Reviewerly/exHarmony huggingface dataset:
Semi-Automated Ontology Building Using Deep Learning to Provide Domain-Specific Knowledge Search in the Marathi Language

In this work, our goal is to build a self-sustainable domain-specific Ontology for the purposes of creating a Knowledge Search Engine. We focused to build it in the Marathi language, which will help school-going children to explore science-related terms. For this, a method is proposed, in which ontology is learned automatically using deep learning model, Bidirectional Long Short-Term Memory (BiLSTM). This paper proposes to use learned ontology to retrieve domain-specific knowledge. The knowledge search engine, which uses constructed ontology to displays search results in Marathi with a very strict limit to the Knowledge complexity of the search results. Unlike, standard search engines, our engine attempts to provide learning resources directly to the user rather than website links. This approach enables the user to directly get information without having to spend time browsing indexed links.
FRVO: A Filter Enhanced Interaction Model for Pedestrian Path Prediction in Crowded Scenarios

The paper presents a filter enhanced interaction model to predict pedestrian trajectory in crowded scenarios, which is essential for robots navigating in pedestrian-rich environments. An adaptive interaction model based on reciprocal velocity obstacle is employed to simulate interaction among pedestrians. It allows an agent to be aware of other agents' aggressiveness and environment crowdedness. Besides, we implement a filter-based online learning framework with adaptive noise covariance to continuously refine the interaction model's inner state, which addresses the uncertainty of pedestrian path planning. We highlight the path optimality and energy efficiency of our method by simulation. Experiments with real-world dataset demonstrate the generality to unknown environments of our method and at least 25% improvement on path prediction error compared with prior models.
Routing of a Multi-Robot System Using a Time-Space Network Model

In this paper, we study the collision-free routing of a multi-robot system to complete given tasks in the shortest time. In a robotic assembly unit, several stations work serially and in parallel. In a station, multiple robots share the same workspace and face the challenge of minimizing the cycle time and avoiding collisions at the same time. For this problem, we propose a new mathematical model that is the so-called time-space network (TSN) model. The TSN model can map the robot location constraints into the routing planning framework, leading to a mixed integer programming problem. By solving this mixed integer programming problem, the collision-free path of multiple robots can be determined. Finally, simulation results illustrate the proposed TSN model can obtain the collision-free route of the multi-robot system.
Self-Organizing Neural Network based Mission Planning for Space Unmanned System

In this paper, the task planning problem of multi-mission location information collection for space unmanned system with long-range detection sensors is considered and modeled as a NP hard combinatorial optimization problem. An algorithm for solving planning problems based on self-organizing neural network is proposed to construct a zero-distance information collection path. On this basis, the effective detection areas of space unmanned system at each mission point are sampled. Four search operators are designed and the appropriate detection points are searched by local search algorithm. In addition, the mission scenario is constructed by random initial multi-mission sites. The simulation and analysis of the algorithm verify the validity of the proposed algorithm for the problem of space unmanned system information collection mission planning.
A Collaborative Deep and Shallow Semisupervised Learning Framework for Mobile App Classification

With the rapid growth of mobile Apps, it is necessary to classify the mobile Apps into predefined categories. However, there are two problems that make this task challenging. First, the name of a mobile App is usually short and ambiguous to reflect its real semantic meaning. Second, it is usually difficult to collect adequate labeled samples to train a good classifier when a customized taxonomy of mobile Apps is required. For the first problem, we leverage Web knowledge to enrich the textual information of mobile Apps. For the second problem, the mostly utilized approach is the semisupervised learning, which exploits unlabeled samples in a cotraining scheme. However, how to enhance the diversity between base learners to maximize the power of the cotraining scheme is still an open problem. Aiming at this problem, we exploit totally different machine learning paradigms (i.e., shallow learning and deep learning) to ensure a greater degree of diversity. To this end, this paper proposes Co-DSL, a collaborative deep and shallow semisupervised learning framework, for mobile App classification using only a few labeled samples and a large number of unlabeled samples. The experiment results demonstrate the effectiveness of Co-DSL, which could achieve over 85% classification accuracy by using only two labeled samples from each mobile App category.
Event Detection and Retrieval in User Generated Videos using Spatio Temporal Fusion Method

Detection and retrieval of user specific interested events in user-generated videos (UGV) is undoubtedly a difficult task due to huge amount of videos on the web and its restricted capturing circumstances. In this article, we proposed effective event detection and retrieval method to identify and retrieve video events just through browsing in the user generated videos by integrating both the spatial and the temporal information. An experimental set up of the proposed method is tested on you tube videos.
Adversarial Capsule Learning for Network Embedding

