Reviewerly/exHarmony huggingface dataset:
Scene Text Recognition with Auto-Aligned Feature Generator

Scene text recognition has attracted increasing attention in computer vision due to its various applications. Most of the existing scene text recognition methods are under the encoder-decoder framework. In order to improve text feature learning of these methods, Generative Adversarial Networks (GANs) are recently integrated to generate clean text images without distorted letters. However, the existing GANs assume the input images are spatially aligned, while the words in natural images are often in irregular shapes. The misalignment brings a big problem for both image generation and text recognition. In this paper, we present a novel text feature alignment network to solve this problem. Our method can handle both horizontal and vertical images with irregular texts. Our proposed framework is end-to-end trainable, and extensive experiments on several public benchmarks demonstrate its superiority in terms of both effectiveness and efficiency.
Empowered, Confident, and Prepared: Driving Chatbot Product Vision Through User Research

This case study explores how a series of customer site visits to two international service centers drove design recommendations for a chatbot building platform that could encourage positive agent‐chatbot collaboration. The first part of the case focuses on the research undertaken by a team of user experience practitioners at the enterprise software company Salesforce. The team used contextual inquiry and group interviews to better understand the daily experience of customer service agents and service teams in search of ways to responsibly implement automation tools like chatbots within a service center environment. The second part of the case study highlights how the UX team applied these learnings into specific product recommendations and developed a set of principles that could drive the product forward while remaining empathetic and supportive of customer service agents.
Development of Octo-Rotor UAV Prototype with Night-vision Stereo Camera System Used for Nighttime Visual Inspection

This paper presents an octo-rotor unmanned air vehicle (UAV) prototype and its vision system used for nighttime visual infrastructure inspection. After developing a stereo vision system using two inexpensive night-vision cameras for obtaining depth information, we conducted a comprehensive evaluation experiment to assess its practical use, to support the design and manufacture of our prototype, to build a camera system including a dedicated camera mount, and to compare and evaluate stereo matching algorithms. The nighttime inspection yielded depth images using stereo matching algorithms of four types for nighttime aerial photography of parallax images. We evaluated optimal stereo matching for nighttime aerial photographs. The experimentally obtained results revealed that the contrast between structure outlines and depth information was extracted clearly for the highest accuracy stereo matching result. Results show that our system concept can open up a new field of inspecting structures using nighttime aerial photography.
Faster Privacy-Preserving Computation of Edit Distance with Moves

We consider an efficient two-party protocol for securely computing the similarity of strings w.r.t. an extended edit distance measure. Here, two parties possessing strings x and y, respectively, want to jointly compute an approximate value for $$\mathrm {EDM}(x,y)$$, the minimum number of edit operations including substring moves needed to transform x into y, without revealing any private information. Recently, the first secure two-party protocol for this was proposed, based on homomorphic encryption, but this approach is not suitable for long strings due to its high communication and round complexities. In this paper, we propose an improved algorithm that significantly reduces the round complexity without sacrificing its cryptographic strength. We examine the performance of our algorithm for DNA sequences compared to previous one.
Active Sample Selection Through Sparse Neighborhood for Imbalanced Datasets

For the imbalanced datasets, a novel biased-based active sampling learning algorithm is proposed for the first time. The algorithm combines two important sampling factors with minority confidence and instances' informativeness in active learning framework The sampling strategy aims at taking into account the selected instances' utilities while avoiding sampling invalid majority instances. For this purpose, a novel label propagation algorithm through sparse neighborhood independent of super-parameter k is proposed to calculate minority confidence. Different from other semisupervised learning methods, the algorithm learns the instances by sparse coding theory and adaptively constructs the sparse neighborhood and the sparse neighborhood graph. For calculating instances' informativeness, we propose an informativeness measure method based on the nearest boundary distance. It mainly utilizes direction vector feature and a heuristic search strategy to construct an auxiliary decision boundary. Then we evaluate the instances' informativeness based on the auxiliary decision boundary.
Study of open source web map rendering software for rendering Bhuvan High Resolution Satellite data

