Reviewerly/exHarmony huggingface dataset:
Honey. I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL

Actors and critics in actor-critic reinforcement learning algorithms are functionally separate, yet they often use the same network architectures. This case study explores the performance impact of network sizes when considering actor and critic architectures independently. By relaxing the assumption of architectural symmetry, it is often possible for smaller actors to achieve comparable policy performance to their symmetric counterparts. Our experiments show up to 99% reduction in the number of network weights with an average reduction of 77 % over multiple actor-critic algorithms on 9 independent tasks. Given that reducing actor complexity results in a direct reduction of run-time inference cost, we believe configurations of actors and critics are aspects of actor-critic design that deserve to be considered independently, particularly in resource-constrained applications or when deploying multiple actors simultaneously.
Decentralized Logging Service using IPFS for Cloud Infrastructure

In any shared space of resources it is very important to have a trustable way to record how and when the resources have been used and by whom. Similarly, in any cloud-based platform, collection of logs is an important activity required to have a trustable record of the activities performed by the users and pin point any malicious deeds performed. Forensic investigations however face a grave challenge of ensuring the integrity of the logs. The activity of collecting logs and ensuring their integrity becomes a necessity with regulations like SOX enforcing it on financial institutes. In this paper, IPFS has been employed to create a system which preserves all the meta-data of logs generated by the network activity of the virtual machine and guarantees the confidentiality, integrity and availability during any forensic investigation. The integrity of the logs is guaranteed by the IPFS system which creates a content-based hash for the logs and stores them securely. As files in IPFS are indexed by their hash, tampering with a log will result in creation of a new hash which won’t exist in the index. Index will still point to the original hash, hence integrity is achieved. In previous research, the systems could guarantee whether a log has been tampered with or not, but none provided a mechanism to recover meta- data of tampered logs to their original state. Using IPFS, this paper aims to make the system more secure and takes it a step forward by providing the meta- data of the original logs for the tampered logs.
Advanced Web Pattern Categorization and Web Content Personalization Techniques

Generic Web accessing mechanism is the web content presentation wherein a single simpler aspect of web interface is being shared by a number of web users, in a runtime scenario, we might find a diverse category of users who want to use different content in a different manner. There must be a metric to individualize web experience so as to create a complete segment of web personalization. The pattern analysis deals with the study of analysing web usage pattern from a number of factors and creating a prediction model which will describe what next a web user will use as part of web surfing. This will eliminate the removal of those contents, which are of no use to a particular web user apparently creating a separate web dashboard depending on the past data and simplifying the overall process of web content personalization using popular data mining algorithms and techniques in a novel manner.
FUZZY TYPE THEORY IN THE ANALYSIS OF ARGUMENTATION

The article deals with a fazzy variant of P. Martin-Löf ’s intuitionistic type theory. It presents the overview of fuzzy type theory rules and an example of its application to the analysis of the persuasiveness of argumentation. In the latter, the truth values of fuzzy logic are interpreted as degrees of persuasiveness of statements and arguments. The formalization is implemented in the proof assistant Agda.
Ontology based knowledge representation: Case study from agriculture domain

The growth of web has touched everyone’s life from an economist, entrepreneur, and academician to a farmer. The agriculture sector is quite important for any country’s growth. Farmers can also be benefited if they are provided with relevant information they need. The reason that it is not happening on a large scale is due to many reasons. Information is available in different formats, platforms and it is highly unstructured. The availability and real time usage of this information is prohibited mainly by the way it is represented. Recently, Ontology has emerged as one of very expressive knowledge representation scheme which enables gathering information from heterogeneous sources and creates a common data model that is shared and agreed upon in diverse domains. The interoperability and deduction capabilities that Ontology offers are very useful for generating new knowledge. Most of the work pertaining to ontology development in agriculture domain is crop specific, where information about fertilizers is confined to a particular crop only. In this paper, we have designed and developed a generic ontology for fertilizers. Fertilizer itself is a concept which should be modeled independently. Even for crop specific ontology, fertilizer is one of the most important concept to be captured. So, it needs to be represented independently. Finding this research gap, an ontology in fertilizer subdomain is developed, taking into account the various issues that are faced while constructing an ontology and the enormous amount of data that is available but is not easy to structure and present in the form of ontology. We have also validated the Ontology using an available tool and domain experts have also validated the developed Ontology.
Comparison of the Effects of Feature Selection and Tree-Based Ensemble Machine Learning for Sentiment Analysis on Indonesian YouTube Comments

