Reviewerly/exHarmony huggingface dataset:
Datatype-generic programming meets elaborator reflection

Datatype-generic programming is natural and useful in dependently typed languages such as Agda. However, datatype-generic libraries in Agda are not reused as much as they should be, because traditionally they work only on datatypes decoded from a library’s own version of datatype descriptions; this means that different generic libraries cannot be used together, and they do not work on native datatypes, which are preferred by the practical Agda programmer for better language support and access to other libraries. Based on elaborator reflection, we present a framework in Agda featuring a set of general metaprograms for instantiating datatype-generic programs as, and for, a useful range of native datatypes and functions —including universe-polymorphic ones— in programmer-friendly and customisable forms. We expect that datatype-generic libraries built with our framework will be more attractive to the practical Agda programmer. As the elaborator reflection features used by our framework become more widespread, our design can be ported to other languages too.
Beyond Relooper: recursive translation of unstructured control flow to structured control flow (functional pearl)

In many compilers, control flow is represented using an arbitrary directed graph. But in some interesting target languages, including JavaScript and WebAssembly, intraprocedural control flow can be expressed only in structured ways, using loops, conditionals, and multilevel breaks or exits. As was shown by Peterson, Kasami, and Tokura in 1973, such structured control flow can be obtained by translating arbitrary control flow. The translation uses two standard analyses, but as published, it takes three passes—which may explain why it was overlooked by Emscripten, a popular compiler from C to JavaScript. By tweaking the analyses and by applying fundamental ideas from functional programming (recursive functions and immutable abstract-syntax trees), the translation, along with a couple of code improvements, can be implemented in a single pass. This new implementation is slated to be added to the Glasgow Haskell Compiler. Its single-pass translation, its immutable representation, and its use of dominator trees make it much easier to reason about than the original translation.
‘do’ unchained: embracing local imperativity in a purely functional language (functional pearl)

Purely functional programming languages pride themselves with reifying effects that are implicit in imperative languages into reusable and composable abstractions such as monads. This reification allows for more exact control over effects as well as the introduction of new or derived effects. However, despite libraries of more and more powerful abstractions over effectful operations being developed, syntactically the common 'do' notation still lags behind equivalent imperative code it is supposed to mimic regarding verbosity and code duplication. In this paper, we explore extending 'do' notation with other imperative language features that can be added to simplify monadic code: local mutation, early return, and iteration. We present formal translation rules that compile these features back down to purely functional code, show that the generated code can still be reasoned over using an implementation of the translation in the Lean 4 theorem prover, and formally prove the correctness of the translation rules relative to a simple static and dynamic semantics in Lean.
User-Defined Foot Gestures for Eyes-Free Interaction in Smart Shower Rooms

With the rapid development of natural human-computer interaction technologies, gesture-based interfaces have become popular. Although gesture interaction has received extensive attention from both academia and industry, most existing studies focus on hand gesture input, leaving foot-gesture-based interfaces underexplored, especially in scenarios where the user’s hands are occupied for other interaction tasks such as washing the hair in smart shower rooms. In such scenarios, users often have to perform interactive tasks (e.g., controlling water volume) with their eyes closed when water and shampoo liquid flow along with their head to eyes area. One possible way to address this problem is to use eyes-free (rather than eyes-engaged), foot-gesture-based interactive techniques that allow users to interact with the smart shower system without visual involvement. Through our online survey, 71.60% of the participants (58/81) have the requirements of using foot-gesture-based eyes-free interactions during showers. To this end, we conducted a three-phase study to explore foot-gesture-based interaction to achieve eyes-free interaction in smart shower rooms. We first derived a set of user-defined foot gestures for eyes-free interaction in smart shower rooms. Then, we proposed a taxonomy for foot gesture interaction. Our findings indicated that end-users preferred single-foot (76.1%), atomic (73.3%), deictic (65.0%), and dynamic (76.1%) foot gestures, which markedly differs from the results reported by previous studies on user-defined hand gestures. In addition, most of the user-defined dynamic foot gestures involve atomic movements perpendicular to the ground (40.1%) or parallel to the ground (27.7%). We finally distilled a set of concrete guidelines for foot gesture interfaces based on observing end-users’ mental model and behaviors when interacting with foot gestures. Our research can inform the design and development of foot-gesture-based interaction techniques for applications such as smart homes, intelligent vehicles, VR games, and accessibility design.
Improved Artificial Bee Colony Algorithm with Adaptive Pursuit Based Strategy Selection