The purpose of network embedding is to learn a low-dimensional representation for each node in the network. One can then use this low-dimensional representation to solve some network analysis tasks, such as node classification and node clustering. At present, there are several network embedding learning methods based on GAN (Generative Adversarial Networks) to enhance the robustness of representations. However, these methods have two drawbacks. First, they are often too difficult to be trained stably. Second, they only learn the robust representations by matching the posterior distribution of the latent representations to the given priors. On the contrary, Capsule Networks can learn a more equivariant representation of images that is more robust to the changes in pose and spatial relationships of parts of objects in images. However, there is still no research using Capsule Network for network embedding since the social network is essentially different from images. For this problem, we propose a new approach of adversarial capsule learning (ACL) for network embedding, which is the first time to use Capsule Network in the network analysis tasks. To be specific, the new model consists of two parts, a generator and discriminator. We use Graph Convolutional Networks (GCN) as the generator to learn the embedding of nodes, and use Capsule Network as the discriminator to distinguish between the real and fake samples as accurately as possible. The experimental results demonstrate the effectiveness of the proposed new method.
Utilization of Blockchain Technology to Overthrow the Challenges in Healthcare Industry

Information is exposing to cyber threats if a site’s encryption is weak in distributed systems. In this situation, information hacking becomes effortless. To overcome this, information should be packed into blocks. Blockchain technology is such a new enabling technology which links the blocks of information to form a chain with other blocks of similar information. A blockchain is a decentralized computation and information sharing platform which enables multiple authoritative domains, which do not trust each other, to cooperate, coordinate and collaborate in a rational decision-making process. Information on a blockchain existed as a shared and continuously reconciled database. The blockchain database do not store in single location. No centralized version of the information exists which becomes difficult to hack. It allows the digital information to be shared in a distributed environment but not to copy in a single location. It created a new backbone of a new type of Internet. Today, blockchain is finding applications in every field like finance, healthcare, economics and legal, etc. Blockchain provides benefits such as reimbursement of healthcare services, exchange of health data, clinical trials and supply chains. Blockchain has the ability to use the increasing amounts of available health data from traditional sources, electronic health records and other digital health applications as well as non-traditional sources. However, there are certain challenges that are preventing the large-scale deployment of this technology in healthcare. Blockchain has the potential to overcome challenges that exist in the healthcare industry such as interoperability, security, cost of maintenance, integrity, traceability and universal access. In this study, few challenges faced by the healthcare industry are reviewed, and the potentiality of blockchain to overcome the challenges is also studied.
Enhanced E-Voting Protocol Based on Public Key Cryptography

This paper proposes an e-voting protocol to allow voting to be done from any location and still abide with the laws of security including identification, authentication, integrity, and anonymity.The proposed method relies on public key encryption that uses two pairs of certificates one for citizens containing private/public key pairs and the second for National Information Technology Center (NITC) that also contains private/public key pairs.NITC will be acting as a trusted certificate authority by to citizens and government. It is considered an objective and neutral unprejudiced party where no other governmental party has authority upon elections and is auditable by the independent election commission.
Flooding-Driven Modifications of a Hierarchical and Irregular Navigation Grid Structure for Large Virtual Terrains used in Simulation Systems

Simulation systems are important tools for the development of training tasks in different application domains. The use of military simulated problems (MSPs) in simulation systems allows the introduction of challenging decision making situations to military trainees so that they can exercise their skills. To support the implementation of such MSPs in an artillery battery simulator, this work investigates how to compute dynamic changes, such as flooding-driven modifications, into the hierarchical and irregular navigation grid structure expressing the environmental characteristics of large virtual terrain scenarios. The paper describes an efficient mechanism to process these modifications during simulation runtime, without affecting the overall system response time, thus the user interaction with simulations. Experimental results show that the proposed techniques are able to handle time-constrained modifications of large virtual terrain scenarios.
User Review based Rating for E-Commerce Platform

A typical e-commerce platform has too many similar items for sale, making it difficult for customers to choose from. User reviews left by previous buyers are worth reading to help customers make purchase choices. However, due to the large number of reviews, users cannot read all reviews to extract real useful information. In this paper, we propose a user review driven rating system, which is particularly designed for Tmall, a famous Chinese e-commerce platform, to help customers to understand the differences among similar items for finding a satisfactory one. Numerical result demonstrates that, on average, aggregated score calculated by our rating system for items is as efficient as the score given by Tmall, while our rating can differentiate items at much finer granularity by the computed multi-dimension scores.
E-Learning tools: Engaging Our Students?

