Reviewerly/exHarmony huggingface dataset:
Algorithm Design with the Selection Monad

The selection monad has proven useful for modelling exhaustive search algorithms. It is well studied in the area of game theory as an elegant way of expressing algorithms that calculate optimal plays for sequential games with perfect information; composition of moves is modeled as a 'product' of selection functions. This paper aims to expand the application of the selection monad to other classes of algorithms. The structure used to describe exhaustive search problems can easily be applied to greedy algorithms; with some changes to the product function, the behaviour of the selection monad can be changed from an exhaustive search behaviour to a greedy one. This enables an algorithm design framework in which the behaviour of the algorithm can be exchanged modularly by using different product functions.
Sound and Complete Type Inference for Closed Effect Rows

Koka is a functional programming language that has algebraic effect handlers and a row-based effect system. The row-based effect system infers types by naively applying the Hindley-Milner type inference. However, it infers effect-polymorphic types for many functions, which are hard to read by the programmers and have a negative runtime performance impact to the evidence-passing translation. In order to improve readability and runtime efficiency, we aim to infer closed effect rows when possible, and open those closed effect rows automatically at instantiation to avoid loss of typability. This paper gives a type inference algorithm with the $${\mathsf {{open}}}$$ and $${\mathsf {{close}}}$$ mechanisms. In this paper, we define a type inference algorithm with the open and close constructs.
Towards Efficient Adjustment of Effect Rows

Koka is a functional programming language with native support for algebraic effects and handlers. To implement effect handler operations efficiently, Koka employs a semantics where the handlers in scope are passed down to each function as an evidence vector. At runtime, these evidence vectors are adjusted using the open constructs to match the evidence for a particular function. All these adjustments can cause significant runtime overhead. In this paper, we present a novel transformation on the Koka core calculus that we call open floating. This transformation aims to float up open constructs and combine them in order to minimize the adjustments needed at runtime. Open floating improves performance by 2.5 $$\times $$ in an experiment. Furthermore, we formalize an aspect of row-based effect typing, including the closed prefix relation on effect rows, which clarifies the constraint on open floating.
Analysis of Approximate Sorting in I/O Model

We consider the problem of approximate sorting in I/O model. The goal of approximate sorting in I/O model is to find out a permutation that is as close as possible to the true ordering of elements in t I/O operations. However, the quality of approximate sorting in I/O model can not be well measured by the existing metrics on permutation space. Thus, we propose a new kind of metric named External metric, which ignores the errors and dislocation that happened in each I/O block. We consider the External Spearman's footrule metric (short for ESP) (Spearman's footrule metric in RAM model) and a new metric external errors (short for EE) (errors in RAM model). ESP shows the block dislocation distance of each element and EE states the number of the dislocation elements in the approximate result. According to the rate-distortion relationship endowed by these two metrics, we find the lower bound of these two metrics of the permutation generated by any external approximate sorting algorithm with t I/O operations. Finally, we propose a k-pass external approximate sorting algorithm that is asymptotically optimal in I/O model.
Space Limited Graph Algorithms on Big Data

We study algorithms for graph problems in which the graphs are of extremely large size N so that super-linear time $$\omega (N)$$ or linear space $$\varTheta (N)$$ would become impractical. We use a parameter k to characterize the computational power of a normal computer that can provide additional time and space bounded by polynomials of k. In particular, we are interested in strict linear-time algorithms using space $$O(k^{O(1)})$$ . In our case studies, as examples, we present a randomized algorithm of time O(N) and space $$O(k^2)$$ that constructs a maximal matching of size upper bounded by k in a graph of size N, and a randomized kernelization algorithm of time O(N) and space $$O(k^3)$$ for the NP-hard Edge Dominating Set problem. Our kernelization algorithm for Edge Dominating Set has its kernel size match the best kernel size by known polynomial-time kernelization algorithms for the problem with no space complexity constraints. We also show that the techniques developed in our algorithms can be used to develop improved streaming algorithms.
UAV Path Planning in Dynamical Environment: A Novel ICACO-IDWA Algorithm

In this paper, a novel UAV path planning algorithm based on improved cellular ant colony algorithm and dynamic window algorithm (ICACO-IDWA) is proposed to solve the problem of dynamically changing threat during actual flight. The main innovations of this paper are as follows. (a) The hexagon grid method is proposed to model the UAV flight space, which solves the problem of inconsistent simulation time step. (b) A novel ICACO-IDWA algorithm is proposed. In the first stage, the optimal path is obtained by the improved cellular ant colony algorithm (ICACO). In the second stage, the improved dynamic window algorithm (IDWA) is used to optimize the optimal path considering dynamic threat. Through the algorithm, the UAV path planning with dynamic threat change is realized. Finally, simulation results verify the effectiveness of the proposed model and algorithm.
Overview

