Reviewerly/exHarmony huggingface dataset:
Applying Genetic Programming for the Inverse Lindenmayer Problem

The aim of this work is to find an automated solution for the Inverse Lindenmayer problem - that is to find the describing system for a given end-result of an L-system - using both Bacterial Programming and other related algorithms. To achieve this, several well-known L-systems were considered, their building symbols taken as the inputs for each algorithm, and the evolution results were compared with the formal definition of each system. The results indicate that this is indeed a viable area of research, as both Bacterial Programming and other different algorithms could be fitted to reverse engineer all of the considered systems.
Transparent players: the use of narrative voices in game theory

This paper examines methods for narrating consciousness in game theory. In order to represent how players process their environment, posture towards one another, and hold themselves accountable to their own thinking, I find two distinct ways that game theorists narrate the consciousness of their players. Quoted monologue is a player’s internal language, which can be articulated to show a player’s perspective to the reader. The other narrative mode is psycho-narration, which puts the external technical skills of the game-theorist into the narrator’s hands to describe a player’s mental activity with more complexity than quoted monologue. Quoted monologue and psycho-narration communicate players’ thoughts in ways that convince the readers of the players’ positions, clarify game solution concepts, and enliven the written text.
AtanK-A New SVM Kernel for Classification

Abstract The efficiency of support vector machine in practice is closely related to the optimal selection of kernel functions and their hyper-parameters. A novel kernel, namely the arctangent kernel, is proposed in this paper. Compared with the Gaussian kernel, the new proposed kernel has a quick similarity descent in the neighborhood of the inspection sample and a moderate similarity descent toward the infinity of the inspection sample. The experimental results on two simulated data sets and some UCI data sets show that the new proposed kernel function has better effectiveness and robustness compared with the polynomial kernel, the Gaussian kernel, the exponential radial basis function, and the former proposed kernel with moderate decreasing.
Data-enhanced Kalman filtering of colored process noise

This paper studies the state estimation of nonlinear dynamical systems using stochastically forced linearized dynamics, where the stochastic input models the effect of process noise and the uncertainty caused by excluding nonlinear terms from the linearized model. The statistics of the input stochastic forcing greatly influence the design of estimation gains and can lead to the undesirable performance of state estimators. When the process noise is colored-in-time, conventional methods can fail to provide reasonable estimates of second-order statistics that are of interest in many feedback control applications. To address this problem, we utilize a recently developed framework for the dynamical modeling of input disturbances that provides statistical consistency at the level of second-order statistics with the underlying nonlinear dynamics. We demonstrate the efficacy of linear innovations models that result from this approach for the ensemble Kalman filtering of colored noise processes.
Softsatisficing: Risk-sensitive softmax action selection

Animals, humans, and organizations are known to adjust how (much) they explore complex environments that exceed their information processing capacity, rather than relentlessly search for the optimal action. The adjusted depth of exploration is supposed to depend on the aspiration level internal to the agent. This action selection tendency is known as satisficing. The Risk-sensitive Satisficing (RS) model implements satisficing in the reinforcement learning framework through conversion of action values into gains (or losses) relative to the aspiration level. The risk-sensitive evaluation of action values by RS has been shown to be effective in reinforcement learning. In this paper, first we analyze RS in comparison with UCB and Thompson sampling algorithms. We also show that RS shows differential risk-attitudes considering the risks. Then we propose the Softsatisficing policy that is a stochastic equivalent of RS and further analyze the exploratory behavior of risk-sensitive satisficing that RS and Softsatisficing implement. We emphasize that Softsatisficing has the potential of modeling risk-sensitive foraging and other decision-making behaviors by humans, animals, and organizations.
A Day Ahead Prediction of Solar PV Power Output Using Ensemble Neural Network

Solar PV power prediction plays a major role in the economic operation of power systems. This paper investigates the performance of various neural network frameworks in the short-term prediction of photovoltaic power output. These frameworks are the Feedforward Neural Network (FNN), the Cascade Neural Network (CNN), and the Ensemble Neural Network (ENN). In addition, the neural network models are trained using a variety of training algorithms, namely the Levenberg-Marquardt, Bayesian regularization propagation, and Scaled Conjugate Gradient. Results show that the ensemble neural network framework has a better prediction performance compared to the other methods. Furthermore, the forecasting accuracy of the Ensemble Neural Network is found to be irrelevant to cloud coverage conditions.
A weakly-supervised deep domain adaptation method for multi-modal sensor data

