Reviewerly/exHarmony huggingface dataset:
Adapted Graph Reasoning and Filtration for Description-Image Retrieval

Due to the significant cognition reduction, multi-media content has become an increasingly important information type nowadays. More and more descriptions are coupled with images to make them more attractive and persuasive. Currently, several text-image retrieval methods have been developed to improve the efficiency of the time-consuming and professional process. However, in practical retrieval applications, it is the vivid and terse descriptions that are widely used, instead of the shallow captions that describe what is contained. Therefore, the most existing methods designed for the caption-style text can not achieve this purpose. To eliminate the mismatch, we introduce a novel problem about description-image retrieval and propose the specially designed method, named Adapted Graph Reasoning and Filtration (AGRF). In AGRF, we firstly leverage an adapted graph reasoning network to discover the combination of visual objects in the image. Then, a cross-modal gate mechanism is proposed to cast aside those description-independent combinations. Experiment results on the real-world dataset demonstrate the advantages of the AGRF over the state-of-the-art methods.
SVM Approach for Forecasting International Tourism Arrival In East Java

Abstract The maneuver of international tourists visiting Indonesia, especially the East Java area, in positive correlation with time. In accordance with the government’s work program to increase GDP from the tourism sector. However, the condition necessary to be supported by the existence of feasibility and adequate tourism services. Facility improvements certainly require a projection of international tourist arrivals visiting East Java. SVM is a machine learning method that has shown excellent performance in predicting time series. In addition, analysis results have shown that SVM had able to predict foreign tourist visits with a high degree of accuracy. Therefore, this research can be used as a support for tourism facility procurement policies, especially in East Java.
Database Storage Format for High Performance Analytics of Immutable Data

Most of modern database management systems offer a set of data manipulation operations, which strictly limits the available methods of data storage optimization. This article describes a database storage format that provides a low latency access to stored data with highly optimized sequential data extraction process by prohibiting any data modification after initially loading the data. The current study is aimed at developing a database management system that is suitable for high performance analytics of immutable data and performs better than database management systems with wider applicability. This paper includes developed data storage formats, data load and extraction algorithms and performance measurements.
Forecasting of photovoltaic power using probabilistic approach

With the developing establishment in the power network of photovoltaic sources and the increasing demand, the requirement of Photovoltaic plants is essential. But the major drawback in PV system is forecasting of power generation because of intermittent behavior of solar energy. Power produced by PV majorly depends on environmental conditions like temperature, irradiance, and humidity which are highly uncertain in nature. Using probabilistic distribution functions, the probability of solar irradiance and temperature is predicted in three seasons i.e., summer, rainy and winter season. The predicted values of temperature and irradiance are used to forecast PV output power for three seasons i.e., summer, winter and rainy seasons. The forecasted PV output power is useful to meet the load demand with reliability and can be used for extending the capacity of PV plant to meet future load demand.
Machine Learning with IoMT: Opportunities and Research Challenges

In recent days, machine learning (ML)/deep learning (DL) is extensively employed in medical industry to analyse large data for the early prediction of disease. This improves the quality of patient healthcare. ML can be used to solve complex health problems and produces accurate results. Medical industry is one of the biggest industries where ML has made its remarks. Creating precise and multidimensional data set is important and plays a vital role in the performance of ML algorithms. Internet of Medical Things (IoMT) enables the medical equipment and healthcare products to share real-time data to create big volume of data for ML.Interconnection of multiple components from different locations in IoMT introduces security challenges in the network in addition to data sharing. Traditional security measures are ineffective due to the inherent vulnerability of the interconnected devices. Monitoring of interconnected IoMT devices intelligently is the need of the day to have zero attacks. ML/DL methods understand the normal and abnormal behaviour of the IoMT components based on the interaction between them in the connected environment. Hence, intelligent security systems can be developed using ML/DL algorithms.This chapter discusses ML methods used in data analysis and how IoMT helps in creating big data for improving the performance of ML. The second part of this chapter discusses the potential IoMT system attacks and the ML/DL methods for enhancing IoMT security along with the open issues, advantages and disadvantage of all the methods
Simon @ DravidianLangTech-EACL2021: Detecting Offensive Content in Kannada Language