Adaptive parameter setting techniques are called parameter control and they are more advantageous than offline tuning approaches. Because parameter tuning itself is an optimization problem and may be time-consuming. Moreover, optimization is a dynamic process and the parameter set-up at the initial stage of the search may not perform well in the later stages. Artificial Bee Colony (ABC) is one of the important algorithms in metaheuristic optimization. In order to contribute to the literature on parameter control in ABC, this study presents an Adaptive Pursuit (AP) selection rule-based improved ABC algorithm (IABC-AP) that automatically controls among 9 search strategies while the optimization process is running. The AP updates the selection probabilities of alternative operators based on their performance and reflects the changes in search space. In order to evaluate the performance of the developed algorithm, an experimental study was performed using a common continuous optimization benchmark called CEC 2017. The numerical results showed that the adaptive selection among 9 search strategies works successfully that the IABC-AP outperforms each of the individual strategies employed. Also, it was seen that the proposed method provides competitive results with other ABC algorithms.
Space–Time Concept in Social Network Search Algorithm

Nowadays, social networks have become popular platforms in which users can interact with each other virtually. The relationship among users, their influence on each other, and similarity in their behavior are the main features of users in the social networks. These features are utilized in the development of an optimization method, which is called the Social Network Search (SNS) algorithm. The SNS method is a recently developed optimizer, which models the treatment of users in expressing their new views. Four novel optimization operators with new formulations are invented, which are called decision moods. These moods are named imitation, conversation, disputation, and innovation, which are real-world behaviors of users in social networks and model how users are affected and motivated to share their new views. This chapter embeds the concept of space–time in the implementation process of the SNS algorithm to developing space–time social network search (STSNS) algorithm. Single-objective, bound-constraint benchmark problems of IEEE congress on evolutionary computation 2014 (CEC 2014) are utilized to study the efficiency of the STSNS algorithm in solving challenging optimization problems. In addition, the results of the STSNS method is compared with a variety range of optimization methods, including eight state-of-the-art, eight popular, and eight novel methods from the literature. Two well-known non-parametric statistical methods, Friedman and Wilcoxon signed-rank, are utilized to analyze the performance of the developed method, and results demonstrate its superiority in dealing with most of the selected complex optimization problems.
Beetle Antennae Search Algorithm for the Motion Planning of Industrial Manipulator

Beetle antennae search (BAS) algorithm as a computational intelligence algorithm has excellent nonlinear optimization ability applied in scientific and engineering applications. In this chapter, BAS is applied to the redundancy resolution of an industrial manipulator with dynamic joint velocity constraints by searching in high-dimensional space. The addressed application does not need to construct inverse kinematics equations in joint velocity level but directly uses forward kinematics to construct antennae fitness function. The experiment is practiced in the CoppeliaSim simulator for the industrial IIWA Kuka industrial manipulator to verify the performance.
Design and Performance Evaluation of Objective Functions Based on Various Measures of Fuzzy Entropies for Image Segmentation Using Grey Wolf Optimization

Image segmentation is the most significant pre-processing phase of computer vision. Thresholding is one of most suitable approach used for the segmentation of image. Searching an optimal value of threshold for image segmentation has got more attention in recent years due to its accuracy and robustness. Over the years, number of researchers has successfully used Shannon and non-Shannon measures of fuzzy entropy based objective function in various evolutionary algorithms to find the optimal value of threshold for image segmentation. In this paper, grey wolf optimization (GWO) algorithm is used along with Shannon and non-Shannon measures of fuzzy entropy for selecting optimal threshold value to segment image in reasonable amount of time. The objective of the proposed research work is to design objective functions based on Shannon and non-Shannon measures of fuzzy entropy for GWO and using them for optimal threshold value selection which will further be used to segment images. Non-Shannon measures of fuzzy entropy used for design of objective functions are Renyi, Havrda-Charvat, Kapur, and M. Masi. The performance of Shannon and non-Shannon fuzzy entropies using GWO is compared with Shannon fuzzy entropy using GA, BBO and recursive approach to find the optimal threshold value. Comparison is done based on the computation time and four image segmentation quality evaluation parameters i.e. PSNR; uniformity; SSIM and MSSIM. The experiments are conducted on a set of ten different kinds of benchmark images that has variant characteristics. Experimental results indicate that the performance of fuzzy Shannon entropy using GWO is better than Shannon fuzzy entropies using GA, BBO, and recursive approach. From overall performances point of view, Havrda-Charvat fuzzy entropy using GWO is performing better than all other approaches in terms of quality of the segmented image and computational time point of view as well.
Virtual contacts

