Reviewerly/exHarmony huggingface dataset:
A Novel Efficient Method for Simulating Non-Stationary Random Processes Combining Generalized Harmonic Wavelet and Stochastic Harmonic Function

Integrating the general expression of the generalized harmonic wavelet (GHW)-based spectral representation method (SRM) and the idea of stochastic harmonic function (SHF), a novel stochastic generalized harmonic wavelet (SGHW) method for fully nonstationary stochastic processes, is established. The advantages of the proposed method are (1) a stochastic process with accurate probability information can be obtained by retaining fewer components. A smaller number of components greatly reduces the number of random variables. Fewer random variables also reduce the difficulty of random process analysis. The superior fitting effect between evolutionary power spectral density (EPSD) obtained from the samples using the SGHW method and the EPSD model proves the validity and effectiveness of the SGHW method. (2) The proposed method contains more accurate probability information and has a higher computational efficiency. The comparisons of relative errors and computational time between the SGHW method and the SRM demonstrate the accuracy and efficiency of the proposed method.
A Consumer Behavior Perspective of Adopting Mobile Contact Tracing Apps in a Public Health Crisis: Lessons from ABTraceTogether for COVID-19 Pandemic

Responses to the COVID-19 pandemic included m-Health innovations, such as contact tracing and exposure notification applications to track virus exposure. Such apps were released by over 45 international governments throughout 2020, becoming the first m-Health innovation with such widescale deployment. Most regions relied on voluntary adoption, and many failed to receive a critical mass of users. Some of these apps can track and share user’s locations, social contacts, and health information, which sparked concerns and misperceptions about the privacy and security of user data. It is important to understand consumer behavior and adoption challenges based on people’s perceptions of benefits, barriers, and risks. To investigate this, we sent an online questionnaire to over 600 participants with open-ended questions asking about their experience with one such app, ABTraceTogether. This chapter covers qualitative findings regarding device and application-level issues participants identified as barriers to their adoption and continued usage of the app, which are accessibility, battery life, downloading challenges, device memory, network connectivity and costs, operating system compatibility, performance issues, and usability. Insight on consumer behavior gained from this study can guide m-Health design and promotion to aid future health crises and personal wellbeing.
Malaria Diagnostics

The imminent scenario of malaria burden on endemic regions burdens healthcare and is a threat to non-endemic regions. Microscopy and rapid diagnostic tests (RDTs) remain the gold standard for malaria detection in resource-constrained regions. They still present low sensitivity at low parasite density, however, with microscopy also requiring trained personnel, expensive and time consuming. Affordable, rapid, specific, sensitive and simple malaria diagnostics remain elusive. Molecular-based diagnostics, polymerase chain reaction and loop-mediated isothermal amplification, although highly sensitive even at low parasitemia, still have challenges hindering their use in resource-constrained regions. This chapter discusses the conventional microscopy, spectroscopy, RDTs and molecular platforms in malaria detection. It also highlights current interventions on mitigations of their existing hurdles and adaptability to developing regions. Such inventions include the amalgamation of different techniques, nanotechnology and artificial intelligence.
Multi-Object Recognition Using a Feature Descriptor and Neural Classifier

In the field of object recognition, feature descriptors have proven to be able to provide accurate representations of objects facilitating the recognition task. In this sense, Histograms of Oriented Gradients (HOG), a descriptor that uses this approach, together with Support Vector Machines (SVM) have proven to be successful human detection methods. In this paper, we propose a scheme consisting of improved HOG and a classifier with a neural approach to producing a robust system for object recognition. The main contributions of this work are: First, we propose an improved gradient calculation that allows for better discrimination for the classifier system, which consists of performing a threshold over both the magnitude and direction of the gradients. This improvement reduces the rate of false positives. Second, although HOG is particularly suited for human detection, we demonstrate that it can be used to represent different objects accurately, and even perform well in multi-class applications. Third, we show that a classifier that uses a neuronal approach is an excellent complement to a HOG-based feature extractor. Finally, experimental results on the well-known Caltech 101 dataset illustrate the benefits of the proposed scheme.
Pre-Informing Methods for ANNs

In the recent past, when computers just entered our lives, we could not even imagine what today would be like. If we look at the future with the same perspective today, only one assumption can be made about where technology will go in the near future; Artificial intelligence applications will be an indispensable part of our lives. While today’s work is promising, there is still a long way to go. The structures that researchers define as artificial intelligence today are actually programmed programs with limits and are result-oriented. Real learning includes many complex features such as convergence, association, inference and prediction. It has been demonstrated with an application how to transfer the input layer connections in human neurons to the artificial learning network with the pre-informing method. When the results are compared, the learning load (weights) was reduced from 147 to 9 with the proposed pre-informing method, and the learning rate was increased between 15–30% according to the activation function used.
Perspective Chapter: Actor-Network Theory as an Organising Structure for Blockchain Adoption in Government

