Reviewerly/exHarmony huggingface dataset:
Implementation of Dubin Curves-Based RRT* Using an Aerial Image for the Determination of Obstacles and Path Planning to Avoid Them During Displacement of the Mobile Robot

The application of mobile robots in autonomous navigation has contributed to the development of exploration tasks for the recognition of unknown environments. There are different methodologies for obstacles avoidance implemented in mobile robots; however, this research introduces a novel approach for a path planning of an unmanned ground vehicle (UGV) using the camera of a drone to get an aerial view that allows to recognize ground features through image processing algorithms for detecting obstacles and target them in a determined environment. After aerial recognition, a global planner with Rapidly-exploring Random Tree Star (RRT*) algorithm is executed, Dubins curves are the method used in this case for nonholonomic robots. The study also focuses on determining the compute time which is affected by a growing number of iterations in the RRT*, the value of step size between the tree’s nodes and finally the impact of a number of obstacles placed in the environment. This project is the initial part of a larger research about a Collaborative Aerial-Ground Robotic System.
Document Classification Using Wikidata Properties

The recent advancements in computer science and technology additionally lead to the generation of a prodigious amount of documents in digital form which are perpetually incrementing. They are widespread in structured as well as semi-structured format. The documents are interrelated and convey the same message but broadcasted from different sources. In this paper, we have endeavoured to enhance the document classification by using classification algorithms integrating with external knowledge base from Wikidata and classify the document of same type in appropriate categories using Wikidata properties as features.
Automatic Curation of Content Tables for Educational Videos

Traditional forms of education are increasingly being replaced by online forms of learning. With many degrees being awarded without the requirement of co-location, it becomes necessary to build tools to enhance online learning interfaces. Online educational videos are often long and do not have enough metadata. Viewers trying to learn about a particular topic have to go through the entire video to find suitable content. We present a novel architecture to curate content tables for educational videos. We harvest text and acoustic properties of the videos to form a hierarchical content table (similar to a table of contents available in a textbook). We allow users to browse the video smartly by skipping to a particular portion rather than going through the entire video. We consider other text-based approaches as our baselines. We find that our approach beats the macro F1-score and micro F1-score of baseline by 39.45% and 35.76% respectively. We present our demo as an independent web page where the user can paste the URL of the video to obtain a generated hierarchical table of contents and navigate to the required content. In the spirit of reproducibility, we make our code public at https://goo.gl/Qzku9d and provide a screen cast to be viewed at https://goo.gl/4HSV1v.
Predicting Cervical Cancer with Metaheuristic Optimizers for Training LSTM

Disease prediction can be extremely helpful in saving people, especially when we are diagnosed with cancer. Cervical cancer, also known as uterine cancer, is the fourth most frequent cancer in women with an estimated 570,000 new cases in 2018 representing 6.6% of all female cancers. In accordance with World Health Organization (WHO), the mortality rate for cervical cancer reaches 90% in the underdeveloped nations and that the high mortality rate found in it could suffer a substantial reduction if there were: prevention, effective screening, treatment programs and early diagnosis. Artificial Neural Networks (ANN) has been helping to provide predictions in healthcare for several decades. Most research works utilize neural classifiers trained with backpropagation (BP) learning algorithm to achieve cancer diagnosis. the traditional BP algorithm has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In this work, we use a type of Recurrent Neural Network (RNN), known as Long Short-Term Memory (LSTM), whose main characteristic is the ability to store information in a series of temporal data. Instead of training the network with the backpropagation, the LSTM network was trained using five different metaheuristic algorithms: Cuckoo Search (CS), Genetic Algorithm (GA), Gravitational Search (GS), Gray Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). From results obtained can be observed that metaheuristic algorithms had performances above 96%.
MPMatch: A Multi-core Parallel Subgraph Matching Algorithm

Subgraph Matching is a fundamental problem in graph analysis, and is widely used in many application scenarios in biology, chemistry and social network. Given a data graph and a query graph, subgraph matching aims to compute all subgraphs of the data graph that are isomorphic to the query graph. The problem is computationally expensive as the core operation it depends on, known as subgraph isomorphism, is NP-complete. In recent years, graph is increasing extensively and it is hard to compute subgraph matching on massive graph data using existing serial algorithm. Meanwhile, there exist distributed solutions, but they are mostly limited to the case where the graphs are unlabelled. In response to this gap, we study the subgraph matching problem in the multi-core environment. From the algorithm level, we propose a multi-core parallel subgraph matching algorithm called MPMatch. From the research level, we explore the concurrent allocation of subgraph matching search space to approach load balancing. We conduct extensive empirical studies on real and synthetic graphs to demonstrate that our techniques improve the performance of serial subgraph matching algorithm via parallelization and well-developed load balancing schema.
Naïve Bayes Classification on Student Placement Data: A Comparative Study of Data Mining Tools