Nearly every real-world deployment of machine learning models suffers from some form of shift in data distributions in relation to the data encountered in production. This aspect is particularly pronounced when dealing with streaming data or in dynamic settings (e.g. changes in data sources, behaviour and the environment). As a result, the performance of the models degrades during deployment. In order to account for these contextual changes, domain adaptation techniques have been designed for scenarios where the aim is to learn a model from a source data distribution, which can perform well on a different, but related target data distribution.In this paper we introduce a variational autoencoder-based multi-modal approach for the task of domain adaptation, that can be trained on a large amount of labelled data from the source domain, coupled with a comparably small amount of labelled data from the target domain. We demonstrate our approach in the context of human activity recognition using various IoT sensing modalities and report superior results when benchmarking against the effective mSDA method for domain adaptation.
Cryptocurrency Analysis using Machine Learning and Deep Learning

Unlike typical banking transactions, blockchain-assisted cryptocurrencies are touted as the currency of the future, allowing peer-to-peer transactions without the need for an intermediary [1]. According to investors, the crypto share market has grown significantly in terms of market capacity, increasing by 300 percent in a year to approximately 1.6 trillion dollars [2]. Crypto investments, on the other hand, are thought to be dangerous given the crypto market's extremely volatile, latent, and non-stationary nature [3]. Stakeholders and investors may be able to easily incorporate crypto into their investment strategy if they can accurately predict the temporal change of the market price over time. In order to anticipate future prices, machine learning (ML) and big data analytics are extremely effective in deciphering stochastic and nonlinear patterns within market data [4].
A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification

Advantages of deep learning over traditional methods have been demonstrated for radio signal classification in the recent years. However, various researchers have discovered that even a small but intentional feature perturbation known as adversarial examples can significantly deteriorate the performance of the deep learning based radio signal classification. Among various kinds of adversarial examples, universal adversarial perturbation has gained considerable attention due to its feature of being data independent, hence as a practical strategy to fool the radio signal classification with a high success rate. Therefore, in this paper, we investigate a defense system called neural rejection system to propose against universal adversarial perturbations, and evaluate its performance by generating white-box universal adversarial perturbations. We show that the proposed neural rejection system is able to defend universal adversarial perturbations with significantly higher accuracy than the undefended deep neural network.
Revisiting the Use of Noise in Evolutionary Robotics

The injection of noise into simulator predictions during controller evolution in Evolutionary Robotics (ER) has long been used as a method of counteracting the reality gap problem. This study endeavoured to design and conduct experiments to quantify the impact of different levels of such noise on two different factors that are believed to contribute to the reality gap: inconsistencies in real-world robotic behaviour and inaccuracies in the simulator used in ER. These experiments were conducted on a robot performing a maze-navigation task. The results obtained in this study showed that, as was anticipated, noise injection during controller evolution does have an appreciable positive impact on the transferability of evolved controllers to the real robot. Various additional trends were observed, however, such as the limited capability of noise to aid in transferability under certain conditions. Additionally, the results of this study illustrated that different contributors to the reality gap may be optimally counteracted using different levels of noise. The results thus emphasized the importance of noise injection and of selecting optimal noise levels during the ER process.
DNN Patching: Progressive Fixing and Augmenting the Functionalities of DNNs for Autonomous Vehicles

Autonomous vehicles have become one of the most-awaited technologies of the century. In the recent years the advances in the area of deep learning and artificial intelligence enabled rapid development of the autonomous driving sector. Some car companies have already deployed autonomous driving software into their vehicles and millions of customers are using them. Although the autonomous driving modules are typically extensively tested prior to its release on the market, the customers can either still encounter certain previously overlooked failure cases or identify missing functionalities of the model. In this letter we focus on developing the methodology for repairing the deep neural network (DNN) steering the self-driving vehicle in response to these two scenarios via patching such that the following conditions are satisfied: i) the patch is added on the top of the original DNN to extend or fix its functionality for the particular failure case, ii) the patched model keeps the original functionalities of the DNN unchanged, and iii) the size of the patch is significantly smaller than the size of the original DNN. Our approach avoids training and evaluating the network from the scratch to improve or fix it, effectively enabling fast response of the autonomous vehicle manufacturers to the customer needs. We demonstrate the plausibility of the proposed approach on the task of autonomous driving in the Carla simulator A. Dosovitskiy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> .
Refining Word Embeddings Using Domain-Specific Knowledge for Drug Reviews Sentiment Classification