Since Generation Z students have grown up around WIFI-laptops, video game, etc. they expect technology to be involved in teaching approaches, however students’ perception towards e-learning tools indicate that 80% of students ($\sim$180 students) prefer a face-to-face approach.
Fully Traceable Vertical Data Architecture

Data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and organizations [1]. Organizations often use models to describe this data architecture for a domain. But what type of models are needed? This vertical data architecture approach describes what different models are needed, how these models are interlinked, what the concerns are of certain representation and how an organization can deal with the challenges of keeping these models aligned. This involves the analysis of a Universe of Discourse (UoD), creating conceptual information models (in FBM), transforming these into logical data models and transform these logical models into one or more implementation models. Issues like traceability back to the UoD and impact directly from the UoD to the implementation are main issues which often are hard to tackle.
Integrating the use of iPad in the Teaching-Learning Process: A Pilot Study from UAE

This study aimed at exploring the effect of using iPads on the academic achievement of grade 10 students in ICT class at a Private School in Abu Dhabi, in the United Arab Emirates, and to compare their results with their peers who study the same subject using traditional educational methods. The study was conducted during the second semester of the school year 2017/2018 and the sample consisted of two groups: the control group (42 students) who were taught using the traditional method, and the experimental group (42 students) who were taught using the iPad. An achievement pre and post-tests were given to the two groups, data were statically analyzed and discussed. The study results have shown statistical difference in the achievement of the experimental group indicating that using the iPad in learning has a positive effect on the academic achievement of grade 10 students compared to those who learned by the traditional method.
Ambient Assisted Living At-Home Laboratory for Motor Status Diagnostics in Parkinson's Disease Patients and Aged People

The chapter summarizes the authors' development on the concept of “at-home lab” (AHL). The concept employs the methods of artificial intelligence (AI), smart internet of things (IoT) technologies, and data mining techniques. The aim is at support for patients with Parkinson's disease and aged people to continuously monitor and evaluate their motor and cognitive status using own smartphone (in particular, IMU as wearable sensor, apps for testing cognitive status, camera for motor tracking). In addition, other devices in the IoT environment can participate in creating the information assistance support for people. This chapter presents and discuss the AHL concept as a further development step of AI in respect with human evolution (NeoNeoCortex). The focus is on evolutionary, environmental, and biological aspects of AI.
A High Visibility and SNR Image From One Single-Shot Low-Light Image

Achieving high visibility and high signal-to-noise ratio (SNR) from a single-shot image captured in low-light environments is an under-constrained problem. To cope with this issue, the intrinsic relationship between the image domain and the radiance domain is first established based on the human visual model, the atmospheric scattering model, and the camera imaging model, and the ideal exposure is derived. Using the illumination-reflection-noise prior, a new convex optimization by employed gradient constraint and the Krisch operator is then presented to estimate the noise-reduced illumination and reflection components. A high SNR image in the optimal exposure is generated in radiance domain, which is finally inversely mapped to obtain a high SNR image in image domain. Experimental results in subjective and objective tests show that the proposed algorithm has a high SNR and pleasant perception in comparison with the state-of-the-art methods.
Joint Estimation and Detection for MIMO-STBC System Based on Deep Neural Network

Motivated by the recent advances in deep neural network (DNN), we propose a general DNN scheme to joint channel estimation and signal detection for multi-input multioutput (MIMO) system with space-time block coding (STBC). Different from the maximum likelihood (ML) detection with imperfect channel state information (CSI) which consists of channel estimator, combiner and detector, the proposed DNN scheme is designed to replace the three modules jointly, estimate CSI implicitly and recover the transmitted symbols directly. Specifically, a four-layer DNN is constructed for MIMO-Alamouti system and is simulated under the condition of quasi-static rayleigh channel and QPSK modulation. The simulation results demonstrate that, at the same bit error ratio (BER), the signalto-noise ratio (SNR) of the DNN-based detection has a loss of 1dB compared with ML detection with perfect CSI, and has a gain of 3dB compared with ML detection with imperfect CSI in MIMO-Alamouti system.
Analysis of Dataset in Private Cloud for Cloud Forensics Using Eucalyptus and Hadoop