Data mining (DM) is used to analyze and classify data and identify hidden patterns stored in a data warehouse in an attempt to predict future trends, which are quintessential to knowledge discovery and provide tremendous support not only to the world of business but also to that of academia. There are various open-source and freely available software tools such as Weka, R, and Orange as well as programming languages like Python used for DM. This study focuses on comparing the performance of these tools by performing Naïve Bayes classification on student placement data. Percentage of marks scored by students in S.S.C. and H.S.C. examinations and their engineering aggregate were inputs to the tools. Moreover, the tools were trained and tested to decide whether a student would be placed or not. Comparative analyses of the tools were done to determine which tool was able to provide the highest prediction accuracy on student placement data.
Reproducibility and Performance of Deep Learning Applications for Cancer Detection in Pathological Images

Convolutional Neural Networks (CNN) are used for automatic cancer detection in pathological images. These data-driven experiments are difficult to reproduce, because the CNNs may require CUDA-enabled Nvidia GPUs for acceleration and training is often performed on a large dataset stored on a researcher's computer, inaccessible to others. We introduce the RED file format for reproducible experiment description, where executable programs are packaged and referenced as Docker container images. Data inputs and outputs are described as network resources using standard transmission and authentication protocols instead of local file paths. Following the FAIR guiding principles, the RED format is based on and compatible with the established Common Workflow Language specification. RED files are interpreted by the accompanying Curious Containers (CC) software. Arbitrarily large datasets are mounted inside containers via FUSE network filesystems like SSHFS. SSHFS is compared to NFS and a local SSD in artificial benchmarks and in the context of a CNN training scenario, where SSHFS introduces a performance decrease by a factor of 1.8. We are convinced that RED can greatly improve the reproducibility of deep learning workloads and data-driven experiments. This is in particular important in clinical scenarios where the result of an analysis may contribute to a patient's treatment.
Exponentially Adaptive Sine-Cosine Algorithm for Global Optimization

Sine-Cosine algorithm (SCA) is an optimization algorithm formulated based on mathematical Sine and Cosine terms. It is widely used to solve various optimization problems. However the algorithm performance in terms of accuracy is not at optimum level. This paper presents an improved SCA with a new adaptive strategy based on an exponential term. The exponential term is adopted to establish a relationship between searching agents step size and fitness cost. The agents step size is exponentially changed due to the change of the fitness cost. The proposed algorithm is tested with a set of benchmark functions in comparison to the original SCA. A statistical analysis of the algorithms performance in terms of their accuracy is conducted. A Wilcoxon Sign Rank test is adopted to check significance level of the proposed algorithm as compared to the original SCA. Based on the simulation conducted, the adaptive strategy has resulted a significance improvement of the accuracy and convergence speed.
Generation of a Short Narrative Caption for an Image Using the Suggested Hashtag

Existing methods for image captioning aim to generate captions in natural language by using the image attributes. Though these captions are enough to explain what the image is about in most cases, yet sometimes more than a single sentence is desired to describe the context of an image especially when a good caption for the image draws more public attention or 'likes' on a social media post. Though some work has been done to develop models that can generate hashtags for images, but no research work exists that can use those hashtags to create storylike captions. A hashtag can be defined as a word preceded by the symbol '#' and is used to identify an image on social media sites like Instagram. The goal of this application paper is to explore the possibility of generating hashtags for an input image and leverage it to generate meaningful anecdotes connecting to the essence of the image. The experiment conducted, uses an attention-based encoder-decoder framework to produce hashtags for the raw image while a character-level language model, which is trained using a multi-layer RNN, is used to generate stories using one of the suggested hashtags. The model was then tested on HARRISON dataset of Instagram images and the results were qualitatively analyzed through a user study. After analyzing the outcomes of the experiment, it was concluded that this area of research has huge prospects.
Lightweight Information Flow