Recent advances in Open Source mapping software are opening up new possibilities for online visualization of satellite data. Bhuvan, Indian Geo-platform of ISRO, hosts multi-temporal, multi-sensor and multi-resolution satellite data over internet and is used for various remote sensing applications. Serving satellite images over internet with good quality and faster response is essential for better user experience and increases the use of satellite data online. Map rendering software is generally used for serving satellite data as web map tiles over internet. This software will process satellite images and generate fixed size tiles based on parameters like zoom level, projection and area of interest. In this study, we explore open source map tile rendering software Mapnik and MapServer. The overall implementation process for hosting satellite data as web map tiles is discussed in detail. Using these two software, satellite data is published with different re-sampling methods as map layers. The published map layers are compared for web tile quality and performance under different load conditions. We also discuss various possible methods for improving the performance of tile creation and rendering.
Model-Driven Development of an Augmented Reality-Based Application for English Language Teaching to Children between 9 and 12 Years Old

English is the main language for international communication. In the Mexican context, information provided by the national education authority points out that even though English language teaching is mandatory since 2017, there is not encouraging progress on the preparation of educational institutions to fulfill the requirement, leading to identify the need of developing new strategies and tools to support the process. This paper is dedicated to describing the process that was followed for the development of ARTUI, an Augmented Reality-based application to support the English language teaching process, following a Model-driven approach. The present proposal relies on the use of Tangible User Interfaces, and the content displayed on it is based on material provided by certification entities. A description of the developed tool and its evaluation in terms of usability are provided.
Air Safety Early Warning System Based on Time Series

In order to quantitatively analyze the influencing factors of flight safety of aircrew, risk warning is made before flight, so as to effectively control flight risk and build an optimization model. The traditional simulated flight early warning system relies on the basic statistical analysis and expert experience to obtain the normal sampling range of some parameters. Once the parameters sampled by the simulated sensor of the system are out of bounds, the alarm and prompt information will occur. This paper is based on the state prediction of high dimensional parameter space, which makes the system have more macroscopic and accurate prediction ability.
Classification of Risk Attitudes from Customer Behavior with Machine Learning

Every product and service in the market has its characteristic which has an impact on a consumer's decision to buy or use them. The risk is a distinctive characteristic of financial products, so in financial product and service design must use risk as a key factor. On the other hand, the consumer has different attitudes to the risk which can distinguish in 3 categories: risk aversion, risk neutral and risk seeking. Therefore, knowing risk attitudes of consumer who is the target market is an important key to define marketing strategy such as designing service and product, campaign, and promotion which going to be offered to them. Using the customer historical data, machine learning can be used to classify risk attitudes of each consumer. In this paper, we compare three machine learning methods to classify consumer's risk attitudes from their behaviors and identify important features. The results of the experiment show that the ensemble method, XGBoost, when used with resampling method ADASYN shows the best accuracy,
Toward PTNET Network Topology Analysis and Routing Algorithm Design

In recent years, data center network is used for transmission, storage and processing of big data, which plays an important role for applications in cloud computing and CDN distribution. Network topology and routing algorithm are its core research content and key technical issues. The traditional network topology is difficult to guarantee the quality of service in scalability and fault tolerance. The server-centric data center network topology can ensure the scale of the data center network by recursively increasing the number of network nodes and links. relative to the Dcell, BCube, and BCCC typical network topology, PTNet network as a typical representative of a new type of the server-centric data center network topology, which has more advantages in scalability, fault tolerance and so on. Multicast and broadcast in data center network have more application scenarios and use value. For example, the video conference online, multimedia remote education and other development are inseparable from the application and promotion of network multicast and broadcast. So it is necessary to research the routing algorithms of multicast and broadcast in the network. Based on the deep research of PTNet network, this paper analyzes and researches the network topology, multicast and broadcast routing algorithm.
Pseudo-stochastic EM for sub-Gaussian α-stable mixture models

Due to the non-existence of a closed-form expression for sub-Gaussian α-stable densities, the M-step of the Expectation-Maximization (EM) algorithm for the Sub-Gaussian α-Stable Mixture Models (SGαSMMs) is intractable, and the EM algorithm for SGαSMM is still an open problem. SGαSMM can model non-homogeneous Gaussian data, accommodate outliers, and high leverage data points, which are concepts of primary importance in robust mixture models. These models are robust and useful tools in modeling heterogeneous data with outlier observations, such as clustering financial or impulsive data. In this paper, a new EM algorithm based on a combination of EM (the first part) and stochastic EM (the second part) algorithms, is used to obtain the maximum likelihood estimators of the parameters of SGαSMM in the M-step. In the first part, the model parameters, except αs, are estimated from an analytical form via EM. In the second part, based on a stochastic EM, the maximum likelihood estimator of α, in each component, is calculated from pseudo-simulated data obtained by suitable rejection sampling. The efficiency of the proposed algorithm is illustrated by using both real and simulated data.
Light Weighted Mutual Authentication and Dynamic Key Encryption for IoT Devices Applications