The main problems in sentiment analysis models on Indonesian YouTube comments are unstructured data and low classification accuracy. Sentiment analysis for Indonesian, which is different from English, requires proper preprocessing and classification methods. Previous research usually using Linear Support Vector Machine (SVM), Naïve Bayes and Decision Tree. Although the accuracy of SVM is better than other algorithms, it still needs to be improved. This study aims to compare the performance of the tree-based ensemble method and feature selection to improve the sentiment analysis model for Indonesian YouTube comments. This research crawled Indonesian YouTube comments from different domains and produce ten datasets. The preprocessing's method in this research was removed stopword, convert slang words, and stemming. For feature selection, we tested two vectorizer method, i.e. Term Frequency (TF) or Term Frequency/Inverse Document Frequency (TF-IDF). The model build using six machine learning, consist of four tree-based ensemble machine learning to raise better accuracy, Linear SVM and Decision Tree. We use tree-based ensemble machine learning, they are Random Forest, and Extra Tree represents bagging ensemble. AdaBoost and Gradient Boosting represent boosting ensemble. SVM and Decision tree as a comparison. Based on experiments by combining feature selection and ensemble machine learning, it can be concluded that the type of vectorizer has little effect on classification accuracy. In all experiments, the best machine learning methods are Extra Tree with an accuracy of 93.39% and AdaBoost with an accuracy of 92.53%. Whereas, the use of TF or TF-IDF does not significantly affect accuracy.
A Hybrid Model for Recommendation Systems

The purpose of recommendation systems is to recommend users to the items which they are interested in. Recent studies have mentioned the individual models of recommendation systems, and each of such models uses different measures to predict users’ level of interest in items. In order to improve the efficiency of recommendation systems, we have built a hybrid model to combine such different measures. Then, we experimented the proposed model on the ML-20M-GroupLens dataset, and the obtained results have shown that our hybrid model based on weighted ratings improves up to 8.02 % in precision compared to the user-based collaborative filtering model (the individual model that gave the highest precision results on our experiments).
LOPE: A Low‐Overhead Payment Verification Method for Blockchains

With its characteristics of decentralization, security, data traceability, and tamper-resistance, the blockchain has been widely used in various domains. Considering the difference in the performance of the devices, the light client is proposed so that devices without the ability to store a full blockchain copy can also participate in the blockchain transactions. However, the light client has to communicate with full nodes and verify the authenticity of a transaction which brings in some extent of communication, computation, and storage overheads to the light client. These overheads cannot be ignored for some low-performance devices, such as embedded devices or IoT chips, and therefore the current light client scheme does not work in this situation. We propose LOPE (a Low-overhead payment vErification method) for poor-capacity nodes in the blockchain system. In LOPE, a grouping protocol is designed to partition full nodes into groups to serve the verification requests of the light client. In addition, Practical byzantine fault tolerance (PBFT) is used to ensure the light client to get a credible result in spite of a few dishonest nodes existing in the group. We conduct LOPE and evaluate it in a testbed. The experiment results show that LOPE reduces more than half of the communication overhead, degrades the computation overhead of the light client to a large extent, and avoids the storage overhead of the hash roots of block headers in the light client. We also conduct theoretical analysis to show the performance improvement and security issues of LOPE.
Security Analysis of A Stream Cipher with Proven Properties

Si and Ding proposed a stream cipher with two keys (the first and the second key) and an expected security strength. To further measure the security, we analyze the stream cipher by considering the selective discrete Fourier spectra attack and the fast selective discrete Fourier spectra attack. The two attacks reveal a fact that the second key is more important than the first key, that is, if the second key is leaked out, the first key can be obtained with a lower time complexity than that of the expected security. In addition, we analyze the ability of the stream cipher to resist the guess-and-determine attack. The results show an attacker is able to gain the two keys with an exponentially improved time complexity and a polynomial data complexity. It implies that we need a securer permutation over finite fields to design a new binary additive stream cipher to achieve the expected security level.
The Multi-Tuned Piano: Keyboard Music without a Tuning System

Abstract Unlike a physical grand piano, a physical synthesis instrument, such as one of Pianoteq’s series of grand pianos, is not necessarily constrained at any moment to a single tuning system. This article discusses why a system using discrete piano pitches (not sliding pitches) chosen freely among the audible pitch continuum presents interesting musical and expressive possibilities. Audio and video of compositions and an improvisation exploiting the system demonstrate its potential and a performing interface for it.
Mechanism for Multithreaded and Asynchronous System Call Execution

