Reviewerly/exHarmony huggingface dataset:
Face Recognition from Sequential Sparse 3D Data via Deep Registration

Previous works have shown that face recognition with high accurate 3D data is more reliable and insensitive to pose and illumination variations. Recently, low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and DoE based structured light systems enable us to access 3D data easily, e.g., via a mobile phone. However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly. In this paper, we aim at achieving high-performance face recognition for devices equipped with such modules which is very meaningful in practice as such devices will be very popular. We propose a framework to perform face recognition by fusing a sequence of low-quality 3D data. As 3D data are sparse and noisy which can not be well handled by conventional methods like the ICP algorithm, we design a PointNet-like Deep Registration Network(DRNet) which works with ordered 3D point coordinates while preserving the ability of mining local structures via convolution. Meanwhile we develop a novel loss function to optimize our DRNet based on the quaternion expression which obviously outperforms other widely used functions. For face recognition, we design a deep convolutional network which takes the fused 3D depth-map as input based on AMSoftmax model. Experiments show that our DRNet can achieve rotation error 0.95° and translation error 0.28mm for registration. The face recognition on fused data also achieves rank-1 accuracy 99.2%, FAR-0.001 97.5% on Bosphorus dataset which is comparable with state-of-the-art high-quality data based recognition performance.
JARVIS: An interpretation of AIML with integration of gTTS and Python

This paper presents JARVIS, a virtual integrated voice assistant comprising of gTTS, AIML[Artificial Intelligence Markup Language], and Python-based state-of-the-art technology in personalized assistant development. JARVIS incorporates the power of AIML and with the industry-leading Google platform for text-to-speech conversion and the voice of the Male Pitch in the gTTS libraries inspired from the Marvel World. This is the result of the adoption of the dynamic base Pythons pyttsx which considers intentionally in adjacent phases of gTTS and AIML, facilitating the establishment of considerably smooth dialogues between the assistant and the users. This is a unique result of the exaggerated contribution of several contributors such as the feasible use of AIML and its dynamic fusion with platforms like Python[pyttsx] and gTTS[Google Text to Speech] resulting into a consistent and modular structure of JARVIS exposing the widespread reusability and negligible maintenance.
Design and Development of a PBL Mobile Application in Islamic Education: A Conceptual Framework

This article aims to design and develop a Problem Based Learning (PBL) Mobile Application for teaching and learning in Islamic Education at the Polytechnic Brunei Darussalam.The development methodology-based application that divides the ADDIE model of instructional development activities into five phases: Analysis, Design, Development, Implementation and Evaluation.Each phase is explained in detail to bring out what action is taken on each phase.ADDIE model has listed guidelines that should be implemented detailed and systematically which involves analysis, design, development, implementation and evaluation.The priority aspects such as the content, learning theory and learning strategies are taken into account in analysing and designing.The researchers applied the behaviorism and constructivist theory as the learning theory.While PBL strategies are embedded in the application throughout the designing and development phase.Development process of an effective mobile application is not easy or takes shorter time but, it requires detailed and systematic planning.A model conceptual framework is constructed to help researchers to make the action more regular.At the end of the study, the authors present each element of the conceptual framework for the design and development a mobile application in teaching and learning in Islamic Education.
Universal Semantic Web Assistant based on Sequence to Sequence Model and Natural Language Understanding

Virtual Assistants are complex systems that try to mimic the conversation with a user through textual inputs or voice commands. Chatbots are subsets of virtual assistant and the names can be used interchangeably. Web based Virtual Assistants offer platform independency and requires a client such as, a browser to open the chatbot. Online virtual assistants or chatbots offer a lot of use-cases and can be employed in online Businesses to offer customer services and to improve customer experience. Web based virtual assistant can enable basic automations that are repetitive in nature. chatbots behave as a part of semantic search and learn through their past experiences. Chatbot can be built either as a specialist chatbot or a generalized one. Our Web based Virtual Assistant is both a generalist and a specialist in the retail and ecommerce domain. Our virtual assistant attempts to answer the users basic queries and applies efficient Natural Language Processing (NLP) techniques to extract the context of the user query. The virtual assistant scrapes the Web and queries the database through efficient web crawlers and through the Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) modules respectively. Each web crawlers are small programs written to crawl certain part of the web and extract useful information in unstructured data format. Efficient Machine Learning and Deep Learning techniques are applied to extract and analyze the data.
Sentiment Analysis for University Students’ Feedback