Blockchain technology (BT) is a promising technology with compelling distributive and security capabilities for digitalising organisations and social systems. It is, however, often approached from a deterministic and technical perspective yet requires social, cultural and institutional changes as part of the process of adopting new technology in the context of the digital government sectors. This study uses actor-network theory (ANT) for its closely related hybrid middle, translation features, token passing through to network stability and interessement, as a lens to shape and understand the complexities surrounding the adoption and use of BT, particularly in the public sector organisations. Using the land registration system in Sudan as a case study, the findings show that ANT provides an adequate lens through which to examine the role of emergent distributive technologies such as BT in shaping social and organisational processes. ANT further contributes to a more holistic adoption of BT in public sector organisations. In the case study, the proposed blockchain guided by ANT simplified the complexity of land processes for registration, selling, buying and ownership, eventually replacing multiple processes with single transactions while at the same time embedding security and transparency.
Privacy-Preserving Top-k Query Processing Algorithms Using Efficient Secure Protocols over Encrypted Database in Cloud Computing Environment

Recently, studies on secure database outsourcing have been highlighted for the cloud computing environment. A few secure Top-k query processing algorithms have been proposed in the encrypted database. However, the previous algorithms can support either security or efficiency. Therefore, we propose a new Top-k query processing algorithm using a homomorphic cryptosystem, which can support both security and efficiency. For security, we propose new secure and efficient protocols based on arithmetic operations. To obtain a high level of efficiency, we also propose a parallel Top-k query processing algorithm using an encrypted random value pool. Through our performance analysis, the proposed Top-k algorithms present about 1.5∼7.1 times better performance with regard to a query processing time, compared with the existing algorithms.
Method for continuous multiscale representation of terrain contours using T‐spline technique

Abstract Contours are the main graphical element for characterizing three‐dimensional terrain on a topographic map. Continuous multiscale representation of contours is crucial to meet users' needs for detailed information between fixed‐scale contours. Wherein shape of the contour should ideally progressively change as the scale continuously changes. However, the lack of a bridge between scale and shape deformation degrees is an obstacle in obtaining continuous multiscale contours. A novel method for continuous multiscale representation of contour based on T‐spline (CMRTS) is proposed. Multiscale contours are generated through interpolation of the T‐spline and constraint of the relationship between scale and shape similarity. First, a T‐spline surface is established using two fixed‐scale contours. Second, the T‐spline surface intersects a set of horizontal planes with gradual heights to generate intersecting lines. Finally, the quantitative relationship between the scale and shape similarity is determined to align each intersecting line with the corresponding scale and achieve continuous multiscale contours. A case study in Chongqing, China implements and evaluates the proposed method. Results indicate that shape changes of contours generated by CMRTS are more natural and progressive than those by two typical methods. Our method shows high potential and practicability for mapping continuous multiscale contours for real‐world terrain applications.
Improvement of K-Means Algorithm and Its Application in Air Passenger Grouping

The k-means is one of the most popular clustering analysis algorithm and widely used in various fields. Nevertheless, it continues to have some shortcomings, for example, extremely sensitive to the initial center points selection and the special points such as noise or outliers. Therefore, this paper proposed initial center points' selection optimization and phased assignment optimization to improve the k-means algorithm. The experimental results on 15 real-world and 10 synthetic datasets show that the improved k-means outperforms its main competitor k-means ++ and under the same setting conditions, namely, using the default parameters,its clustering performance is better than Affinity Propagation, Mean Shift, and DBSCAN. The proposed algorithm was applied to analyze the airline seat selection data to air passengers grouping. The clustering results, as well as absolute deviation rate analysis, realized customer grouping and found out suitable audience group for the recommendation of seat selection services.
Finish-line photography system based on multi-scale convolutional neural network deblurring

In sports science research, the dynamic non-uniform blur caused by the movement of running athletes is a challenging problem in computer vision, that seriously affects the judgment accuracy of the finish-line photography system. With the rapid development of deep learning technology, image preprocessing, object identification, and object classification have been widely used and studied. This work proposes multi-scale convolutional neural network image deblurring to eliminate dynamic blur generated by the athletes in the shooting process. This network comprises three end-to-end convolutional neural subnetworks of different scales to recover the blurry athlete image caused by various factors on the field. The system effectively extracts the detailed edge of the image on each scale from coarse to fine. Many experiments show that this method can deblur the image captured by the finish-line photography system in real-time and rapidly achieve a better visual effect in the athlete’s dynamic image.
Experience-Based Constructionism as a Basis for HCI Education: A Case Study