Web servers provide us knowledge on many aspects. To provide contents, web servers have to read many files from I/O devices. To access devices or use kernel resources, executing processes must invoke system calls. To invoke a system call of the operating system kernel, transition of the thread switches between user and kernel modes are necessary, which causes overhead time, and other execution processes to be blocked until the system call is completed. In this paper, we propose a mechanism called Asyncsyscall that can execute asynchronously using a request and an executing thread. Asyncsyscall enables system calls to execute with less overhead and less blocking of other executing processes.
Over-sketching Operation to Realize Geometrical and Topological Editing across Multiple Objects in Sketch-based CAD Interface

We developed a new general-purpose sketch-based interface for use in two-dimensional computer-aided design (CAD) systems. In this interface, a sketch-based editing operation is used to modify the geometry and topology of multiple geometric objects via over-sketching. The interface was developed by inheriting a fuzzy logic-based strategy of the existing sketch-based interface SKIT (SKetch Input Tracer). Using this interface, a user can make drawings in a creative manner; e.g., they can start with a rough sketch and progressively achieve a detailed design while repeating the over-sketches.
A pseudorandom bit generator based on arctangent function

Abstract In this paper, in addition to translation &amp; scale (already adopted before), we employ another elementary transformation, i.e. fold, to acquire a variant of Arctangent Function. Both Bifurcation Diagram &amp; Lyapunov Exponent Spectrum indicate that the new mapping owns excellent chaotic properties. Afterwards, a pseudorandom bit generator is devised based on it. Pseudorandom tests illustrate that the mapping is the 3rd best one so far. The results show great application prospect.
Recaptured Image Forensics Based on Image Illumination and Texture Features

Advanced devices like digital cameras and smart devices have triggered the greater possibility of recapturing high-quality images from output media. These high-quality recaptured images are genuine enough to escape the detection of normal human eyes, and the existing image forensic techniques. Giving the malicious users opportunities to distribute these fraudulent images to execute illegal activities. Due to the difference in illumination environments during original scene capture and recapturing processes, the recaptured image exhibits different illumination feature patterns compared with the original scene images. Furthermore, the fact that during every image capturing event the camera compresses the images, implies that a recaptured image suffers double compression which significantly alters its texture features. Various studies have showed illumination and texture features to be key features for generating statistical patterns that can give pertinent information for recaptured image detection. This study proposes an efficient method that combines the illumination and texture features for enhancing the recaptured image detection. The proposed approach begins with dividing the image into 16x16 blocks, extracts texture and illumination features from each image block and finally aggregates the extracted features for classification. Evaluated on a publicly available image dataset with high quality recaptured images, the proposed approach recorded good detection results with a detection accuracy of up to 94.4%, a recall of 95.6% and a false detection rate of 6.7%.
Extreme Learning Machine Based on Calculating the Output Weight of Partial Robust M-regression

Abstract In order to improve the nonlinear mapping capability and learning performance of extreme learning machine (ELM), a new learning algorithm called extreme learning machine based on calculating the output weight of partial robust M-regression is proposed. This algorithm introduces the partial robust M-regression into the extreme learning machine algorithm. Firstly, the hidden layer output matrix H is calculated by extreme learning machine. Secondly, the matrix H and vector Y are weighted by weighted strategy. Then, PRM algorithm is used to establish the regression model between the weighted matrix Hw and vector Yw , and calculate its regression coefficient, namely output weight of the ELM Algorithm. The proposed method predicts the Mackay’s robot arm regression and sediment concentration of the Yellow River Basin to verify the effectiveness of the method. The simulation results show that the proposed PRMELM algorithm is superior to the original extreme learning machine algorithm in prediction accuracy and generalization performance.
QuARk: A GUI for Quality-Aware Ranking of Arguments

With the Web augmenting every day and computers increasingly getting more powerful, research in the field of computational argumentation becomes more and more important. One of its research branches is argument retrieval, which aims at finding and presenting users the best arguments for their queries. Several systems already exist for this purpose, all having the same goal but reaching it in different ways. In line with existing work, an argument consists of a claim supported or attacked by a premise. Now that argument retrieval has become a separate task in the CLEF lab Touché, displaying the ranking is becoming increasingly important.
FindMoviez: A Movie Recommendation System

Movie recommendation has become one of the most efficient ways of making the user experience more personalized and connecting the user with the movies that the user might like. In this paper, FindMoviez, a movie recommendation engine has been introduced, which is based on a combination of two recommendation algorithms, implemented in a web application. The three sub-data sets (i.e.m ratings, users, and metadata) of the famous Movielens data set have been used. A combination of item–item collaborative filtering and genre based using the average weighted rating method has been used. These algorithms have been modified in a way where the user is always recommended movies, and even if one of the above algorithms fails, the other comes into play making this product more reliable for the user. Thus, the user can completely relies on this product for genuine movie recommendations.
Logistic regression model with TreeNet and association rules analysis: applications with medical datasets

