Reviewerly/exHarmony huggingface dataset:
Automatic Identification and Production of Related Words for Historical Linguistics

Language change across space and time is one of the main concerns in historical linguistics. In this article, we develop tools to assist researchers and domain experts in the study of language evolution. First, we introduce a method to automatically determine whether two words are cognates. We propose an algorithm for extracting cognates from electronic dictionaries that contain etymological information. Having built a data set of related words, we further develop machine learning methods based on orthographic alignment for identifying cognates. We use aligned subsequences as features for classification algorithms in order to infer rules for linguistic changes undergone by words when entering new languages and to discriminate between cognates and non-cognates. Second, we extend the method to a finer-grained level, to identify the type of relationship between words. Discriminating between cognates and borrowings provides a deeper insight into the history of a language and allows a better characterization of language relatedness. We show that orthographic features have discriminative power and we analyze the underlying linguistic factors that prove relevant in the classification task. To our knowledge, this is the first attempt of this kind. Third, we develop a machine learning method for automatically producing related words. We focus on reconstructing proto-words, but we also address two related sub-problems, producing modern word forms and producing cognates. The task of reconstructing proto-words consists of recreating the words in an ancient language from its modern daughter languages. Having modern word forms in multiple Romance languages, we infer the form of their common Latin ancestors. Our approach relies on the regularities that occurred when words entered the modern languages. We leverage information from several modern languages, building an ensemble system for reconstructing proto-words. We apply our method to multiple data sets, showing that our approach improves on previous results, also having the advantage of requiring less input data, which is essential in historical linguistics, where resources are generally scarce.
The Golden Bullet: A Comparative Study for Target Acquisition, Pointing and Shooting

In this study, we evaluate an interaction sequence performed by six modalities consisting of desktop-based (DB) and virtual reality (VR) environments using different input devices. For the given study, we implemented a vertical prototype of a first person shooter (FPS) game scenario, focusing on the genre-defining point-and-shoot mechanic. We introduce measures to evaluate the success of the according interaction sequence (times for target acquisition, pointing, shooting, overall net time, and number of shots) and conduct experiments to record and compare the users' performances. We show that interacting using head-tracking for landscape-rotation is performing similarly to the input of a screen-centered mouse and also yielded shortest times in target acquisition and pointing. Although using head-tracking for target acquisition and pointing was most efficient, subjects rated the modality using head-tracking for target acquisition and a 3DOF Controller for pointing best. Eye-tracking (ET) yields promising results, but calibration issues need to be resolved to enhance reliability and overall user experience.
An IoT Architecture of Microservices for Ambient Assisted Living Environments to Promote Aging in Smart Cities

Ambient Assisted Living (AAL) environments encompass technical systems and the Internet of Things (IoT) tools to support seniors in their daily routines. They aim to enable seniors to live independently and safely for as long as possible when faced declining physical or cognitive capacities. This work presents the design, development and deployment of an AAL system in the context of smart cities. The proposed architecture is based on microservices and software components. We examined the requirements and specifications of AAL systems in smart homes, in efforts to describe and evaluate how they would be transposable in the case of smart cities. The system has been tested and evaluated in the laboratory; it has been deployed in real life settings within city and is still in use by five elderly people.
A content-location-aware public welfare activity information push system based on microblog

Microblog has become a widely used social media tool, which can help users publish or obtain Public Welfare Activity (PWA) information. Due to the characteristics of large-scale and arbitrary in microblog use, a large amount of messy information is produced, which makes it difficult to discover interesting PWA information. Thus, developing a push system, which can seek a certain number of local potential users for a PWA to be Pushed (PWAP), would be a great help. However, it is challenging to discover users explicitly interested in PWAs and obtain a certain number of local potential users. In this paper, a Content-Location-Aware PWA information Push system based on microblog (CLAP) is proposed, which can seek a certain number of local potential users for a PWAP from massive microblog users, and push the corresponding information to them. Specifically, a topic model and keyword search based method is firstly utilized to discover users who are explicitly interested in the PWAP (i.e., seed users). Secondly, a distance metric learning based method is utilized to obtain a certain number of local potential users by measuring the similarities between seed users and local users. Experimental results demonstrate that the proposed system has a potential to generate superior results.
Some Cyberpsychology Techniques to Distinguish Humans and Bots for Authentication