At present, in most of the areas of research, development and daily usage, almost all platforms and service providers are dependent on cloud computing. To achieve scalability and sustainability, cloud architecture is considered as a result of technological amalgamation. If the design of the architecture is complex in nature, it could have adverse effects on data recoverability and analysis when the system gets compromised. This type of structure introduces issues like inherent architecture flaws, backdoors, code smelling which could lead to exploitable vulnerabilities for hackers and scammers. Likewise, its complex nature puts limits on forensic investigation methods. For such problems, digital forensic provides a solution. It uses the three-stage approach which consists of evidence collection eye-witnessed by the user, evidence preservation in an unaltered way, offline safeguarding of evidence for feigning its collection. Among its various methods like live forensics, timeline analysis, logging, sandboxing, logging is generalized and easy to use method. The proposed work is about digital forensic technique, log analysis, which is the most effective approach to override investigation issues in the cloud environment. Snort, Network Intrusion Detection System (NIDS), will work as a daemon on Eucalyptus private cloud to monitor and log intrusive attempts of network activities on it. Second, the proposed approach will fix the incapability of Eucalyptus to export logs to the rSyslog server. Third, the generated datasets, irrespective of location and format, would be analyzed by Hadoop, for improved analysis of a system.
Performance Analysis of Neighborhood Component Feature Selection for Oral Histopathology Images

Precise feature selection leads to improved accuracy in automatic oral cancer detection using digital Hematoxylin and Eosin (H&E)-stained histopathology images of oral squamous cell carcinoma. In this work, we have used neighborhood component analysis (NCA) feature selection technique with stochastic gradient descent based feature weight estimator. To verify the efficiency of the feature selection technique and independency of choice of classifier, three well known classifiers are used. The histopathology images of oral mucosa are classified using selected features. It has been observed that misclassification rate is reduced considerably after feature selection is employed.
Efficient Computation of BSD Invariants in Genus 2

Recently, all Birch and Swinnerton-Dyer invariants, except for the order of , have been computed for all curves of genus 2 contained in the L-functions and Modular Forms Database [LMFDB]. This report explains the improvements made to the implementation of the algorithm described in [vBom19] that were needed to do the computation of the Tamagawa numbers and the real period in reasonable time. We also explain some of the more technical details of the algorithm, and give a brief overview of the methods used to compute the special value of the L-function and the regulator.
Web Search Using Automatically Generated Facets

World Wide Web is the vital source for millions of users. The primary goal of the WWW is to provide the valid, right and the most relevant information for the end users who is searching for the information. Navigating through a number of pages for the right information is a difficult task. A novel method to ameliorate end user search experience by combining sub-queries and facets is proposed. Facets are group of words or phrase which summarize and explain the various aspects of a query. The significant information about a query usually represented in retrieved documents as list so query facets can be generated by combining these important lists. Removal of useless items from list can lead to the formation of good quality facet.
Sharing Blockchain Performance Knowledge for Edge Service Development

The integration of Internet of Things (IoT) and cloud services with edge technologies has enabled the development of many new types of edge services, which leverage blockchain features for cross-organizational, traceable and verifiable records. However, developing such edge services with blockchain features requires not only knowledge about complex blockchain technologies but also how blockchain technologies coexist with edge computing service models and architectures and deployments. In the context of edge service development, coupling edge systems, software models for edge services and blockchain technologies is complex. Thus, a strong collaboration and knowledge sharing for edge systems and blockchain technologies will help addressing many concerns of the developer. However, there is a lack of frameworks for sharing knowledge about blockchain software artefacts and deployments for edge services. In this paper, we present various types of information linking blockchain performance with service deployments at different levels. We represent and associate benchmarked performance information of blockchain operation and blockchain infrastructural services with common edge service interactions and resource deployments. Based on that, we develop a service offering blockchain knowledge to the developer seeking relevant blockchain operation information for their development decisions. We will present a prototype of our framework with benchmarked information obtained from experiments with Ethereum and Hyperledger.
Recommending Movies on User's Current Preferences via Deep Neural Network

Traditional recommendation system (RS) offers remarkable results in recommending movies. RS ignore the idea that preferences of a user changes with respect to time. The user cold case scenario is a problem in which user does not have a profile in the system. To address the cold case scenarios, we proposed, developed, and evaluated the recommendation engine based on user current preferences with the use of deep neural networks. The movies are fed to BGRU on the fly and a recommendation of the list of movies was made for the user to watch next. Our model recommends the movies based on his/her current preferences. The experiments were carried out on Movie Lens Dataset. The model was evaluated and have shown significant improvements in results in comparison to conventional RS. The results are presented based on Recall@K metrics resulting in accurate and personalized movies recommendation to user.