With the development of internet technologies, more users are sharing and seeking health-related information in online medical forums about medications and treatments and their contents are usually in subjective nature. Patient-generated drug reviews are valuable and useful textual contents which have not been researched largely by researchers in the natural language processing area. Analyzing drug reviews can assist to improve the pharmacovigilance systems. We propose a refining technique for traditional word embedding using domain-specific knowledge for the drug reviews sentiment classification. The domain-specific lexicon generated from the drug reviews corpus is applied to refine traditional word embeddings in the feature extraction process of sentiment classification. We evaluate our proposed method on the publicly available drug review datasets. According to the experimental results, the proposed method outperformed refined word embeddings using domain-independent lexicon in terms of accuracy in sentiment classification of drug reviews. It indicates the significance of domain knowledge in sentiment analysis of medical domain.
Geophysical Investigation and Geochemical Study of Sediment along the Coastal Area in Kota Belud Sabah, Malaysia

Abstract This article discusses the geophysical investigation of sediment and the status of heavy metals pollution in the coastal sediment from Usukan Beach, Kota Belud Sabah Malaysia. Study area is located at the northwest coast of Sabah which is bounded by the South China Sea and made up of Crocker Formation which age from Late Eocene to Early Miocene, Wariu Formation with Middle Miocene age and Quaternary Alluvium. This study using geoelectrical method namely electrical resistivity (IR) and induced polarization (IP) to measure the thickness of sediment and the parent rock lithology respectively. Whereas the heavy metal contents were measured using the Inductively Coupled Plasma-Optical Emission Spectrometry (ICP- OES). Three sets of subsurface cross-sections were conducted for the geophysical survey and covered the depth around 37 meters for all the survey line. The result of geophysical survey shows that the alluvium quaternary deposits in the low-lying flat area have a thickness up to 13 metres and overlie the mudstone dominated rock of the Crocker Formation. The geochemical analysis shows a decreasing ranking order of elemental concentrations Fe&gt;Mn&gt;Zn&gt;Cu&gt;Pb&gt;Cr&gt;As. The analyses show all elements are within the background values and acceptable standard limits of the Sediment Quality Guidelines (SQG) by USEPA for coastal sediments. The assessment of heavy metals revealed only minimal degree of pollution in the coastal sediments.
A comparative study on the importance of each face part in facial gender recognition via convolutional neural networks

Nowadays, gender recognition systems are very important in several fields such as security, human machine interaction, surveillance and targeted advertising. However, many factors, such as makeup and disguise, can affect recognition and extend the processing time. Our research revolves around this issue. This is a comparative experimental study of the significance of each part of the face (eyes, mouth, nose) in the gender facial recognition via convolutional neural networks (CNN). As a first step our goal is to find the most crucial part of the face in order to determine the most important part in the gender recognition. The used method was tested on the UTKFace dataset and the preliminary results confirm that the eyes contain the most discriminating information regarding gender identification. We achieve a classification accuracy of 92% for eyes, 91% for mouth and 89% for nose. Then we propose a second study on the degree of importance of the eyes for both genders by training the system using only eyes. We achieve a classification accuracy of 99% for eyes of men and 99% for eyes of women.
Representation of Smart Contracts as State Diagrams

Smart contracts have gained popularity with the emergence of blockchain technology, although the concept behind them has been studied since the 1990s. The automation of contracts signed in real life is an interdisciplinary subject and draws attention not only in the scope of technology but also in areas like business area and legal area. With the aim of providing clear understanding, accuracy and security of information in the process of creating a smart contract, many tools have been developed, either to avoid vulnerabilities or to allow anyone to contribute in writing a contract. In view of this scenario and seeking to further facilitate the general understanding of a smart contract, this paper aims to study the representation of smart contracts as state diagrams. It summarizes, through a systematic mapping, the many ways to visually represent smart contracts as state diagrams, emphasizing their states and transitions. An experiment was also carried out in order to show how state diagrams can facilitate the understanding of a contract. The results show that state diagrams do help understanding smart contracts.
Threshold-free Anomaly Detection for Streaming Time Series through Deep Learning