Students’ feedbacks have been an essential part of a range of higher educations as universities in Vietnam recently. This information could be about lectures, facilities, curriculum and how to improve them. The feedback is collected and processed manually at the end of each semester. We proposed to build a system which is ability to categorize students’ feedbacks automatically. This system helps us save time, human resource and money for any higher education institutions. Firstly, we created university students’ feedbacks data in two years and organized them into three classes: Positive, Negative and Neutral. We built the Vietnamese sentiment dataset with 5000 classified sentences. Then, we use three classifiers which are Naïve Bayes, Maximum Entropy and Support Vector Machine on our annotated data. The result proves that Maximum Entropy algorithm is better than Naïve Bayes and Support Vector Machine with the best score of 91.36%. With high accuracy, we confidently implement our results to develop the students’ feedbacks system to detect students’ opinions. With negative and positive students’ opinions, we can adjust and improve the lectures, facilities, curriculum and make the quality of university better over the years.
Analysis of Comments on Youtube Videos using Hadoop

YouTube is a popular video sharing portal of today's time which also allows users to provide their feedback in form of comments, likes and subscriptions. Analysis of this feedback plays a crucial role in enhancement of video content to suit the need of the time. The comments, in specific, can highly benefit a new user in forming an informed and decisive opinion about a particular video. The work presents a system to perform a detailed analysis and classification of the comments on YouTube videos. The comments corresponding to videos have been ingested into the Hadoop Distributed File System (HDFS) and queried with the Hadoop analytical software, Hive. Further, sentiment analysis of these comments has been performed using Python. The proposed system has been evaluated by executing multiple self-designed queries on the YouTube data. Results of execution times of these queries have been tabulated and presented in the form of graphs. Further, results of sentiment analysis of comments that determine the reaction to the video, which are encouraging.
A novel double spending attack countermeasure in blockchain

A blockchain database containing files regarding transactions of cryptocurrency is sometime vulnerable to double spending attack. This type of attack pertains to a coin being spent more in more that one transaction in the network. This paper is motivated by a goal to create a blockchain that can withstand double spending attacks. This way, honest miners will be able to safely and securely exchange cryptocurrency. There currently lack valuable prevention methods in the network therefore we designed a novel countermeasure to combat double spending attacks on the blockchain system. We proposed the MSP (Multistage Secure Pool) framework in order to address the vulnerabilities on the blockchain. This was designed to handle both discrete and general issues that affect the overall security of the blockchain. Our evaluation using this application shows that there was a decrease in the amount of attacks propagating through the system based on our system's robustness and capabilities. We also present machine learning capabilities of the system in our study in order to enable a progressive aspect to the design. Providing our application with the ability to analyze data in order to recognize and classify distinct actions will enable for greater comprehension. An application that learns, updates and configures to meet specified defensive standards present key design features which enables for greater understanding and future analysis of the overall blockchain network.
Bringing Semantic Knowledge Graph Technology to Your Data

The most used knowledge graph today is Google Search. What makes Google Search powerful is its ability to derive knowledge by finding a correlation between the concepts in a search query as opposed to a correlation between words (also known as <i>from strings to things</i>).<sup>1</sup> In a Google search, Google is able to increase the relevance of search results by making connections in its knowledge graph that exceed the possibilities of textual search.
Collaborative Deep Neural Network for Printed Text Recognition of Indian Languages

In this paper, we evaluated the recognition performance of CRNN (Convolutional Recurrent Neural Network) on Indian language printed documents. We compared the current CRNN performance with MDLSTM (2 dimensional LSTM) and Tesseract (LSTM) on same test bed. The CRNN outperformed the previous approaches. Several experiments are done on 7 Indian languages i.e. Hindi, Marathi, Tamil, Kannada, Malayalam, Bangla and Gurumukhi. This CRNN architecture is feed with pre-segmented lines. Dataset used contained approximately 5000 pages for each language which were then divided into training, validation and testing set. The proposed fused network i.e. CRNN, does the task of feature extraction and sequence labeling as a single unit. Wherein, Convolutional Neural Network (CNN) is pitched in especially to eliminate dependency of hand crafted features. It directly processes 2D image (binarized) of each line and extracts features based on the learning kernels. Then, Recurrent Neural Network (RNN) with BLSTM (bi directional LSTM) architecture is applied on learned features to produce inference/ sequence probabilities. The output of CTC layer transcripts these probabilities into Unicode range of the evaluated language and one blank label. These layers and their parameters are empirically selected and kept same for all the languages.
Exploring Usability and Gratifications for Virtual Reality Applications at Festivals