The unambiguous and unseamed integration of mixed and non-identical end systems are offered by the Internet of Things (IoT). It has been greatly used in many applications such as network automobile, micro-controller, smart farming, smart retail, wearable devices, and industrial internet. In these applications, a substantial number of IoT devices are established that can sense, actuate, evaluate and publicize. There could be a possible violation of the IoT devices also by capturing the servers or the sensors that is, an intruder may affect the sensor and record false data, or an intruder may capture the servers and incorrectly actuate sensors. Therefore, there is significant need of an strong but light weighted mutual authentication scheme. Following this, authentication is done before the data exchange among sensors and the server. Moreover, due to limited capacity of batteries in IoT devices, the mutual authentication protocol is expected to be light weighted. This work proposed an authentication scheme by using the dynamic cipher along with the existing public key encryption, where the client generates the key dynamically, which can only be solved by brute force. The key which is dynamically generated using random function is used for encryption and decryption. Further, an enhancement to the security is proposed by introducing dynamic cipher based authentication. The proposed scheme is evaluated in software environments i.e., IPv6 over low-power wireless personal area networks (6LoWPAN) which is widely used for IoT applications.
Conditional Contrast High Itemset Mining for Two Dataset in Two Time Slot

In various organizations, different amount of profits is obtained by selling different types of data of different quantities collected from multisource. The products have different profit rates based on the frequency of selling, i.e., some may be rarely sold and profit for them is less, however, some of these items are sold frequently and profit for them is high. In general, High Utility Itemset (HUI) mining considers profit and item quantities in transactions. To evaluate these frequent itemset, Frequent Pattern Mining (FPM) technique is considered which is based on finding out the frequency of the items to occur repeatedly within the dataset. In this work, Conditional Contrast High Itemset Tree (CCHIT) is used to store database information. The proposed tree finds out itemset of data, combines them thereafter few conditions are applied for pruning of itemset that is obtained from multi-source data.
Stick Buffer Cache v2: Improved Input Feature Map Cache for Reducing off-chip Memory Traffic in CNN Accelerators

Data movement between the Convolutional Neural Network (CNN) accelerators and off-chip memory is critical with respect to the overall power consumption. Minimizing power consumption is particularly important for low power embedded applications. Specific CNN compute patterns offer a possibility of significant data reuse, leading to idea of using specialized on-chip cache memories which enable significant improvement in power consumption. However, due to unique caching pattern present within CNNs, standard cache memories would not be efficient. In this paper novel on-chip cache memory architecture, based on idea of input feature map striping, is proposed, which requires significantly less on-chip memory resources compared to previously proposed solutions. Experiment results show that the proposed cache architecture can reduce on-chip memory size by a factor of 16 or more, while increasing power consumption no more than 15%, compared to some of previously proposed solutions.
Photo Annotation: A Study on Automatic Photo Management Based on Contextual Information

The increasing number of photos that people keep in their digital data storage devices often get scattered and it gets quite difficult to keep track of them effectively in time of need or to manage them efficiently. We present a process of photo annotation based on the metadata of the images and have developed a photo management application that derives information of location, daylight, weather, event name and persons related to the photo through external application programming interfaces (API) and libraries from the respective images metadata. These informations are annotated against the photos and saved into the database of the application so that the photos can be brought into the users view when they are sought according to these informations i.e. the location or event on which a particular photo was taken. We have also created a test database of 1350 images and our application has the accuracy of 82% for Daylight, 27.85% for events, 95% for locations, 40.75% for persons and 80% for weather data. The test images are taken from a span of two years and are of 40 events, 45 locations and of 25 camera models.
International Students’ Gender Impact to Use Mobile Learning in Tertiary Education

This study aims to design a model on mobile learning used by international students in tertiary education. It has used a survey questionnaire distributed through the LimeSurvey. A total of 12 international students from a tertiary education (6 female students and 6 male students) participated in Canada. The results revealed that male students got higher impact to the performance expectancy (PE), social factors (SF), and facilitating condition (FC) but on the other hand, female students got an impact on PE and FC. Furthermore, female students got a significant impact on PE and SF on behavioral intention (BI). Gender is having an impact on EE and FC. Finally, PE and SF have an impact on the BI to use mobile learning in tertiary education. These findings will help practitioners, educators, policymakers to implement mobile learning in tertiary education for international students in Canada and abroad.
Quantitative Assessment of Remote Code Execution Vulnerability in Web Apps