As new technologies constantly change what we understand as a computer, Human–Computer Interaction (HCI) educators need to stay updated and prepare their students to work with an ever-growing number of socio-technical situations. This phenomenon constitutes a challenge for HCI syllabuses and practices, demanding diversified approaches to HCI education. In this article, we articulate Papert’s constructionism with Dewey’s theory of experience to propose an experience-based constructionism approach to practices in HCI education. We illustrate our approach in a case study with 55 computing undergraduate students engaged in the design and construction of open-ended, physical interactive artworks. Nine interactive artifacts were created. The students have shown an effective experience with our approach, reporting an expanded view of HCI, and demonstrating competence in appropriating new methods and instruments, suggesting the effectiveness of our approach to HCI education. Results of this work may encourage other HCI educators and practitioners to experience the proposed approach in their specific contexts.
MONITORING OF MODERN GEODYNAMIC PROCESSES IN THE TERRITORY OF EASTERN KAZAKHSTAN BY METHODS OF SATELLITE GEODESY AND LINEAMET ANALYSIS

The work presented in this article was primarily aimed at studying the geodynamic features of the development of the territories of the East Kazakhstan region, located in various seismogeological conditions. The results of these works are presented in the form of maps developed on the basis of the use of remote and GPS data. Since the development of geodynamic substantiation by ground methods requires significant amounts of time and funds for exploration work, the authors proposed a complex based on the interpretation of satellite imagery data.
Multi-donor Neural Transfer Learning for Genetic Programming

Genetic programming (GP), for the synthesis of brand new programs, continues to demonstrate increasingly capable results towards increasingly complex problems. A key challenge in GP is how to learn from the past so that the successful synthesis of simple programs can feed into more challenging unsolved problems. Transfer Learning (TL) in the literature has yet to demonstrate an automated mechanism to identify existing donor programs with high-utility genetic material for new problems, instead relying on human guidance. In this article we present a transfer learning mechanism for GP which fills this gap: we use a Turing-complete language for synthesis, and demonstrate how a neural network (NN) can be used to guide automated code fragment extraction from previously solved problems for injection into future problems. Using a framework which synthesises code from just 10 input-output examples, we first study NN ability to recognise the presence of code fragments in a larger program, then present an end-to-end system which takes only input-output examples and generates code fragments as it solves easier problems, then deploys selected high-utility fragments to solve harder ones. The use of NN-guided genetic material selection shows significant performance increases, on average doubling the percentage of programs that can be successfully synthesised when tested on two different problem corpora, compared with a non-transfer-learning GP baseline.
Three-Dimensional Affine Spatial Logics

Abstract We focus on a branch of region-based spatial logics dealing with affine geometry. The research on this topic is scarce: only a handful of papers investigate such systems, mostly in the case of the real plane. Our long-term goal is to analyse certain family of affine logics with inclusion and convexity as primitives interpreted over real spaces of increasing dimensionality. In this article we show that logics of different dimensionalities must have different theories, thus justifying further work on different dimensions. We then focus on the three-dimensional case, exploring the expressiveness of this logic and consequently showing that it is possible to construct formulas describing a three-dimensional coordinate frame. The final result, making use of the high expressive power of this logic, is that every region satisfies an affine complete formula, meaning that all regions satisfying it are affine equivalent.
SSD-SGD: Communication Sparsification for Distributed Deep Learning Training

Intensive communication and synchronization cost for gradients and parameters is the well-known bottleneck of distributed deep learning training. Based on the observations that Synchronous SGD (SSGD) obtains good convergence accuracy while asynchronous SGD (ASGD) delivers a faster raw training speed, we propose Several Steps Delay SGD (SSD-SGD) to combine their merits, aiming at tackling the communication bottleneck via communication sparsification. SSD-SGD explores both global synchronous updates in the parameter servers and asynchronous local updates in the workers in each periodic iteration. The periodic and flexible synchronization makes SSD-SGD achieve good convergence accuracy and fast training speed. To the best of our knowledge, we strike the new balance between synchronization quality and communication sparsification, and improve the tradeoff between accuracy and training speed. Specifically, the core components of SSD-SGD include proper warm-up stage, steps delay stage, and the novel algorithm of global gradient for local update (GLU). GLU is critical for local update operations by using global gradient information to effectively compensate for the delayed local weights. Furthermore, we implement SSD-SGD on MXNet framework and comprehensively evaluate its performance with CIFAR-10 and ImageNet datasets. Experimental results show that SSD-SGD can accelerate distributed training speed under different experimental configurations, by up to 110% (or 2.1× of the original speed), while achieving good convergence accuracy.
Cortex-Based Mechanism For Discovery of High-Order Features