This article introduces the system for the shared task of Offensive Language Identification in Dravidian Languages-EACL 2021. The world’s information technology develops at a high speed. People are used to expressing their views and opinions on social media. This leads to a lot of offensive language on social media. As people become more dependent on social media, the detection of offensive language becomes more and more necessary. This shared task is in three languages: Tamil, Malayalam, and Kannada. Our team takes part in the Kannada language task. To accomplish the task, we use the XLM-Roberta model for pre-training. But the capabilities of the XLM-Roberta model do not satisfy us in terms of statement information collection. So we made some tweaks to the output of this model. In this paper, we describe the models and experiments for accomplishing the task of the Kannada language.
Evolvable Hardware State of the Art

Nature has always inspired traditional solutions to think differently. Modern applications require adaptive and fault-tolerant systems. Bio-inspired computing provides an option. Evolvable hardware (EHW) is a method where hardware is designed to adapt automatically by using optimization algorithms called evolutionary algorithms (EAs). Evolvable hardware has the potential to provide solution for complex real-world applications when compared with growing artificial intelligence (AI). This paper introduces concept of evolutionary algorithms and building blocks of evolvable hardware. Evolvable hardware has many applications, one of them being classifier systems discussed in detail. The concept of evolvable hardware came into world 23 years ago; from then onward, it has given various promising areas, but the challenges posed have stopped evolvable hardware to become competent with traditional solutions. This paper also discusses new advanced platforms and frameworks which opens a new horizon for evolvable hardware.
Teenovate: Using Intergenerational Participatory Design to Teach Students about Adolescent Online Safety

One of the best ways to teach child-computer interaction (CCI) is by doing. We present a new program that we are developing called “Teenovate,” which will be an intergenerational participatory design group comprised of college students (undergraduate and graduate) and adolescents (ages 13-17). Teenovate members will act as both co-designers and co-researchers to generate innovative design-based solutions to salient adolescent online safety challenges and evaluate the effectiveness of these approaches. Teenovate is meant to be a sustainable educational program for Computer Science students taking the college-level introductory course on participatory design methods, as well as the teens enrolled in the program. Thus, it would be an invaluable opportunityfor us to attend an IDC workshop focused on curriculum development for teaching CCI to students.
Simplified Face Quality Assessment (SFQA)

Face quality assessment has grown into a necessary pre-requisite for better performance of the face recognition (FR) algorithm pipeline. In this work, we proposed a novel face quality assessment algorithm for face recognition algorithms. We named it simplified face quality assessment (SFQA) for its simple provision to be used in accompaniment with any FR algorithm. We first proposed a hashing based deep learning model for the prediction of face quality from the features of the corresponding FR algorithm. The last layer of the deep hash net gives binary bits as output which is then converted to the decimal value to get the face quality score (FQS) ranging between 0 and 100. The mutual quality of the probe and gallery images are mathematically clubbed to get the Face Quality Confidence Score (FQCS). We have experimentally shown the effects of FQCS on the recognition algorithm. We trained the prediction model using the FR algorithm for the quality estimation on the face images from CAS-PEAL, LFW, and QDF database. The performance of SFQA is found to outperform the state-of-the-art methods, as discussed in the paper.
Robust subspace clustering based on latent low rank representation with non-negative sparse Laplacian constraints

As a successful improvement on Low Rank Representation (LRR), Latent Low Rank Representation (LatLRR) has been one of the state-of-the-art models for subspace clustering due to the capability of discovering the low dimensional subspace structures of data, especially when the data samples are insufficient and/or extremely corrupted. However, the LatLRR method does not consider the nonlinear geometric structures within data, which leads to the loss of the locality information among data in the learning phase. Moreover, the coefficients of the learnt representation matrix can be negative, which lack the interpretability. To solve the above drawbacks of LatLRR, this paper introduces Laplacian, sparsity and non-negativity to LatLRR model and proposes a novel subspace clustering method, termed latent low rank representation with non-negative, sparse and laplacian constraints (NNSLLatLRR), in which we jointly take into account non-negativity, sparsity and laplacian properties of the learnt representation. As a result, the NNSLLatLRR can not only capture the global low dimensional structure and intrinsic non-linear geometric information of the data, but also enhance the interpretability of the learnt representation. Extensive experiments on two face benchmark datasets and a handwritten digit dataset show that our proposed method outperforms existing state-of-the-art subspace clustering methods.
An Efficient Blind Image Deblurring Using a Smoothing Function