In a world where artificial intelligence is one of the greatest assets, unmanned operations seem to be the future. The world of cybersecurity is witness to numerous system break-ins for the purpose of gaining access. One of the ways to gain access to systems is fulfilled by authentication, the process where an entity verifies who he or she claims to be to access a system. With network traffic increasing day by day, the bots form a huge chunk of the network traffic. Over the last few years, bots have been trained to imitate human beings to gain access to computer based systems. Traditional authentication methods are based on what we know, who we are and what we have, and can be bypassed easily these days. Bots have been known to imitate human beings in order to gain access to systems by identifying captchas and picture based authentication systems. A bot gaining access to sensitive data may have severe repercussions. Thus there is a need to introduce certain parameters that could easily tell apart a bot and a human being. One of the primary factors that differentiates bots and human beings is the kind of psychology embedded in humans. In this paper, we identify a set of cyberpsychological factors that could differentiate human beings from bots. This may prove to be beneficial for the purpose of authentication where a human being may be able to authenticate himself/herself based on his/her psychology, whereas a bot may not be able to do the same because of the lack of its thinking capabilities. Therefore, the issue focuses on three important domains, that is, cybersecurity, artificial intelligence and cyberpsychology.
Space-Time Vehicle Tracking at the Edge of the Network

While large number of cameras have been deployed on campus, governments and restricted areas to protect the safety of residents and their properties, manually searching for the track of a suspicious human or vehicle is painful and prone to errors. An intelligent camera surveillance system is crucial to make the usage of the large-scale camera system efficient and smooth. In this paper, we present our Space-Time Vehicle Tracking system (STVT) which targets to track all vehicles over time and store the result as trajectories of vehicles in a graph database. We are then able to respond to queries such as Where did this suspicious vehicle come from? and Where is it headed to? directly from the stored trajectories. STVT makes use of computer vision techniques including Yolo v2, Dlib's correlation tracker, and adaptive histogram to transform each video stream into a stream of vehicle detection events. A publish-subscribe-based messaging system is built above the camera networks to enable communicating vehicle detection events between cameras. A graph database is used to store the vehicle detections and connect them into trajectories of vehicles. We have a built a prototype of STVT and are currently assessing its effectiveness with a small subset of cameras. Specifically, we process video streams from 7 Georgia Tech's on-campus street cameras with STVT, and identify new challenges and future works in a real-world deployment.
Super resolution of DS-2 satellite imagery using deep convolutional neural network

Nowadays, Satellite images are used for various analysis, including building detection and road extraction, which are directly beneficial to governmental applications, such as urbanization and monitoring the environment. Spatial resolution is an element of crucial impact on the usage of remote sensing imagery. High spatial resolution means satellite images provide more detailed information. To improve the spatial resolution at the sensor level, many factors are ought to be taken into consideration, such as the manufacturing process. Moreover, once the satellite is launched, no further action can be taken from the perspective of hardware. Therefore, a more practical solution to improve the resolution of a satellite image is to use Single Image Super Resolution (SISR) techniques. This research proposal deals with the re-design, implementation, and evaluation of SISR technique using Deep Convolutional Neural Network with Skip Connections and Network in Network (DCSCN) for enlarging multispectral remote sensing images captured by DubaiSat-2 (DS-2) and estimating the missing high frequency details. The goal is to achieve high performance in terms of quality, and to test whether training the network using luminance channel only, which is extracted from YCbCr domain, can achieve high quality results. For this purpose, DCSCN is trained, evaluated, and tested using a dataset collected from DS-2. A single low resolution DS-2 image is used to construct its high resolution version by training the model from scratch and fine-tuning its hyper-parameters to produce optimal results. The performance is evaluated using various quality indices, such as Structural Similarity Index Measurement (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Wavelet domain Signal-to-Noise Ratio (WSNR). The performance is compared to other state-of-the-art methods, such as Bil-inear, Bi-cubic, and Lanczos interpolation.
Service-oriented Application Composition with Evolutionary Heuristics and Multiple Criteria

The need to create and deploy business application systems rapidly has sparked interest in using web services to compose them. When creating mission-critical business applications through web service compositions, in addition to ensuring that functional requirements are met, designers need to consider the end-to-end reliability, security, performance, and overall cost of the application. As the number of available coarse-grain business services grows, the problem of selecting appropriate services quickly becomes combinatorially explosive for realistic-sized business applications. This article develops a business-process-driven approach for composing service-oriented applications. We use a combination of weights to explore the entire QoS criteria landscape through the use of a multi-criteria genetic algorithm (GA) to identify a Pareto-optimal multidimensional frontier that permits managers to trade off conflicting objectives when selecting a set of services. We illustrate the effectiveness of the approach by applying it to a real-world drop-ship business application and compare its performance to another GA-based approach for service composition.
Unified Modeling of Imputation, Forecasting, and Prediction for AD Progression

