Reviewerly/exHarmony huggingface dataset:
Unsupervised feature selection via transformed auto-encoder

As one of the fundamental research issues, feature selection plays a critical role in machine learning. By the removal of irrelevant features, it attempts to reduce computational complexities of upstream tasks, usually with computation accelerations and performance improvements. This paper proposes an auto-encoder based scheme for unsupervised feature selection. Due to the inherent consistency, this framework can solve traditional constrained feature selection problems approximately. Specifically, the proposed model takes non-negativity, orthogonality, and sparsity into account, whose internal characteristics are exploited sufficiently. It can also employ other loss functions and flexible activation functions. The former can fit a wide range of learning tasks, and the latter has the ability to play the role of regularization terms to impose regularization constraints on the model. Thereinafter, the proposed model is validated on multiple benchmark datasets, where various activation and loss functions are analyzed for finding better feature selectors. Finally, extensive experiments demonstrate the superiority of the proposed method against other compared state-of-the-arts.
Trumping Hate on Twitter? Online Hate Speech in the 2016 U.S. Election Campaign and its Aftermath

Trumping Hate on Twitter? Online Hate Speech in the 2016 U.S. Election Campaign and its Aftermath
CNN-based intelligent safety surveillance in green IoT applications

Safety surveillance is considered one of the most important factors in many constructing industries for green internet of things (IoT) applications. However, traditional safety monitoring methods require a lot of labor source. In this paper, we propose intelligent safety surveillance (ISS) method using a convolutional neural network (CNN), which is an auto-supervised method to detect workers whether or not wearing helmets. First, to train the CNN-based ISS model, the labeled datasets mainly come from two aspects: 1) our labeled datasets with the full labeled on both helmet and pedestrian; 2) public labeled datasets with the parts labeled either on the helmet or pedestrian. To fully take advantage of all datasets, we redesign CNN structure of network and loss functions based on YOLOv3. Then, we test our proposed ISS method based on the specific detection evaluation metrics. Finally, experimental results are given to show that our proposed ISS method enables the model to fully learn the labeled information from all datasets. When the threshold of intersection over union (IoU) between the predicted box and ground truth is set to 0.5, the average precision of pedestrians and helmets can reach 0.864 and 0.891, respectively.
Investigating immersive learning technology intervention in architecture education: a systematic literature review

Purpose This study intends to review the existing studies on the application of immersive learning technology (ILT) in architecture education field. A systematic literature review (SLR) was conducted on the characteristics and implementation of ILT, research purpose, approach and outcome of research. Design/methodology/approach The PICO concept (Population, Intervention, Comparison, Outcome measures) was used to form the research keywords. The Scopus database was searched and supported by supplementary search on Google Scholar, ProQuest, Emerald Insight and Springer Link. Based on the inclusion and exclusion criteria, 19 peer-reviewed journal articles published between 2013 and 2019 were identified. Findings Virtual reality was found to be the most prevalent ILT applied in architecture education and commonly used as simulation. Most of the studies were applied in year-two architecture study and were mainly implemented for architecture design subject. Very few studies have associated the use of ILT with learning theories, and most of the existing studies have examined the effect of ILT on learning performance. Motivation was found to be the dominant emotional state, and most of the interventions deal with a higher cognitive level. Research limitations/implications Only a small number of articles were selected due to the limited number of studies on the subject. Nevertheless, analysis of the selected few has provided valuable insight into the current scenario of the research topic. Originality/value This study adds to the existing literature by examining the existing empirical evidence on ILT intervention in the architecture education field. The findings will contribute towards innovating the learning process among architecture students and encouraging the use the ILT as part of architecture education system in higher education institutions.
Translation Mining: Definiteness across Languages (A Reply to Jenks 2018)

We present a parallel corpus study that compares the distribution of German contracted/uncontracted articles and Mandarin bare nouns/demonstratives. Work by Schwarz (2009) and Jenks (2018) leads us to predict that German contracted articles pattern with Mandarin bare nouns and German uncontracted articles with Mandarin demonstratives. We show that these predictions are only partly borne out and argue for a more fine-grained typology of definiteness.
DifuzzRTL: Differential Fuzz Testing to Find CPU Bugs