This paper introduces an efficient deblurring image method based on a convolution-based and an iterative concept. Our method does not require specific conditions on images, so it can be widely applied for unspecific generic images. The kernel estimation is firstly performed and then will be used to estimate a latent image in each iteration. The final deblurred image is obtained from the convolution of the blurred image with the final estimated kernel. However, image deblurring is an ill-posed problem due to the nonuniqueness of solutions. Therefore, we propose a smoothing function, unlike previous approaches that applied piecewise functions on estimating a latent image. In our approach, we employ L2-regularization on intensity and gradient prior to converging to a solution of the deblurring problem. Moreover, our work is based on the quadratic splitting method. It guarantees that each subproblem has a closed-form solution. Various experiments on synthesized and real-world images confirm that our approach outperforms several existing methods, especially on the images corrupted by noises. Moreover, our method gives more reasonable and more natural deblurred images than those of other methods.
Reinforcement Learning and Trajectory Planning based on Model Approximation with Neural Networks applied to Transition Problems

In this paper we use a multilayer neural network to approximate the dynamics of nonlinear (mechanical) control systems. Furthermore, these neural network models are combined with offline trajectory planning, to form a model-based reinforcement learning (RL) algorithm, suitable for transition problems of nonlinear dynamical systems. We evaluate the algorithm on the swing-up of the cart-pole benchmark system and observe a significant performance gain in terms of data efficiency compared to a state-of-the-art model-free RL method (Deep Deterministic Policy Gradient (DDPG)). Additionally, we present first experimental results on a cart-triple-pole system test bench. For a simple transition problem, the proposed algorithm shows a good controller performance.
Using the Ship-Gram Model for Japanese Keyword Extraction Based on News Reports

In this paper, we conduct an in-depth study of Japanese keyword extraction from news reports, train external computer document word sets from text preprocessing into word vectors using the Ship-gram model in the deep learning tool Word2Vec, and calculate the cosine distance between word vectors. In this paper, the sliding window in TextRank is designed to connect internal document information to improve the in-text semantic coherence. The main idea is to use not only the statistical and structural features of words but also the semantic features of words extracted through word-embedding techniques, i.e., multifeature fusion, to obtain the importance weights of words themselves and the attraction weights between words and then iteratively calculate the final weight of each word through the graph model algorithm to determine the extracted keywords. To verify the performance of the algorithm, extensive simulation experimental studies were conducted on three different types of datasets. The experimental results show that the proposed keyword extraction algorithm can improve the performance by a maximum of 6.45% and 20.36% compared with the existing word frequency statistics and graph model methods, respectively; MF-Rank can achieve a maximum performance improvement of 1.76% compared with PW-TF.
Attention-based Visual Question Generation

Visual questions are the questions asked with reference to visual content viz. images and videos. Generating visual questions is a challenging task as, it involves asking semantically valid and syntactically correct questions. Most of the methods for visual question generation use the rule-based or template-based approaches. For the same reason they fail to generate novel questions. In this paper, we propose an attention-based mechanism for generating visual questions. The paper compares the results of a simpler encoder-decoder model with the attention model and show the effectiveness of attention for the said task. It highlights the explainable nature of attention mechanism for generating visual questions.
An adaptive approach for word sense disambiguation for Hindi language

Abstract Word sense disambiguation has been a classical problem and it is still considered as a big challenge for NLP researchers. Word vectors have shown remarkable improvements from baseline performances for various NLP challenges. An adaptive approach is developed to resolve the ambiguity at word level using word vectors and presented in this paper. CBOW and Skip-gram models of Word2Vec are used to generate the word embeddings. An input sentence is split into two sets: The first set contains all possible ambiguous words and the second set is constituted from all the unambiguous words. The words from the first set are processed through IndoWordNet to collect all the senses of respective words, and such senses are stored in sensedictionary. An adaptive approach is applied to find out the exact interpretation of the ambiguous word from the sense dictionary for each word from the set of ambiguous words with the context words from the set of unambiguous words. The proposed approach is evaluated on a large scale Hindi corpus and claimed better results than previous attempts.
The evolution of patent system: invention created by artificial intelligence

With development and application of computer science research in depth, the invention created by Artificial Intelligence (AI) could fully achieve the standards of granting patents. The paradigm shift brought about by the scientific revolutions cannot be separated from the constraints of social systematic structure. Even if these inventions created by AI will not have an excessive impact to against our patent system, the general public’s concerns about technology out of control have led to the continuous delay of patentability of the inventions created by AI. From the principle of interest balance to deal with these problems, it is only necessary to coordinate and resolve the legal relationship behind the inventions created by AI, and make appropriate adjustments through patent system. To give AI inventors the personality status through legal fiction will not only not destroy the ethics and rules of human society, but will be more conducive to stimulating the research and development of AI research and further promoting its industrialization.
An Approach to Elicit Trustworthiness Requirements in Blockchain technology