Abstract Computer mediated communication (CMC) has changed everyday life, including the ways we use language—which takes us back to where we began: algorithms. It is not the term that concerns us here but how we use what has been described as the ‘most potent tool that people have had at their disposal since the beginnings of human history’. Its applications are expanding by the day, and the changes brought about by digitalization based on it are manifold affecting us in unanticipated ways, including the nexus of language and mobility. Our focus in this chapter is on this relationship under conditions of the ever-increasing interconnectedness thanks to the algorithmic advances we have experienced over the last few decades. In order to understand the ongoing changes, it puts the medial development from small-scale orality to world-spanning CMC in historical context.
Preface

Extract This textbook is based on a lecture course the author has given to first-year physics students at the University of Oxford and it profits from many years of tutoring physics students at Balliol College Oxford. It is an attempt to provide a modern introduction for students in the mathematical sciences into the classical subject of linear algebra. Linear algebra is one of the basic disciplines of mathematics and it underlies many branches of more advanced mathematics, such as analysis in several variables and differential geometry. Put simply, linear structures are the building blocks for many more advanced constructions in mathematics. Frequently, linear algebra is also the first serious course in mathematics that undergraduate students have to face. It brings about the first rigorous proofs, which mark a definite departure from the standard high-school training of applying formulas and performing routine calculations. This transition can be difficult. At the same time, linear algebra has many important applications in practically all areas of the mathematical sciences. The principle of linear superposition underlies a large variety of physical laws. Linearity is the leading approximation to systems near equilibrium so that even non-linear systems can often be described by linear counterparts. Methods of linear algebra also play an important role in computer and data sciences. For example, linear algebra is an important ingredient of artificial neural networks.
Logical Consequence

Abstract This chapter explains the central concept of this book — the concept of consequence relation, as well as its counterpart, that of consequence operator. We prove the main properties of these concepts. In addition, we show several ways in which consequence relations can be defined and modified. We pay special attention to the determination of consequence relations using logical matrices and inference rules. The first way leads to the Lindenbaum method and the Lindenbaum theorem. The second method is very natural, and we consider several of its options. In particular, we discuss in some detail the definition of consequence relations defined by modus rules. We also discuss in this chapter the idea of completeness.
Sentential Formal Languages and Their Interpretation

Abstract This chapter introduces the type of schematic formal language that is used in Chapters 2–6 and 10 of the book. This sentential schematic language is illustrated by a number of examples. Also in this chapter, we define the operation of uniform (or simultaneous) substitution and the semantics for this language by introducing the concept of a logical matrix. The latter concept is also illustrated by a number of examples.
Neuro‐weighted multi‐functional nearest‐neighbour classification

Abstract Background The performance of nearest‐neighbour classification is highly sensitive to the quality of data. In order to reduce the impact of the inevitable existence of irrelevant features in data, this paper employs the information learned by neural networks to implement a feature weighting technique and associated weighted multi‐functional nearest‐neighbour classification method. Method The non‐iterative neural networks are easy to implement while enjoying remarkable computational efficiency. In this paper, four non‐iterative neural networks (ELM, E‐ELM, RAWN and RVFL) are employed to evaluate the significance of features to decisions learned and stored in the parameters of neural networks. Moreover, the bias of the significance of features is comforted by using cross‐validation. The resulting feature significance is converted into feature weights to further implement weighted multi‐functional nearest‐neighbour classification. Result The experimental results demonstrate that the proposed neuro‐weighted feature weighting strategy can effectively reduce the impact of irrelevant features and enhance the performance of multi‐functional nearest‐neighbour classification. Contribution The proposed algorithm explores an avenue to efficiently utilize the learned knowledge of neural networks to reduce the role of irrelevant feature in nearest‐neighbour classification.
University Educational Administration Management Platform Integrating Distributed Real-time Cloud Computing System

The prosperity of the economy has made the society develop rapidly, and the traditional education management system can no longer meet the needs of the modern society. Under the background of college enrollment expansion, it has brought new challenges to school management and development. In recent years, the evolution and widespread adoption of virtualization, service-oriented architecture, autonomous, and utility computing have converged to give birth to a new paradigm: cloud computing. Cloud computing allows on-demand delivery of software, hardware, and data as a service. Cloud computing has the characteristics of massive processing and storage capacity, high efficiency, virtualization, and low cost. It has changed the traditional way of accessing software and hardware, providing users with convenient services and computing resources, which has achieved remarkable results in the related fields. Meanwhile, cloud computing plays an important role in education and teaching through its wide application in various fields. Based on this, this paper aims to study the application of distributed real-time cloud computing system in educational administration management in colleges and universities. This paper establishes a university educational administration management platform based on the cloud service computing system, and applies the management technology and distributed storage technology of the cloud computing platform. The system constructed in this paper significantly improves the utilization of CPU and memory. The research results of this paper showed that the response speed of the educational administration management system built through cloud computing has been significantly improved, with an average response time of 1.3s and an error of less than 0.4.
Inamori’s Strategy Analysed Through Financial Statements: Inception-1981, 1973–1974, 1974–1979, 1979–1983, 1983–1985, and 1973–1989

