for Fashion via Large Scale Soft Biometrics Analysis

Fashion analysis has gained increasing attention thanks to its immense potential in fashion industry, precision marketing, and sociological analysis, etc. While a lot of fashion analysis work has been done for clothing and makeup, few of them address the problem from the perspective of large scale soft biometrics. In this paper, we focus on soft biometric attributes on human faces, particularly lip color and hair color, based on the analysis of which using a large scale data set we aim to reveal the fashion trend of lipstick color and hair color. To this end, we first perform the following steps on each image: face detection, occlusion detection, face parsing, and color feature extraction from the lip and hair regions. We then perform clustering based on the extracted color features in the given large scale data set. In the experiments, we collect from the Internet 15, 366 mouth-occluded and 14, 580 hair-occluded face images to train an effective occlusion detector such that noisy face images with occluded mouths/hairs are excluded from the subsequent fashion analysis, and another more than 20, 000 face images for analyzing the fashion trend of lipstick and hair colors. Our experimental results on the collected large scale data set prove the effectiveness of our proposed method.
Lightweight Image De-raining for IoT-enabled Cameras

A traditional surveillance system with image enhancement often heavily relies on cloud-tier processing, which demands higher bandwidth and increases latency. To reduce the bottleneck on the cloud and bandwidth under raining weather, we propose a novel image de-raining network for recourse-constrained IoT-enabled cameras by leveraging edge intelligence. The experiment results show that our network is capable of de-raining under heavy-rain conditions, and achieves faster processing time compared with previous researches.
Machine Learning Models for Activity Recognition and Authentication of Smartphone Users

Technological advancements have made smartphones to provide wide range of applications that enable users to perform many of their tasks easily and conveniently, anytime and anywhere. For this reason, many users are tend to store their private data in their smart phones. Since conventional methods for security of smartphones, such as passwords, personal identification numbers, and pattern locks are prone to many attacks, this research paper proposes a novel method for authenticating smartphone users based on performing seven different daily physical activity as behavioral biometrics, using smartphone embedded sensor data. This authentication scheme builds a machine learning model which recognizes users by performing those daily activities. Experimental results demonstrate the effectiveness of the proposed framework.
Improvement of YOLOv3 network based on ROI

The paper adopts the YOLOv3 network, by removing the calculation of non-interest areas to improve the operation speed of YOLOv3. It increases the operating speed in proportion to the size of the non-interest area. The author studies the characteristics of the convolutional layer and the YOLO layer, and proposes a new processing method from the experimental results. In this paper, the ROI image processing technology in the video detection sequence is used to filter the ROI region, eliminate the calculation outside the ROI, and improve the detection speed and accuracy of the YOLOv3 network.
Performance and Energy Balanced Algorithm for Executing High Performance Computing Application

NoC (Network-on-Chip) in recent times has been broadly used in majority of present multi-core (MC) processing environment (i.e., processor) (especially in cloud computing servers). Then, SoCs (System-on-Chips), as a scalable and flexible strategy to the widespread integration of higher number of element in the chips. Nonetheless, a main issue is reliable processing, performance efficiency and energy efficacy perquisite of BigData processing framework. This work assume self-aware MC architectures that autonomously adjust or optimize their performance to accommodate users quality of service (QoS) performance requirement, job execution performance, energy efficiency, and resource accessibility. Numerous researchers are concerned about the lack of energy conservation in cloud computing environment and hence numerous power efficient technologies are introduced by different researchers to protect environment by enormous amount of power dissipation and enhance the performance of the environment. However, these power efficient technologies requires enormous amount of interaction cost between inter-processors. Moreover, these technologies provides insufficient results and energy consumption of cloud computing devices cannot be reduced largely due to enormous amount of memory utilization, routing etc. For overcoming research challenges, this work present a novel performance and energy balanced scheduling (PEBS) approach in cloud environment based on Dynamic voltage and Frequency Scaling (DVFS) technique is proposed. Experiment outcome shows PEBS_DVFS model attains good trade-off between system performance and energy consumption in multicore cloud computing (CC) architectures when compared with existing model.
Diaforá: A Visualization Tool for the Comparison of Biological Taxonomies

We address the problem of visualizing differences between two versions of a biological taxonomy. Given the dynamics of the taxonomic work, taxonomists are often faced with alternative versions of a taxonomy that need to be reconciled. Nevertheless, visual comparison of hierarchies is an open problem that involves several difficult challenges in Visual Analytics. First, how to display not one but two possibly large taxonomies on a fixed-size screen. Second, how to highlight all differences between the two taxonomies. We present Diaforá, an interactive tool that infers and visualizes the differences. Automatic inference is achieved by incorporating taxonomy rules to identify operations such as merging, splitting, and renaming of taxa, among others. Highlighting of differences is accomplished by using the edge drawing technique, which has been enhanced with a number of features suggested by users of a prototype version. Diaforá has been implemented and tested with real world taxonomies such as Bryozoa and Annelida as well as with artificial taxonomies.
An Efficient Analysis Method for Monostatic quantum radar cross section