We develop a type system for identifying the information flow between variables in a program in the Guarded Commands language. First we characterise the types of information flow that may arise between variables in a non-deterministic program: explicit, implicit, bypassing, correlated or sanitised. Next we allow to specify security policies in a number of traditional ways based on mandatory access control: defining a security lattice, working with components or decentralised labels, both as pertains to confidentiality and integrity. Offending information flows are those identified by the type system and that violate the security policy; a program is sufficiently secure if it contains only acceptable information flows.
Kangaroo Mob Heuristic for Optimizing Features Selection in Learning the Daily Living Activities of People with Alzheimer's

Dementia is a disease that affects a large proportion of elders and the number of elders that suffer from this condition is expected to increase dramatically in the next decades. Alzheimer's accounts for approximately two thirds of the people that suffer from dementia and it is a term that describes generically the memory loss. The inability to remember has major consequences upon the daily living activities patterns of the elders that suffer from this condition and consequently the study of the daily living activities performed by the people with Alzheimer's can reduce the health care costs accordingly. The main contributions of this article are: (1) the development of a novel bio-inspired algorithm called Kangaroo Mob Optimization, (2) the development of a machine learning methodology for predicting the daily living activities based on Binary Kangaroo Mob Optimization Features Selection and Random Forest and (3) the application of the proposed methodology on four datasets from literature and on one synthetic dataset in order to demonstrate the improvement of the classification performance.
Leakage-Resilient Hierarchical Identity-Based Encryption with Recipient Anonymity

As a promising public key cryptographic primitive, hierarchical identity-based encryption (HIBE) introduces key delegation mechanisms into identity-based encryption. However, key leakage and recipient anonymity issues have not been adequately addressed in HIBE. Hence, direct applications of traditional HIBE schemes will violate data security and abuse users’ privacy in practice. In this paper, we propose an anonymous unbounded hierarchical identity-based encryption scheme, which achieves bounded leakage resilience and the hierarchy depth is not limited. Our security proofs based on the dual system encryption technique show that the proposed scheme is capable of resisting key leakage and it realizes recipient anonymity in the standard model. In addition, leakage resilience analysis indicates that our scheme allows the leakage rate of approximate 1/3 no matter the hierarchy depth of identities. Finally, performance comparisons show the practicability of our scheme. In particular, the secret key of our construction is of a fixed-length.
Practical Obstacle Avoidance Path Planning for Agriculture UAVs

This research deals with the coverage path problem (CPP) in a given area with several known obstacles for agriculture Unmanned Aerial Vehicles (UAVs). The work takes the geometry characteristics of the field and obstacles into consideration. A practical method of the coverage path planning process is established. An obstacle avoidance path planning is used to find a coverage path for agriculture UAVs. The method has been tested with an Android application and is already applied in reality. The results turn out that the method is complete for this kind of coverage path planning problem.
Estimating the global solar radiation in Putrajaya using the Angstrom-Prescott model

Abstract Global solar radiation (GSR) studies are essential in enhancing the use of solar PV systems especially for Building Integrated PV system (BIPV). Various studies on GSR had been carried out in Malaysia including Penang, Kuala Lumpur and Kota Bharu at the earlier stage. Afterward, data from the Malaysian Meteorological Department (MET) and the Malaysian National University were used to estimate the monthly average daily global radiation for various locations in Malaysia including Kuala Lumpur, Kuala Lumpur, Petaling Jaya, Bandar Baru Bangi, Kota Baru, Senai, Bayan Lepas, Kuching and Kota Kinabalu. GSR in Putrajaya, a location which has identified to implement Malaysian Building Integrated Photovoltaic (MBIPV) projects has yet to be estimated. The conventional method to obtain the amount of GSR at a location is to install a measuring device like a pyranometer at the site to observe and document its day-to-day recordings. However, this is a time-consuming and costly practice. The models used to estimate the GSR are usually based on astronomical factors, geometrical factors, physical factors and meteorological factors. There are numerous models used to predict GSR which can be categorized based on sunshine, cloud, temperature and other meteorological parameters. Additionally, the global solar energy models can be grouped by its modelling techniques such as linear, nonlinear, artificial intelligence and fuzzy logic modelling techniques. The reviews discovered that the models used to estimate GSR were mostly correlated to sunshine duration (SD), air temperature, relative humidity and geographical parameters like latitude, longitude and altitude. In this paper, the GSR in Putrajaya is estimated from the hourly solar radiation and daily sunshine hours data from the Malaysian Meteorological Department. The data, along with the calculated monthly average daily extraterrestrial radiation on a horizontal surface and monthly average maximum possible daily sunshine is used to plot a scatter graph that is linearly fitted. The gradient and vertical axis cut-off from the linear fit is used to obtain the coefficients in the Angstrom-Prescott (A-P) model. Subsequently, the coefficients were used to estimate the GSR in Putrajaya. In line with the government’s initiative, this study will provide an estimation of the GSR in Putrajaya where the finding will be beneficial to the stakeholders, policy maker, developer and investor and energy authority. The average monthly GSR was obtained for nine out of twelve months with highest at 21.18 MJ/m 2 in March and lowest at 18.34 MJ/m 2 in June.
A Comparative Overview of Classification Algorithm for Bangla Handwritten Digit Recognition