With the exponentially increasing use of online tools, applications that are being made for day to day purpose by small and large industries, the threat of exploitation is also increasing. Remote Code Execution (RCE) is one of the topmost critical and serious web applications vulnerability of this era and one of the major concerns among cyber threats, which can exploit web servers through their functionalities and using their scripts/files. RCE is an application layer vulnerability caused by careless coding practice which leads to a huge security breach that may bring unwanted resource loss or damages. An attacker may execute malicious code and take complete control of the targeted system with the privileges of an authentic user with this vulnerability. Attackers can attempt to advance their privileges after gaining access to the system. Remote Code Execution can lead to a full compromise of the vulnerable web application as well as the web server. This chapter highlights the concern and risk needed to put under consideration caused by RCE vulnerability of a system. Moreover, this study and its findings will help application developers and its stakeholders to understand the risk of data compromise and unauthorized access to the system. An exploitation algorithm is proposed to identify RCE vulnerability in web application. Then based on it, around 1011 web applications were taken under consideration and experiments were conducted by following manual double blinded penetration testing strategy. The experiments show that more than 12% of web application were found vulnerable to RCE. This study also explicitly listed the critical factors of Remote Code Execution vulnerability and improper input handling. The experimental results are promising to motivate developers to focus on security enhancement through proper and safe input handling.
A Novel Approach for Human Fall Detection and Fall Risk Assessment

There are several studies concerned in providing quality health care services to all people. Among them, human fall detection systems play an important role, because fall is the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. The three basic approaches used to develop fall detection systems include some sort of wearable, ambient or non-invasive based devices. Most of such systems are very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. Thus, this study proposes a non-invasive fall detection system based on the height, velocity, statistical analysis, fall risk factors and position of the subject from depth information. The proposed algorithm also utilizes fall risk factors of the user, during fall detection process to classify the subject with chances of fall such as high fall risk or low fall risk level. Thus, making the system adaptable to the physical condition of the user. The proposed system also performs a fall injury assessment after the fall event to alert for appropriate assistance and includes a fall risk assessment tool which can work independently to predict falls or to classify people with their fall risk levels. From the experimental results, the proposed system was able to achieve an average accuracy of 98.3% with sensitivity of 100% and specificity of 97.7%.
A Survey on Precision Treatment for Humans Using Cognitive Machine Learning Techniques

Personalized treatment (PT) is an emerging area in healthcare that provides personalized health. Personalized, targeted, or customized treatment gains more attention by providing the right treatment to the right person at the right time. Traditional treatment follows a whole systems approach, whereas PT unyokes the people into groups and helps them in rendering proper treatment based on disease risk. In PT, case by case analysis identifies the current status of each patient and performs detailed investigation of their health along with symptoms, signs, and difficulties. Case by case analysis also aids in constructing the clinical knowledge base according to the patient's needs. Thus, PT is a preventive medicine system enabling optimal therapy and cost-effective treatment. This chapter aims to explore how PT is served in works of literature by fusing machine learning (ML) and artificial intelligence (AI) techniques, which creates cognitive machine learning (CML). This chapter also explores the issues, challenges of traditional medicine, applications, models, pros, and cons of PT.
Feature Map Swap: Multispectral Data Fusion Method for Pedestrian Detection

This paper proposes a novel multispectral data fusion method for pedestrian detection. For all-day vision, a fusion of CCD and Infrared (IR) sensors are inevitable, and fusion of heterogeneous data based on a Convolutional neural network (CNN) is only based on the feature map concatenation of parallel CNN architecture. However, concatenation that is simply applied has a problem in that it can not fully utilize multispectral data in a deep network architecture. Therefore, this paper proposes a method called Feature Map Swap (FMS) of swapping feature maps in addition to concatenation. The proposed method can use multispectral data more efficiently by facilitating learning, by swapping the different domain weights of feature maps besides to concatenation. Also, the proposed method does not require any modifications, such as adding or removing layers to an already configured network architecture. Experimental results show that the performance of the KAIST multispectral pedestrian dataset is improved by about 7-10% based on log-average miss rate compared to simple concatenation.