Anomaly detection for streaming time series is a key issue in real applications, especially in the IT industry like ecommerce. Instead of employing the traditional threshold-based approach to achieve anomaly detection, we propose a threshold-free approach through deep learning in this paper. Two parallel pipelines: the intelligent baseline (a neural network assisted with several optimization steps) and the unsupervised detection (a combination of neural network and multiple machine learning algorithms) cooperatively and comprehensively analyze the streaming time series. The intelligent baseline performs well in cases where time series show clear periodic morphology, while the unsupervised detection excels at cases where efficiency is highly required and the periodicity is less clear. With this complementary design of the two parallel modules, the threshold-free anomaly detection can be achieved without the dependence on careful threshold design. Experiments prove that the proposed threshold-free approach obtains accurate predictions and reliable detections.
Robust Particle Filter for Space Navigation Under Epistemic Uncertainty

This chapter presents a new robust particle filter that can handle set-valued probability measures. The method implemented in this new robust filter returns bounds on the probability associated with a given quantity of interest. The particles are generated with an importance sampling technique and propagated only once. The bounds are then computed by an inexpensive tuning of the importance weights. An optimisation procedure based on Branch & Bound which exploits the estimator finite derivative will be presented. The new robust particle filter is applied to the computation of the probability of collision of a piece of debris with an operational satellite in a scenario characterised by epistemic uncertainty on measurements and initial conditions.
COVID-19 Social Safety Nets Sentiment Analysis On Twitter Using Gated Recurrent Unit (GRU) Method

One of the Indonesian government's programs in dealing with Covid19 problems is the Social Safety Net program which is given to the community, especially Covid19 assistance which is given every month to the community. Based on the assistance provided by the government, many people expressed their opinions through Twitter social media. This study aims to analyze the sentiment on Twitter tweets regarding the Social Safety Net Program from March to December 2020. The data collected is 4061 tweets data. The data is classified into two classes, namely positive and negative. The classification algorithm used is Gated Recurrent Unit (GRU). Hyperparameter testing is carried out in order to produce an optimal model. In the optimal GRU hyperparameter, when there are 10 GRU units, the activation function is sigmoid, the optimizer used is Adam, the batch size is 128, with 10 epochs of iteration and 0.2 dropout size. The GRU model produces an f1score of 92.09%, a precision of 90.34%, and a recall of 93.90%.
Development of a new hybrid cloud cover detection method for all sky mapping

Local precise cloud detection techniques in the daytime are finding a wide range of applications in solar farms and for tracking the factory flue gases. Additionally, the cloud coverage is a measure for the site quality of astronomical observatories. The moon is itself a problem for astronomical observations. If there are astronomical objects near to moon, the light scattered by clouds from moon in the sky also has to be determined to extract from the observation schedule of robotic telescope. A novel hybrid method to deal with all-sky images in any conditions in daytime and nighttime is developed, and related algorithms are implemented in MATLAB. A GUI has been presented which interactively analyzes the images taken from all-sky cams. It plots a radar scan of the sky with clouds in polar coordinate systems. The systematic mean error is estimated at less than 3%.
#MeToo as an ‘angry mob’ or in search of meaning? Using language to assess the focus of #MeToo tweets across four events

The Twitter #MeToo movement has been characterized as an angry mob, out for revenge. However, several lines of research warrant a critical evaluation of this characterization: (1) social movements are often delegitimized; (2) anger is an inconsistent response to sexual assault; and (3) preliminary evidence suggests a focus of #MeToo was meaning-making. To evaluate the focus of #MeToo, the language of tweets was assessed across four sequential events, each associated with peak usage of the hashtag. Three mixed measures ANOVAs showed there were more cognitive than emotion words, suggesting a greater thought-than anger-focus. The patterns of first versus third-person pronoun usage differed at the first event compared to the last event, consistent with a change in focus from meaning-making to meaning-made. Power words were accompanied by more first-person singular pronouns than first-person plural pronouns at all events, suggesting tweeters were focused more on personal empowerment, than attaining power over others. We conclude that #MeToo language was not focused on anger and revenge, but on personal empowerment and meaning-making, and that examining the language of social media movements can help to understand whether criticisms of these movements are evidence-based, or backlash.
Prashn: University Voice Assistant

University websites are the best source to get information about a university. But each university has a different methodology for the implementation of its website and there is no common layout for finding specific information. This can be difficult for users, especially potential students who are trying to compare different universities to decide where to get admitted in. Natural Language Human-Computer Interaction (HCI) is trending these days due to its ease of use. Thus, implementing a chatbot on university websites can reduce a lot of time students spend looking for information they need like office timings, address, admission procedure, accommodation information, etc. Voice Assistants are the future of Natural language HCI. Prashn is a web application that allows users to get all the necessary information about Indira Gandhi Delhi Technical University for Women, Delhi either through a chat interface or through speech recognition.