Quantum radar is a new concept proposed in recent years, which has attracted much attention. Quantum radar cross section (QRCS) is an important parameter of quantum radar. However, the simulation for QRCS is enormous for that it's based on the interaction of photons with atoms. In this paper, an extremely efficient method is proposed to calculate the monostatic QRCS. Besides, a two-dimensional arbitrary model is simulated. Compared with physical optics method, the side lobe enhancement effect of QRCS is verified. Meanwhile the high efficiency of this method is well documented.
Generating Text using Generative Adversarial Networks and Quick-Thought Vectors

Generative Adversarial Networks (GANs) have been shown to perform very well with the image generation tasks. Many advancements have been made with GANs over the past few years, which are making them more and more accurate in their generation tasks. State-of-the-art methods of natural language processing (NLP) involve word embeddings such as global vectors for word representation (GLoVe) and word2vec. These word embeddings help apply text data to a neural network by converting the textual data to numbers that the networks could use. The main focus for GANs has been image generation and in the past few years there have been research works to apply GANs to the text generation task. This paper presents a Quick-Thought GAN (QTGAN) to generate sentences by incorporating the Quick-Thought model. Quick-Thought vectors offer richer representations than prior unsupervised and supervised methods and enable a classifier to distinguish context sentences from other contrastive sentences. The proposed QTGAN is trained on a portion of the BookCorpus dataset that has been converted to Quick-Thought embeddings. The embeddings generated from the generator are then classified and used to pick a generated sentence. BLEU scores are used to test the results of the training and compared to the Skip-Thought GAN. The increases in BLEU-3 and BLEU-4 scores were achieved with the QTGAN.
Evolutionary Algorithms for Constructing an Ensemble of Decision Trees

Most decision tree induction algorithms are based on a greedy top-down recursive partitioning strategy for tree growth. In this paper, we propose several methods for induction of decision trees and their ensembles based on evolutionary algorithms. The main difference of our approach is using real-valued vector representation of decision tree that allows to use a large number of different optimization algorithms, as well as optimize the whole tree or ensemble for avoiding local optima. Differential evolution and evolution strategies were chosen as optimization algorithms, as they have good results in reinforcement learning problems. We test the predictive performance of this methods using several public UCI data sets, and the proposed methods show better quality than classical methods.
Algorithms on Compressed Time-Evolving Graphs

Time-evolving graphs are structures that encapsulate how a graph changes over time. Thus, we not only have to deal with large graphs consisting of nodes and edges in the billions, but we must also keep track of when these edges activate and deactivate over long lifetimes. In this age of big historical data, we must make use of efficient time-evolving graph compressions, or we will find ourselves quickly out of main memory. These time-evolving graph compressions must not only be space efficient, but must also facilitate fast querying directly on the compressed graph. In this paper, define several novel time-evolving graph problems and develop algorithms to solve them directly on various, massive, synthetic and real-world time-evolving graphs compressed using our technique. Our experiments provide details of the compressed graph sizes, algorithm run times, and other metrics.
Multiple Layered Security Analyses Method for Cryptocurrency Exchange Servicers

Internet is a common method of trading business today. The usage of cryptocurrencies has increased these days and it has become a trend to utilize them. Cryptocurrency exchange servicers provide different smartphone apps that unfortunately may become the target of malicious attacks. This paper focuses on how it achieves highest security and proposes the multiple layered security analyses method for cryptocurrency exchange servicers.
An Investigation of the Backgrounds of Health Sciences Librarians

The purpose of this study was to investigate the educational and professional backgrounds of health sciences librarians currently working in the U.S. and Canada. A survey consisting of 15 questions was created using the software program Qualtrics. 389 total surveys were received. Respondents reported having a wide variety of educational and professional backgrounds. Only about 22% of respondents reported having health sciences work experience prior to entering the field. Recruitment into the field of health sciences librarianship should take a broad approach, and professional development efforts should appeal to a wide array of experience levels.
Gaze from Head: Gaze Estimation Without Observing Eye

We propose a gaze estimation method not from eye observation but from head motion. This proposed method is based on physiological studies about the eye-head coordination, and the gaze direction is estimated from observation of head motion by using the eye-head coordination model trained by preliminarily collected data of gaze direction and head pose sequence. We collected gaze-head datasets of from people who walked around under real and VR environments, constructed the eye-head coordination models from those datasets, and evaluated them quantitatively. In addition, we confirmed that there was no significant difference between the models from the real and VR datasets in their estimation accuracy.
Change of the Learning Cycle After Blockchain

The purpose of this chapter is to create a foresight related to the role of using blockchain to meet the learning needs and how it may change learning cycle in 21st century. In this context, firstly explanation of the development of digital learning was given by describing the paradigm changes in lifelong learning activities. Learning needs of the 21st century were explained within the framework of constructivism and connectivism in terms of changes in learning tendencies. The problems encountered in the new learning tendencies were examined in the context of critical theory. Then to determine how the blockchain can respond to problems in learning, blockchain was defined, with its usage areas and the innovations it can bring to the field were interpreted. Finally learning and blockchain issues were synthesized, which are the focal point of this section, and how these can be used in learning applications, how they can respond to learning needs were discussed.