In this paper, we propose a novel deep recurrent neural network as an Alzheimer’s Disease (AD) progression model, capable of jointly conducting tasks of missing values imputation, phenotypic measurements forecast, and clinical state prediction of a subject based on his/her longitudinal imaging biomarkers. Unlike the existing methods that mostly ignore missing values or impute them by means of an independent statistical model before training a disease progression model, we devise a unified recurrent network architecture for jointly performing missing values imputation, biomarker values forecast, and clinical state prediction from the longitudinal data. For these tasks to be handled in a unified framework, we also define an objective function that can be efficiently optimized by means of stochastic gradient descent in an end-to-end manner. We validated the effectiveness of our proposed method by comparing with the comparative methods over the TADPOLE challenge cohort.
A Systematic Review of Current Trends in Web Content Mining

Abstract Knowledge in web documents, Relevance ranking of webpages and so on are some of the under-researched areas in web content mining (WCM). Apart from the general data mining tools used for knowledge discovery in web, there have been few attempts at reviewing WCM and these were from the perspective of the methods used and the problems solved but not in sufficient depth. This existing literature review attempts does not also reveal which problems have been under-researched and which application area has the most attention when it gets to WCM. The goal of this systematic review is to make available a comprehensive and semi-structured overview of WCM methods, problems and solutions proffered. To provide a comprehensive literature review on this subject, 57 publications which include journals, conferences proceeding, and workshops were considered between the periods of 1999-2018. The findings reveal that updating dynamic content, efficient content extraction, eliminating noise blocks etc remain the most prominent challenges associated with WCM with a very high attention on solving these problems in a more efficient manner. Also, most of the solutions proffered to the problems still come with their various limitations which make this area of research fertile for future research. Caching dynamic web data. With regard to content, the techniques used for content extraction in WCM consist of used Data Update Propagation (DUP), Association rule, Object Dependence Graphs, classification techniques, Document Object Model, Vision-Based Segmentation, Hyperlink-Induced Topic Search and so on. Finally, the study revealed that WCM has been mostly applied to general websites which include random webpages seeking to extract specific parameters. The review was able to identify the limitations of the current research on the subject matter and identify future research opportunities in WCM.
Key Escrow Protocol Based on a Tripartite Authenticated Key Agreement and Threshold Cryptography

While instant messaging systems bring convenience to people's lives and work, they also make it easier for malicious users to discuss and plot illegal activities.Therefore, determining how to balance the privacy protection requirements of user communication in the network with the authorized monitoring requirements of law enforcement agencies (LEAs) is a meaningful task.To solve this problem, a new tripartite authenticated key agreement (Tri-AKA) protocol and a session key escrow scheme based on threshold cryptography and the new Tri-AKA protocol were proposed.In the proposed scheme, the LEA participates as a normal user in the key agreement process of two users and uses (t, n) threshold cryptography to share its ephemeral private key with n key escrow agents (KEAs).When necessary, the LEA can combine t KEAs to recover the specified session key and decrypt the communications, thereby preventing malicious administrators in the LEA from arbitrarily monitoring user communications.Finally, we proved the security of the proposed Tri-AKA protocol under the Computational Diffie-Hellman (CDH) assumption with the Random Oracle Model and the security of the proposed key escrow scheme under the Elliptic Curve Discrete Logarithm (ECDL) assumption.Analysis of our session key escrow scheme and comparison with other schemes show that our scheme can avoid the ''once monitor, monitor forever'' scenario and achieve finegrained control in each session.Moreover, our scheme has low storage overhead for each KEA.
Enriching the academic wordlist and Secondary Vocabulary Lists with lexicogrammar: Toward a pattern grammar of academic vocabulary