This Chapter provides an overview of the main theme of the Book. Today’s digital technologies, business models for online transactions and most of the academic thinking about copyright and related rights are light years away from those in 1996. The Book examines (i) whether the provisions of the Internet Treaties and the early legislations which transposed the provisions of the Internet Treaties into regional (e.g. European Community) and national legislations (e.g. the U.S.), figuratively legacies of the Palaeolithic era of Internet and other digital technologies, have any relevance in today’s digital world, (ii) if they are still of relevance whether any parts are showing signs of wear and tear and (iii) if so, whether the fixing could be done by tinkering or whether a root and branch revision of the Internet Treaties is called for. This Chapter also gives an overview of the content of each Chapter of the Book.
Composition of Static and Dynamic Analysis for Algorithmic-Level Code Semantic Optimization

Program optimization can be done at various levels like design, algorithm, data structure, source code, and run time. It can be based on various parameters like time, space, and code complexity. Higher the level of optimization, the more significant the impact. This paper proposes algorithm-level optimization. Programs with similar semantics are analyzed on a composite metric dependent on factors such as time, space, cyclomatic complexity, and code quality of snippets to suggest the most optimal algorithmic solution. Factors like time, space are calculated dynamically and approximated using polynomial regression. Factors like cyclomatic complexity and Halstead difficulty metric are calculated statically.
Inharmonic Frequency Analysis of Tabla Strokes in North Indian Classical Music

Tabla is a majorly used accompanying rhythm instrument in North Indian classical music (NICM). In contrast with Western rhythm instruments such as drums, the membranes of the Tabla instrument are loaded with an ink. Due to this ink, the membrane vibrates for a longer duration and creates various inharmonic frequencies (overtones) that contribute to the timbre of the sound. In this research, we have analysed nine basic Tabla strokes with respect to their modes of vibrations and identified the prominent overtone frequencies and their corresponding musical notes that contribute to the homophonic texture of the Tabla sound. Though the duration of the overtones is short, they still contribute to the timbral quality of the Tabla stroke. It is found that both the Tabla drums’ membrane exhibit eight inharmonic overtones, each carrying a frequency corresponding to a musical note. These all notes contribute to the timbral structure of the audio produced by each basic Tabla stroke. This research aims at standardizing the Tabla instrument frequency overtones and corresponding musical notes that contribute to the homophonic texture of Tabla stroke sound.
Utilizing source code syntax patterns to detect bug inducing commits using machine learning models

Detecting Bug Inducing Commit (BIC) or Just in Time (JIT) defect prediction using Machine Learning (ML) based models requires tabulated feature values extracted from the source code or historical maintenance data of a software system. Existing studies have utilized meta-data from source code repositories (we named them GitHub Statistics or GS), n-gram-based source code text processing, and developer’s information (e.g., the experience of a developer) as the feature values in ML-based bug detection models. However, these feature values do not represent the source code syntax styles or patterns that a developer might prefer over available valid alternatives provided by programming languages. This investigation proposed a method to extract features from its source code syntax patterns to represent software commits and investigate whether they are helpful in detecting bug proneness in software systems. We utilize six manually and two automatically labeled datasets from eight open-source software projects written in Java, C++, and Python programming languages. Our datasets contain 642 manually labeled and 4014 automatically labeled buggy and non-buggy commits from six and two subject systems, respectively. The subject systems contain a diverse number of revisions, and they are from various application domains. Our investigation shows the inclusion of the proposed features increases the performance of detecting buggy and non-buggy software commits using five different machine learning classification models. Our proposed features also perform better in detecting buggy commits using the Deep Belief Network generated features and classification model. This investigation also implemented a state-of-the-art tool to compare the explainability of predicted buggy commits using our proposed and traditional features and found that our proposed features provide better reasoning about buggy commit detection compared to the traditional features. The continuation of this study can lead us to enhance software effectiveness by identifying, minimizing, and fixing software bugs during its maintenance and evolution.
An effective veracity management for sentiment analysis in social media using crowdsourcing and multi‐set feature learning