The digital creation of virtual environments has opened many doors for the creation of new experiences, offering the trend of ever immersive, engaging, and multisensory virtual reality (VR) experiences. However, studies on VR within the science festival context are limited. Therefore, with a focus on usability and gratifications, this study aims to explore the antecedents of the behavioral intention to use VR at science festivals. Before participating in a questionnaire, 447 users experienced a VR application and data were analyzed using structural equation modeling. Findings reveal that usability influences gratification factors, which influence users' behavioral intention to engage with science.
Detection of Falls with Smartphone Using Machine Learning Technique

As the population aging issue becomes more serious these days, fall detection of the elderly has been attracting a great deal of interests. A fall detection system includes data collection, data pre-processing, feature extraction, feature selection, and then activity classification. In this context the researchers have conducted studies on acceleration-based fall detection using external accelerometer or built-in sensors in smartphone. In this paper a novel approach for fall detection using machine learning technique is proposed, which employs a new pre-processing technique to remove noise from sensor data. It mainly consists of two processes: short-term smoothing to remove the short term vibration and long-term smoothing to smooth sensor readings captured in a longer time window. To detect falls, statistical models are proposed to extract the features. A public dataset, MobiFall, is used for performance evaluation, which contains the data of accelerometer and gyroscope with the orientation along each axis in the smartphone coordination system. With the selected features, the proposed scheme identifies falls from the activities of daily living with a high accuracy of up to 98.3%. Moreover, tFall dataset is also used to perform a cross verification of the proposed scheme.
Distributed Image Encryption Based On a Homomorphic Cryptographic Approach

The objective of this research is to develop a novel image encryption method that can be used to considerably increase the security of encrypted images. To solve this image security problem, we propose a distributed homomorphic image encryption scheme where the images of interest are those in the visible electromagnetic spectrum. In our encryption phase, a red green blue (RGB) image is first separated into its constituent channel images, and then the numerical intensity value of a pixel from each channel is written as a sum of smaller pixel intensity sub-values, leading to having several component images for each of the R, G, and B-channel images. A homomorphic encryption function is used to separately encrypted each of the pixel intensity sub-values in each component image using an encryption key, leading to a distributed image encryption approach. Each of the encrypted component images can be compressed before transmission and/or storage. In our decryption phase, each encrypted component image is decompressed if necessary, and then the homomorphic property of the encryption function is used to transform the product of individually encrypted pixel intensity sub-values in each encrypted component images, to the encryption of their sum, before applying the corresponding decryption function with a decryption key to recover the original pixel's intensity values for each channel image, and then recovering the original RGB image. Furthermore, a special case of an RGB image encryption and decryption where a pixel's intensity value from each channel is written as a sum of only two sub-values is implemented and simulated with a software. The resulting cipher-images are subject to a range of security tests and analyses. Results from these tests shown that our proposed homomorphic image encryption scheme is robust and can resist security attacks, as well as increases the security of the associated encrypted images. Our proposed homomorphic image encryption scheme has produced highly secure encrypted images.
An Automated Security Analysis Framework and Implementation for MTD Techniques on Cloud

Cloud service providers offer their customers with on-demand and cost-effective services, scalable computing, and network infrastructures. Enterprises migrate their services to the cloud to utilize the benefit of cloud computing such as eliminating the capital expense of their computing need. There are security vulnerabilities and threats in the cloud. Many researches have been proposed to analyze the cloud security using Graphical Security Models (GSMs) and security metrics. In addition, it has been widely researched in finding appropriate defensive strategies for the security of the cloud. Moving Target Defense (MTD) techniques can utilize the cloud elasticity features to change the attack surface and confuse attackers. Most of the previous work incorporating MTDs into the GSMs are theoretical and the performance was evaluated based on the simulation. In this paper, we realized the previous framework and designed, implemented and tested a cloud security assessment tool in a real cloud platform named UniteCloud. Our security solution can (1) monitor cloud computing in real-time, (2) automate the security modeling and analysis and visualize the GSMs using a Graphical User Interface via a web application, and (3) deploy three MTD techniques including Diversity, Redundancy, and Shuffle on the real cloud infrastructure. We analyzed the automation process using the APIs and showed the practicality and feasibility of automation of deploying all the three MTD techniques on the UniteCloud.
A similarity-based two-view multiple instance learning method for classification