The interaction between lexis and grammatical patterns has been known in the literature as lexicogrammar, and research in this area has suggested that learning vocabulary necessitates learning accompanying grammatical patterns. Existing academic wordlists, focusing on isolated vocabulary items out of grammatical context, thus could be enriched with their target vocabulary's common grammatical patterns. This paper therefore describes the lexicogrammar of academic vocabulary on the widely used Academic Word List and recently published Secondary Vocabulary Lists. From a corpus of secondary school reading material, it extracts the grammatical patterns frequently associated with academic vocabulary. Drawing on frameworks such as Pattern Grammar and Construction Grammar, and using methods such as colligation and collexeme analysis, this study describes the subset of academic vocabulary associated with particular grammatical patterns. Furthermore, the results of this study are developed into a pedagogical resource that lists the vocabulary and grammar interactions, along with authentic examples of the interaction from the corpus and relevant statistical information. The paper offers specific pedagogical uses of how the resource, named the Lexicogrammar of Academic Vocabulary lists (LAV lists), can be used for developing the breadth and depth of academic vocabulary knowledge in students.
Synthesis of Medical Images Using GANs

The success of artificial intelligence in medicine is based on the need for large amounts of high quality training data. Sharing of medical image data, however, is often restricted by laws such as doctor-patient confidentiality. Although there are publicly available medical datasets, their quality and quantity are often low. Moreover, datasets are often imbalanced and only represent a fraction of the images generated in hospitals or clinics and can thus usually only be used as training data for specific problems. The introduction of generative adversarial networks (GANs) provides a mean to generate artificial images by training two convolutional networks. This paper proposes a method which uses GANs trained on medical images in order to generate a large number of artificial images that could be used to train other artificial intelligence algorithms. This work is a first step towards alleviating data privacy concerns and being able to publicly share data that still contains a substantial amount of the information in the original private data. The method has been evaluated on several public datasets and quantitative and qualitative tests showing promising results.
Fast and Accurate Trajectory Tracking for Unmanned Aerial Vehicles based on Deep Reinforcement Learning

Continuous trajectory control of fixed-wing unmanned aerial vehicles (UAVs) is complicated when considering hidden dynamics. Due to UAV multi degrees of freedom, tracking methodologies based on conventional control theory, such as Proportional-Integral-Derivative (PID) has limitations in response time and adjustment robustness, while a model based approach that calculates the force and torques based on UAV's current status is complicated and rigid. We present an actor-critic reinforcement learning framework that controls UAV trajectory through a set of desired waypoints. A deep neural network is constructed to learn the optimal tracking policy and reinforcement learning is developed to optimize the resulting tracking scheme. The experimental results show that our proposed approach can achieve 58.14% less position error, 21.77% less system power consumption and 9.23% faster attainment than the baseline. The actor network consists of only linear operations, hence Field Programmable Gate Arrays (FPGA) based hardware acceleration can easily be designed for energy efficient real-time control.
A State of Art Approaches on Deep Learning Models in Healthcare: An Application Perspective

Acquisition of knowledge and actionable insights from complex, high-dimensional and nonhomogeneous healthcare data still remains a major difficulty in the evolving health care applications. Different data types have been emerged in the advanced healthcare research area such as maintaining patient's records, imaging, sensors data and content that are not simple, nonhomogeneous, badly annotated and normally not structured well. Conventional data mining and machine learning methods has been executing feature engineering to attain efficient and highly robust features from the data, and then constructs a model to predict or cluster data. Several difficulties exist in the situation of complex information and insufficient domain information. The recent advancements in the Deep Learning (DL) models offer novel and efficient end to end frameworks for health care data. In this study, we attempt to survey the recently presented DL models in the advanced medicinal filed in various aspects.
Implementation of AES Algorithm on Text And Image using MATLAB

One of the major problems faced by the world today is Data Security. In reality communication channel which is used to transfer data from transmitter to receiver is highly insecure. To resolve this problem the data is being manipulated to another form, so that the person with access to the secret key can only read it. This process of manipulation of original data to another form so that eavesdropper cannot access it, is known as encryption. Advanced Encryption Standard (AES) is the most commonly used algorithm for data encryption. This algorithm can be applied on both text and image. In this paper the input to AES algorithm is Text and an image, which results in encrypted output. This encrypted output is given as an input to AES decryption algorithm, which results in decrypted output. The algorithm is implemented using MATLAB software.
An Acoustic Recognition Model for English Speech Based on Improved HMM Algorithm

This paper presents an English speech recognition system based on the improved HMM model. Firstly, this paper integrates the process including speech acoustic analysis, acoustic HMM model establishment and recognition through the analysis of the system framework and implementation of continuous speech recognition system. To enable HMM to assume that the time distribution of continuous segments obeys geometric distribution and its real distribution law is not consistent, we adopt HMM-based segment distribution to describe the time correlation of speech signals more accurately. The experiments show that the algorithm improves the recognition rate and shows better adaptability. Therefore, the recognition ability of acoustic target recognition system can also be effectively improved.