Summary Recent innovations of the Internet‐of‐Things (IoT) are facilitated the data processing to an enormous scale. Nevertheless, the collected data are inherently uncertain because of noise, discrepancy, and incompleteness. To solve these constraints, improving data veracity is crucial for IoT‐based applications. As big data veracity is more critical, it must predict the veracity of tweets automatically for decision‐making. There have been many techniques developed including artificial intelligence and crowdsourcing models to estimate the veracity of tweets from single sources. But, these models are ineffective while the amount of crowd labels for tweets from multiple sources is not feasible. Also, analyzing the sentiment of public opinions has high complexity. Therefore, this article focuses on developing an efficient model to predict the veracity of tweets from multiple sources and analyze the sentiments of the passage. Primarily, different tweets with text labels are collected. Afterward, a crowdsourcing model is introduced which utilizes the crowdflower dataset to obtain the crowd labels for collected tweets. The Generative Adversarial Network (GAN) model is applied to increase the veracity of tweets by handling the low‐veracity tweets. Finally, the multi‐set feature learning model is implemented to classify the sentiment of opinions. The performance results manifest that the proposed model accomplishes superior accuracy of 97% than the classical veracity prediction models.
АНАЛИЗА СТАНДАРДА ДИДАКТИЧКОГ ОБЛИКОВАЊА САДРЖАЈА НА ПРИМЕРУ БАСНИ У ЧИТАНЦИ ЗА ПРВИ РАЗРЕД ОСНОВНЕ ШКОЛЕ ИЗДАВАЧКЕ КУЋЕ ЕДУКА

In this paper, we deal with the analysis of the quality standards of the didactic design of textbooks on the example of a reading book for the first grade published by Eduka (Jović and Jović, 2019). The analysis was performed according to general textbook quality standards (Ivić et al., 2009) and legal frameworks for regulating the quality of textbooks and textbook sets, using the example of fables that are part of the content of the analyzed source. The analysis of the achievement of standards in the field of didactic design in the paper will point out the possibilities of improving the existing didactic-methodical apparatuses that accompany fables. Keywords: textbook quality standards, didactic-methodical apparatus, textbook, reading book, fable
Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder

Unsupervised domain adaptation involves knowledge transfer from a labeled source to unlabeled target domains to assist target learning tasks. A critical aspect of unsupervised domain adaptation is the learning of more transferable and distinct feature representations from different domains. Although previous investigations, using, for example, CNN-based and auto-encoder-based methods, have produced remarkable results in domain adaptation, there are still two main problems that occur with these methods. The first is a training problem for deep neural networks; some optimization methods are ineffective when applied to unsupervised deep networks for domain adaptation tasks. The second problem that arises is that redundancy of image data results in performance degradation in feature learning for domain adaptation. To address these problems, in this paper, we propose an unsupervised domain adaptation method with a stacked convolutional sparse autoencoder, which is based on performing layer projection from the original data to obtain higher-level representations for unsupervised domain adaptation. More specifically, in a convolutional neural network, lower layers generate more discriminative features whose kernels are learned via a sparse autoencoder. A reconstruction independent component analysis optimization algorithm was introduced to perform individual component analysis on the input data. Experiments undertaken demonstrated superior classification performance of up to 89.3% in terms of accuracy compared to several state-of-the-art domain adaptation methods, such as SSRLDA and TLMRA.
3D22MX: Performance Subjective Evaluation of 3D/Stereoscopic Image Processing and Analysis

This work is divided into three parts: (i) a methodology developed for building a 3D/ stereoscopic database, called 3D22MX, (ii) a software tool designed for degradation of 3D/stereoscopic images, and (iii) a psychophysical experiment carried out for a specific type of noise. The novelty of this work is to integrate these three parts precisely to provide not only professionals who design algorithms to estimate three-dimensional image quality but also those who wish to generate new image databases. For the development of the 3D/stereoscopic database, 15 indoor images and 5 outdoor ones were spatial-calibrated and lighted for different types of scenarios. Criteria calibration is different for indoor images with respect to outdoor images. The software tool to degrade 3D/stereoscopic images is designed from MatLab programming language since images captured in the first part are calculated for achieving several image degradation. Our program has ten different types of noises for degradation, such as white Gauss impulse, localvar, spatial correlation, salt &amp; pepper, speckle, blur, contrast, jpeg, and j2k. Due to each type of noise containing up to five levels of degradation, in this proposal, a database of 20 images is required to design a tool for degrading and generating three-dimensional images ranging from all types of noise to yield psychophysical. Finally, there are applied specific criteria to carry out some psychophysical experiments with 3D/stereoscopic images. Moreover, we analyzed the methodology used to qualify and apply images to the j2k noise, explaining every degradation level for this noise.
From Beginning to BEGANing: Role of Adversarial Learning in Reshaping Generative Models