Bangla handwritten digit recognition (BHDR) is a well-known problem in the digitization of Bangla language and Bengali script. A lot of work has been done on BHDR and very good accuracy has been achieved. This success can be extended to handwritten Bangla character (vowel, consonant) recognition which will result in automatic understanding of Bangla handwritings. But the main difficulty is faced when it comes to choosing an appropriate classification algorithm to recognize the character of Bengali handwritten script. In this paper, a comparative overview of classification algorithms for BHDR has been provided which will make it easy to decide an appropriate classification algorithm. Here, we have shown a broad comparison of eight (08) different classification algorithms using CMATERdb 3.1.1 Bangla Handwritten Numeral datasets. Different evaluation metrics have been used to justify the comparative analysis. Artificial Neural Network (ANN) performed best whereas Logistic Regression performed well compared to others in terms of the sensitivity, specificity, and error rate. This comparative overview will help scientist especially new researcher to give a quick start with Bangla handwritten character recognition and digitalization.
Al Osool

Za'atari Syrian Refugee Camp in Jordan is home to about 83,000 people fleeing the violence in Syria. Building on a multi-year engagement at Za'atari, we developed an information-enabled community engagement approach to identify and address challenges at the camp, engage community members in problem solving and increase self- reliance. Using HCI mixed methods, we facilitated the creation of a community-commissioned database of household assets, being implemented by the UNHCR with potential for adoption in other refugee camps. Our main contributions are participatory approaches to understanding information behaviors within a fragmented social fabric in high-constraint, low-affordance settings resulting from armed conflict. This work generates insights valuable for researchers and designers working with refugees.
Reasoning with Justifiable Exceptions in $$\mathcal{E}\mathcal{L}_\bot $$ Contextualized Knowledge Repositories

The Contextualized Knowledge Repository (CKR) framework has been proposed as a description logics-based approach for contextualization of knowledge, a well-known area of study in AI. The CKR knowledge bases are structured in two layers: a global context contains context-independent knowledge and contextual structure, while a set of local contexts hold specific knowledge bases. In practical uses of CKR, it is often desirable that global knowledge can be “overridden” at the local level, that is to recognize local pieces of knowledge that do not need to satisfy the general axiom. By targeting this need, in our recent works we presented an extension of CKR with global defeasible axioms, which apply local instances unless an exception for overriding exists; such an exception, however, requires that justification is provable from the knowledge base. In this paper we apply this framework to the basic description logic $$\mathcal{E}\mathcal{L}_\bot $$ . We provide a formalization of $$\mathcal{E}\mathcal{L}_\bot $$ CKRs with global defeasible axioms and study their semantic and computational properties. Moreover, we present a translation of CKRs to datalog programs under the answer set semantics for instance checking.
Spotify Tailoring for B2B Product Development

Agile software development has become increasingly common in the context of large-scale organisations. Typically, software organisations tailor agile methods to fit their needs and ultimately maximise success. The size of the organisation, business goals, and operative models are some examples of factors for which agile methods are tailored. Spotify model is introduced to facilitate the development of a very large-scale project with a Business-to-Consumer (B2C) model, but mission-critical large-scale projects with Business-to-Business (B2B) model are not addressed by the model. Hence, a question that imposes itself is: What are practitioner perceptions of agile tailoring when using the Spotify model? In this paper, we conduct a longitudinal embedded case study to investigate practitioner perceptions of agile method tailoring on a large-scale mission-critical project in B2B environment. The case study lasted over 21 months during which 14 semi-structured interviews were conducted. To analyse the collected data, the Grounded Theory (GT) is adopted. As a result, we identify 44 tailored practices and attributes for B2B product development. Based on this tailoring, 4 influential factors on "Spotify Tailoring" have been derived. These derived factors are worth considering for other organisations concerned with agile method tailoring for large-scale mission-critical projects in B2B context.