Abstract The leading Blockchain technology facilitates in many sectors including finance and healthcare. However, some technical challenges such as fraud and hacking arise may impact trust of users towards this technology. Requirement elicitation is one of requirement engineering activities to determine system requirements through discussion with stakeholders from documentation and underlying domain before executes it to development stage. The available methods for eliciting requirements, such as interviews and brainstorming are found to be time-consuming, and they fail to identify trustworthiness requirements. Further, eliciting requirements for Blockchain application is found to be challenging, as the properties of Blockchain application are different from the common technology. Moreover, the common elicitation practices are lacking on considering trust elements when eliciting requirements. Therefore, this research aims to propose a new automated elicitation approach which is a library for trust requirements of Blockchain application. A new trust taxonomy will be identified to classify and analyse the association between trust factors and its attributes. A proof concept prototype tool will be developed to realize the approach. Finally, this new approach is expected to enhance the acceptance of people towards application developed using Blockchain technology. Furthermore, this study will provide positive results in producing sustainable Blockchain application or software.
The vulnerability of UAVs: an adversarial machine learning perspective

The study of the vulnerabilities of unmanned aerial vehicles (UAVs) to a wide range of counter-UAV (C-UAV) attacks is well-known and established. While most C-UAV lies in the cyber, sensing, and kinetic domains, there is an emerging threat to these platforms from the perspective of adversarial machine learning (ML). Modern ML approaches are vulnerable to attacks that are largely imperceptible to humans and can be extremely successful in causing undesired false positives and false negatives in real-world scenarios. With the proliferation of ML algorithms throughout the software stack of modern UAVs, these new attacks could have real implications in the security of UAVs. A successful attack on a UAV has real-world consequences such as a collision or takeover of the platform itself. We describe a methodology for understanding the vulnerability of UAVs to these attacks by threat modeling each potential state and mode of the UAV, from powering-on, to various mission modes. In this threat modeling, we consider well-known attacks on deep learning approaches, such as state-of-the-art object detection, but also explore the possibility of novel attacks on traditional computer vision approaches, such as stereo algorithms. We examine one potential threat vector and evaluate the likelihood of success of such an attack given the current progress of adversarial ML.
iHPPVis: Interactive Visual Analysis of Industrial data in Heavy Plate Production

This paper develops an interactive visualization system, called iHPPVis, to analyze and locate the cause of quality-related faults for the heavy plates production. A time distribution of the products under different operating conditions based on Marey's graph is presented and the process data for corresponding conditions to identify clusters and outliers is visualized, which utilizes the alternative dimension reduction algorithms. The crucial stage that leads to the abnormality of product quality is also diagnosed, and the data distribution of heterogeneous process variables in the crucial stage is exhibited. By integrating alternative algorithms with interactive visual analysis to achieve quality-related fault diagnosis, iHPPVis can facilitate the improvement of heavy plates quality. A case study is conducted to demonstrate its effectiveness and exhibit a pilot application of visual analytics for the heavy plates production.
The influence of the amount of catalyst on the degree of sulfonation in the sulfonation process on waste-based Styrofoam (polystyrene) into Poly-(Styrene Sulfonic Acid)

Abstract This study aims to determine the influence of the amount of Ag2SO4 silver sulfate catalyst and amount of time on the degree of sulfonation in waste-based Styrofoam (polystyrene) into Poly-Styrene Sulfonic Acid (PSSA). The method used is sulfonation with sulfuric acid H2SO4 as a sulfonating agent. Based on the research, the sulfonation reaction was successfully carried out, which was shown by the functional group in the FTIR test and the highest degree of sulfonation achieved was 67.6%, which come from variation amount of 2% Ag2SO4 catalyst and 25 minutes of reaction time.
Mobile Robots Path Planning based on $A^{\ast}$ Algorithm Improved with Jump Point Search

There are many proposed algorithms for path planning but the major challenges of them are high consumption of memory and a long execution time. To cope with such challenges, an algorithm based on A* algorithm improved with Jump Point Search (JPS) is proposed and verified in this paper. Firstly, instead of expanding a node into its adjacent nodes the A* algorithm is improved to expand by using the concept of jump points till an obstacle or end of the map is reached. Secondly, few nodes present on the opposite side of the direction of traversal that are leading away from the target are deleted. Finally, an optimal smoother path is obtained by removing redundancy from the path. In this paper the environment for the optimal navigation of the robot is described by using grid methods and a series of simulations performed are presented. Experimental results reveal that the designed algorithm based on the A* algorithm and JPS algorithm decreases the execution time of the path and it is effective compared with traditional A* algorithm.