This study establishes an innovative and effective approach for generating new variables and interactions for logistic regression using the two data mining techniques TreeNet and association rules analysis. With TreeNet as the first step in our logistic model building, the new variables are generated by discretizing the quantitative variables. With ASA as the following step, the new interactions are generated from all the original categorical variables and all the newly generated predictors from TreeNet. These newly generated variables and interactions (low- and high-order) are used as candidate predictors to build an optimal logistic regression model. The method is tested on and the results given for four medical datasets—heart disease, heart failure, breast cancer, and hepatitis—with the complete model process presented for the last of these. The results indicate that building a model in this way constitutes a major advance in logistic regression modeling that cannot be achieved using other existing methods.
Classification of Low-Resolution Satellite Images Using Fractal Augmented Descriptors

Satellite imagery consists of highly complex spatial features that make it difficult for traditional image processing techniques to use them for classification tasks. In this paper, we propose a novel method to use these hidden fractal information that naturally exist in these satellite images. We have designed a fractal-based descriptor which generates a scale invariant fractal image for easier fractal-based pattern extraction and uses it as an added feature vector that is combined with the original image and fed into a VGG-16 deep learning architecture which successfully classifies even low-resolution satellite images with an f1-score of 0.78.
Penalized -regression-based bicluster localization

Biclustering (co-clustering, two-mode clustering), as one of the classical unsupervised learning methods, has been applied in many different fields in recent years. Different types of biclustering methods have been developed such as probabilistic methods, two-way clustering methods, variance minimization methods, and so on. However, few regression-based methods have been proposed to the best of our knowledge. Such methods have been applied in traditional clustering, which can improve both the computational efficiency and the clustering accuracy. In this paper, we present a penalized regression-based method for localizing the biclusters (PRbiclust). By imposing Truncated LASSO Penalty (TLP) and group TLP terms to penalize the column vectors and the row vectors in the regression model, the structure of biclusters in the data matrix is recovered. The model is formulated as an optimization problem with nonconvex penalties, and a computationally efficient algorithm is proposed to solve it. Convergence of the algorithm is proved. To extract the biclusters from the recovered data matrix, we propose a graph-based localization method. An evaluation criterion is also proposed to measure the efficiency of bicluster localization when noise entries exist. We apply the proposed method to both simulated datasets with different setups and a real dataset. Experiments show that this method can well capture the bicluster structure, and performs better than the existing works.
Exploring Natural Language Processing Techniques in Social Media Analysis during a Pandemic

People around the world have used social media extensively to communicate and express opinions especially during this time of the rapid spread of COVID-19. Nowadays, the various narratives of social media users are important that can be used in creating measures to curb the deadly disease. However, the manual collection of data from social media such as Facebook and its analysis can take time. Thus, this study attempted to use natural language processing (NLP) techniques such as topic modeling and word embedding to identify the concepts contained in the posts and comments of Facebook users in the Philippines regarding the pandemic. This study harvested posts and comments in Facebook groups that are primarily Filipino citizens that express opinions and suggestions in COVID-19 responses. Using Latent Dirichlet Allocation (LDA), this study was able to generate 10 topics related to the concepts of (1) self-discipline, (2) prayers for the frontliners, (3) total lockdown, (4) following government guidelines and protocols, and (5) flattening the curve of the disease. Meanwhile, word groups generated by Word2vec developed concepts such as (1) mass testing, (2) hope for faster recovery, and (3) expectation from the government. The average cosine similarity for word groups is 0.92, which implies strong relatedness of each word per group. This study proved that the use of NLP techniques helped in analyzing the themes of Facebook posts and comments related to the pandemic.
Need and Relevance of Common Vocabularies and Ontologies in IoT Domain

Data plays a foremost part in Future Communication. Internet of Things encompasses various sources of data of different type, by different name. The complexity and variance in data increase the criticalness of the model. Same data has different meaning in different applications. These variances make it difficult to apply any query on data. To resolve the issues in naming of data values and to create consistency Vocabulary and Ontologies are must. Data gathered on a big platform e.g. for IoT via cloud need to manage data in proper manner, which is spread all over the internet on different servers. The features and related research is explained in this chapter, having a brief introduction with application areas.