In Chapter 6 we analysed the Amoeba Management System as the primary tool for implementing Inamori Management Philosophy, and we now proceed to the study of Kyocera’s financial strategy that reflects the business and corporate strategy followed by Kyocera under Kazuo Inamori’s leadership. This study will help to obtain a more thorough understanding of Inamori Management Philosophy through the trajectory of the movements of Kyocera’s financial resources.
Application Based Network Traffic Dataset and SPID Analysis

Currently, web-based applications have become a part of every piece of our daily lives. The rapid advancements in these applications which have found its use in variety of sectors has made it necessary for the respective security systems to adapt as fast in order to identify these applications. In this work, some up-to-date and commonly used applications&#x2019; web traffic data have been collected for network traffic classification problem and they are presented for the use of researchers. In addition, an analysis of these data with respect to this problem is performed using features based on statistical protocol identification. It has been shown that for the traffic classification, training a Random Forest classifier with these features is more effective than using the mean KL divergence which was used in previous work with these features.
Image Forgery Detection Based On Parallel Convolutional Neural Networks

As a result of the advancement of software tools used in digital image processing, it has become very easy to generate fake images by applying various manipulations techniques on the original (authentic) images. These manipulated images can easily be used with malicious intentions in important fields such as law, medicine and communication. Hence, image forgery detection, determining whether an image is original or forged, is an important task. In this study, an image forgery detection system is proposed by combining three deep neural network structures in parallel, unlike the uniform deep learning methods used in image forgery detection. The proposed method has been evaluated on three different datasets, and the results clearly demonstrate the efficiency of the proposed method with promising classification accuracy.
Transfer Learning Based Super Resolution of Aerial Images

Images created using the Super Resolution method can generate more information compared to their low resolution counterparts. A super-resolved image, which is created using an original image captured by an imaging source is not only more meaningful to human perception but also has advantages on downstream tasks such as object detection and pattern recognition. In this work, we aim to apply the Super Resolution method to the Aerial Images captured for surveillance to enable more information about the original scenes. To achieve this Super Resolution Generative Adversarial Network (SRGAN), which is based on the Generative Adversarial Networks architecture is used. We also applied transfer learning methodology to achieve better image quality. Public xView and DOTA datasets which contain images mostly captured by satellites around the world are used to train a generative model via SRGAN architecture. Furthermore, DIV2K dataset is used to pre-train a generative model, and then the transfer learning technique is used to train separate models on xView and DOTA validation datasets. Perceptual Index (PI) and Root Mean Squared Error (RMSE) which are used on European Conference on Computer Vision -Perceptual Image Restoration and Manipulation Workshop 2018 are computed as the performance metrics. We have seen that the model which gives the best PI results, i.e. better perceptual quality, on xView and DOTA validation datasets is the one trained using the DIV2K dataset and the model which gives the best RMSE results, i.e. better reconstruction quality, is the one trained using the transfer learning technique.
Improving tracking with a tracklet associator

Multiple object tracking (MOT) is a task in computer vision that aims to detect the position of various objects in videos and to associate them to a unique identity. We propose an approach based on Constraint Programming <tex>$(CP)$</tex> whose goal is to be grafted to any existing tracker in order to improve its object association results. We developed a modular algorithm divided into three independent phases. The first phase consists in recovering the tracklets pro-vided by a base tracker and to cut them at the places where uncertain associations are spotted, for exam-ple, when tracklets overlap, which may cause identity switches. In the second phase, we associate the previ-ously constructed tracklets using a Belief Propagation Constraint Programming algorithm, where we pro-pose various constraints that assign scores to each of the tracklets based on multiple characteristics, such as their dynamics or the distance between them in time and space. Finally, the third phase is a rudimen-tary interpolation model to fill in the remaining holes in the trajectories we built. Experiments show that our model leads to improvements in the results for all three of the state-of-the-art trackers on which we tested it (3 to 4 points gained on HOTA and IDF1).