Recognition of significant features in the sensory input patterns is one of the brain’s fundamental information processing tasks. This is a challenging task, since behaviorally significant environmental features are orders of complexity removed from raw sensory inputs. Based on a proposal that learning of significant environmental variables is performed in the cerebral cortex by the dendrites of individual neurons, we describe a computational mechanism (SINBAD cell) that discovers orderly features through a search for different, but nevertheless highly correlated functions over non-overlapping subsets of the available environmental variables. Organized into series of cortex-like layers, such SINBAD cells ought to be able to discover complex orderly features present in the input patterns.
On the security of TrCBC

TrCBC is a variant of CBC-MAC which appeared in Zhang et al. (2012) [9]. The authors claimed TrCBC to be a secure message authentication code (MAC) with some interesting properties. If TrCBC is instantiated with a block cipher with block length n, then it requires ⌈λ/n⌉ block cipher calls for authenticating a λ-bit message and requires a single key, which is the block cipher key. The authors state that TrCBC can have tag lengths of size less than n/2. We show that with high probability, an adversary can forge TrCBC with tag length n/2−1 with just three queries. The attack that we show can be applied to forge a large class of messages. The authors proved TrCBC to be a pseudorandom function (PRF). A scrutiny of the claimed PRF bound shows that for some recommended values of tag lengths, the bound turns out to be quite large. Thus, the security theorem does not imply security of TrCBC for all recommended tag lengths.
An Agile Leadership Framework for Business Leaders to Launch, Evaluate, and Sustain Social Innovation

Increasing equality and accelerating purposeful growth are challenges in our world. In the post-pandemic era, we see many social innovation efforts are set back by years. Now, enterprises need to “build back better” – businesses that offer profit-generating solutions that are innovative, practical, and sustainable. These business practices are intended to create a diverse and inclusive society. These also close the gap between “those who have” and “those who have not.” With all these top of mind for leaders globally, the question is, how will leaders venture into where they have not been before? In this article, the author offers a possible framework for global leaders to evaluate their strategies with a holistic lens and guide their actions with agility to launch, sustain, and scale programs that are both purposeful and profitable.
An emotion analysis dataset of course comment texts in massive online learning course platforms

Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning platforms using the “three-person voting label method” based on the “sentence-level” and multi-category labeling dimensions with our self-developed system. After testing the consistency of the labeling results using the Fleiss Kappa method, we found that the consistency of the dataset was about 0.51, representing a moderate strength of agreement. Based on the dataset, the prediction accuracy of the Long-Short Term Memory (LSTM) method is about 0.68. This dataset provides a benchmark for the multi-category emotion dataset in the Chinese online learning field. It can provide a basis for the subsequent solution of emotion analysis, monitoring, and intervention in the education field. It can also provide a reference for constructing subsequent datasets in the education field.
Visual Place Recognition via a Multitask Learning Method With Attentive Feature Aggregation

Visual place recognition has gained popularity in recent years. Mainstream convolutional neural network-based methods formulate it as a ranking task and optimize it in the paradigm of deep metric learning, however, the ranking-motivated losses concern only the ranking relationship for each query image and the compactness of intraplace feature distribution is seldom considered. It is still challenging due to varying viewpoints, illuminations, and even dynamic objects. In this article, a novel multitask learning framework is proposed, which combines the existing triplet ranking task and our designed binary classification task to jointly optimize the network for better generalization capability. Specifically, a binary classification network with the corresponding binary cross-entropy loss is designed in the classification task. In this way, the intraplace feature compactness and interplace feature separability are reinforced. At the testing stage, this classification network is discarded without increasing the computation cost. Furthermore, an attention module is presented to promote the network to concentrate on the salient regions by assigning different importance to each spatial position. Our method achieves the top-10 recalls of 97.27%, 94.6%, and 96.93% on Pitts250k-test, Tokyo 24/7, and TokyoTM-val data sets, respectively. Extensive experiments prove that the proposed network can learn discriminative global features with better robustness to viewpoints and environmental variations.