Deep generative models, such as deep Boltzmann machines, focused on models that provided parametric specification of probability distribution functions. Such models are trained by maximizing intractable likelihood functions, and therefore require numerous approximations to the likelihood gradient. This underlying difficulty led to the development of generative machines such as generative stochastic networks, which do not represent the likelihood functions explicitly, like the earlier models, but are trained with exact backpropagation rather than the numerous approximations. These models use piecewise linear units that are having well behaved gradients. Generative machines were further extended with the introduction of an associative adversarial network leading to the generative adversarial nets (GANs) model by Goodfellow in 2014. The estimations in GANs process two multilayer perceptrons, called the generative model and the discriminative model. These are learned jointly by alternating the training of the two models, using game theory principles. However, GAN has many difficulties, including: the difficulty of training the models; criticality in the selection of hyper-parameters; difficulty in the control of generated samples; balancing the convergence of the discriminator and generator; and the problem of modal collapse. Since its inception, efforts have been made to tackle these issues one at a time or in multiples at several stages by many researchers. However, most of these have been handled efficiently in the boundary equilibrium generative adversarial networks (BEGAN) model introduced by Berthelot et al. in 2017. In this work we presented the advent of adversarial networks, starting with the history behind the models and c developments done on GANs until the BEGAN model was introduced. Since some time has elapsed since the proposal of BEGAN, we provided an up-to-date study, as well as future directions for various aspects of adversarial learning.
Synthetic Data Generation for Visual Detection of Flattened PET Bottles

Polyethylene terephthalate (PET) bottle recycling is a highly automated task; however, manual quality control is required due to inefficiencies of the process. In this paper, we explore automation of the quality control sub-task, namely visual bottle detection, using convolutional neural network (CNN)-based methods and synthetic generation of labelled training data. We propose a synthetic generation pipeline tailored for transparent and crushed PET bottle detection; however, it can also be applied to undeformed bottles if the viewpoint is set from above. We conduct various experiments on CNNs to compare the quality of real and synthetic data, show that synthetic data can reduce the amount of real data required and experiment with the combination of both datasets in multiple ways to obtain the best performance.
Robot path planning based on shuffled frog leaping algorithm combined with genetic algorithm

When the shuffled frog leaping algorithm (SFLA) is used to solve the robot path planning problem in obstacle environment, the quality of the initial solution is not high, and the algorithm is easy to fall into local optimization. Herein, an improved SFLA named ISFLA combined with genetic algorithm is proposed. By introducing selection, crossover and mutation operators in genetic algorithm, the ISFLA not only improves the solution quality of the SFLA, but also accelerates its convergence speed. Moreover, the ISFLA also proposes a location update strategy based on the central frog, which makes full use of the global information to avoid the algorithm falling into local optimization. By comparing ISFLA with other algorithms including SFLA in the map environment of different obstacles, it is confirmed that ISFLA can effectively improve the minimum path optimization and robustness in the simulation experiments of mobile robots.
Number of Lines of Image Reconstructed from a Revealing Emission Signal as an Important Parameter of Rasterization and Coherent Summation Processes

An important issue in the protection of information against electromagnetic penetration is the possibility of its non-invasive acquisition. In many cases, getting hold of protected information involves recreating and presenting it in a readable and understandable form. In particular, this applies to data processed in graphic form and in such a form presented on the side of eavesdropping system. The effectiveness of reconstructing data in graphic form requires knowledge of raster parameters, i.e., the line length and the number of lines of the reproduced image. This article presents new measures allowing for the determination of the correct number of lines in an image. The maximum value of the measures has been proposed as a criterion for the correctness of determining the number of image lines. A predetermined number of image lines was assumed as the input data, which was determined on the basis of the analysis of the amplitude variability of the recorded revealing emission signal. The result of the considerations of the effectiveness of the measures adopted in the process of electromagnetic infiltration was the indication of methods that allow for the correct determination of the number of lines of the reproduced image. The correct number of image lines allows the use of the coherent summation algorithm of tens of images.
Application of Profile Prediction for Proactive Scheduling

Today, cloud environments are widely used as execution platforms for most applications. In these environments, virtualized applications often share computing resources. Although this increases hardware utilization, resources competition can cause performance degradation, and knowing which applications can run on the same host without causing too much interference is key to a better scheduling and performance. Therefore, it is important to predict the resource consumption profile of applications in their subsequent iterations. This work evaluates the use of machine learning techniques to predict the increase or decrease in computational resources consumption. The prediction models are evaluated through experiments using real and benchmark applications. Finally, we conclude that some models offer significantly better performance when compared to the current trend of resource usage. These models averaged up to 94% on the F1 metric for this task.
(Retracted) Robust face recognition using multimodal data and transfer learning

The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article.AMS, SDC, SCB, and SP either did not respond directly or could not be reached.