Multiple instance learning (MIL) has been proposed to classify the bag of instances. In practice, we may meet the problems which have more than one view data. For example, in the image classification, textual information is always used to describe the image, which can be considered as two-view data. In this paper, we propose a new similarity-based two-view multi-instance learning (STMIL) method that can incorporate two-view data into learning so as to improve classification accuracy of MIL. In order to obtain the predictive classifier, we first convert the proposed model into a convex optimization problem, and then propose a new alternative framework to solve the proposed method. We then analyze the convergence of the proposed STMIL method. The experiments have been conducted to compare the performance of our proposed method and the previous methods. The results show that our method can deliver superior performance than other methods.
Intelligent glove for sign language communication

Communication is very important in daily life while many people in Thailand cannot communicate by talking. On the other hand, they can communicate only sign language. The sign language used in a specific group of people, but other people cannot understand. Therefore, we decided to do a project about glove for sign language communication. This research created a hand glove for deaf and mute people to provide better communication between disabling and normal people. We applied computer programming by used the flex sensor and GY-521 Module. The flex sensor has measured the bent of fingers and generated an analog output. GY-521 Module is use for measured the direction and movement of the hand. After got data from both sensors, it will send to Arduino IDE converted all the data to alphabet and text before converted to speech. Then we tested the accuracy of the glove, it has accuracy about 70 - 100% which depends on another factor. From this process, we got a hand glove for sign language communication. This glove will help deaf and mute people to communicate more efficiently.
Autonomous Last Mile Shuttle ISEAUTO for Education and Research

The article introduces an educational and research project ISEAUTO, targeted to using self-driving cars to solve urban mobility issues. The project focusses on the design and development of an autonomous shuttle as a collaboration between academic staff of the university, students, and a partner company. The article presents an account of the experience of developing vehicle from scratch in one year using a stock electric vehicle, widely available sensors and open source software. Technical solutions based on the latest trends in autonomous mobility are conferred, special attention is given to control and software architectures. In addition to reaching the goal of making the shuttle drive autonomously by the end of the first year of the project, it was possible to combine various tasks with teaching and award more than 460 ECTS to participating students. The project continues and a commercial version of the vehicle is in development.
Theories of Conversation for Conversational IR

Conversational information retrieval is a relatively new and fast-developing research area, but conversation itself has been well studied for decades. Researchers have analysed linguistic phenomena such as structure and semantics but also paralinguistic features such as tone, body language, and even the physiological states of interlocutors. We tend to treat computers as social agents—especially if they have some humanlike features in their design—and so work from human-to-human conversation is highly relevant to how we think about the design of human-to-computer applications. In this article, we summarise some salient past work, focusing on social norms; structures; and affect, prosody, and style. We examine social communication theories briefly as a review to see what we have learned about how humans interact with each other and how that might pertain to agents and robots. We also discuss some implications for research and design of conversational IR systems.
Multi-task Deep Reinforcement Learning for Scalable Parallel Task Scheduling

The rapid development of artificial intelligence in multiple scenarios, including machine learning, image recognition, and autonomous driving, has led to an explosion of computation jobs. These jobs are often divided into parallel child tasks and executed in distributed clusters with limited computing resources, making parallel task scheduling one of the most important research topics nowadays. Most studies about parallel task scheduling focused on formulating special scenarios and service requirements as optimization problems. However, the complicated and dynamic parallel computing environments are hard to model, predict and control, making those previous methods unscalable and unable to reflect the real scenarios. In this paper, a Multi-task Deep reinforcement learning approach for scalable parallel Task Scheduling (MDTS) is firstly devised. Generally, Deep Reinforcement Learning (DRL) is a model-free optimization algorithm for long-term control by leveraging experience, but it suffers the curse of dimensionality for decision when coping with complex parallel computing environments and jobs with diverse properties. We extend the action selection in DRL to a multi-task decision, where the output branches of multitask learning are fine-matched to parallel scheduling tasks. Child tasks of a job are accordingly assigned to distributed nodes without any human knowledge while the resource competition among parallel tasks is leveraged through shared neural network layers. Extensive experiments show that the MDTS significantly reduces the job execution time compared with least-connection scheduling and particle swarm optimization algorithm by 15.3% and 39.8% respectively. Moreover, MDTS outperforms the raw DRL algorithm on job execution time, load imbalance value, and total cost by 42.8%, 47.5%, and 59.0%.
An Effective Data Normalization Strategy for Academic Datasets using Log Values

Data transformation is converting input data from one form to another form. Normalization is a standard data transformation techniques which can be used to transform data. There are many types of normalization techniques, like Min-Max normalization, Cube Root normalization, etc. In this paper, we implement a two-phase normalization process. In the first phase, the data is normalized using Cube Root normalization. Next, the normalized data is still more scaled by applying the logarithmic transformation. Finally, the performance of the transformed values is evaluated against standard metrics.