Security bugs in CPUs have critical security impacts to all the computation related hardware and software components as it is the core of the computation. In spite of the fact that architecture and security communities have explored a vast number of static or dynamic analysis techniques to automatically identify such bugs, the problem remains unsolved and challenging largely due to the complex nature of CPU RTL designs.This paper proposes DIFUZZRTL, an RTL fuzzer to automatically discover unknown bugs in CPU RTLs. DIFUZZRTL develops a register-coverage guided fuzzing technique, which efficiently yet correctly identifies a state transition in the finite state machine of RTL designs. DIFUZZRTL also develops several new techniques in consideration of unique RTL design characteristics, including cycle-sensitive register coverage guiding, asynchronous interrupt events handling, a unified CPU input format with Tilelink protocols, and drop-in-replacement designs to support various CPU RTLs. We implemented DIFUZZRTL, and performed the evaluation with three real-world open source CPU RTLs: OpenRISC Mor1kx Cappuccino, RISC-V Rocket Core, and RISC-V Boom Core. During the evaluation, DIFUZZRTL identified 16 new bugs from these CPU RTLs, all of which were confirmed by the respective development communities and vendors. Six of those are assigned with CVE numbers, and to the best of our knowledge, we reported the first and the only CVE of RISC-V cores, demonstrating its strong practical impacts to the security community.
What We Can Learn From Visual Artists About Software Development

This paper explores software's role in visual art production by examining how artists use and develop software. We conducted interviews with professional artists who were collaborating with software developers, learning software development, and building and maintaining software. We found artists were motivated to learn software development for intellectual growth and access to technical communities. Artists valued efficient workflows through skilled manual execution and personal software development, but avoided high-level forms of software automation. Artists identified conflicts between their priorities and those of professional developers and computational art communities, which influenced how they used computational aesthetics in their work. These findings contribute to efforts in systems engineering research to integrate end-user programming and creativity support across software and physical media, suggesting opportunities for artists as collaborators. Artists' experiences writing software can guide technical implementations of domain-specific representations, and their experiences in interdisciplinary production can aid inclusive community building around computational tools.
New Sequential and Parallel Support Vector Machine with Grey Wolf Optimizer for Breast Cancer Diagnosis

Breast cancer is one of the most common types of cancer worldwide. Early detection of cancer increases the probability of recovery. This work has three contributions. The first contribution is improving the performance of support vector machine (SVM) using a recent grey wolf optimizer (GWO) for diagnosis breast cancer with efficient scaling techniques. The second contribution is proposing three efficient scaling techniques against the classical normalization technique. The last contribution is using a parallel technique which applies task distribution to improve the efficiency of GWO. The proposed sequential model is applied on two different datasets, Wisconsin diagnosis breast cancer (WDBC) dataset and Electronic Health Records (EHR). Experimental results of WDBC show that the proposed hybrid GWO-SVM model achieves 98.60% with normalization scaling. Also, using the proposed scaling techniques with the proposed GWO-SVM model gives a fast convergence and achieves accuracy rate by 99.30%. The parallel version of the proposed model achieves a speedup by 3.9 on four CPU cores. On the other hand, Experimental results of EHR show that the proposed hybrid GWO-SVM model achieves 93.26% with normalization scaling against 82.05 for SVM.
Residual scale attention network for arbitrary scale image super-resolution

Research on super-resolution has achieved great success on synthetic data with deep convolutional neural networks. Some recent works tend to apply super-resolution to practical scenarios. Learning an accurate and flexible model for super-resolution of arbitrary scale factor is important for realistic applications, while most existing works only focus on integer scale factor. In this work, we present a residual scale attention network for super-resolution of arbitrary scale factor. Specifically, we design a scale attention module to learn discriminative features of low-resolution images by introducing the scale factor as prior knowledge. Then, we utilize quadratic polynomial of the coordinate information and scale factor to predict pixel-wise reconstruction kernels and achieve super-resolution of arbitrary scale factor. Besides, we use the predicted reconstruction kernels in image domain to interpolate low-resolution image and obtain coarse high-resolution image first, then make our main network learn high-frequency residual image from feature domain. Extensive experiments on both synthetic and real data show that the proposed method outperforms state-of-the-art super-resolution methods of arbitrary scale factor in terms of both objective metrics and subjective visual quality.
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In this paper, a new Taylor-type differentiation formula is investigated for the first-order derivative approximation to generate higher numerical accuracy in the implementation of zeroing neural network model discretization. Then, two novel discrete-time zeroing neural network models are first developed, analyzed and verified for online solving time-varying nonlinear optimization problem. Moreover, the 0-stability, consistency and convergence reveal that the developed discrete-time zeroing neural network models converge to the theoretical solution to the time-varying nonlinear optimization problem with the residual error O(τ4), where τ signifies the sampling gap. In addition, according to the smooth solution accuracy, the proposed discrete-time zeroing neural network models have better performance than traditional computing models by adopting the Taylor-type computational differentiation formula with O(τ3) pattern. Finally, two illustrative numerical examples are further provided to demonstrate the efficiency and superiority of the proposed discrete-time zeroing neural network models for time-varying nonlinear optimization problems solving compared with the classical neural network models, furthermore, the proposed models are also applied on two-link mechanical arm with motion generation.
The impact of blockchain technology on consumer behavior: a multimethod study

Blockchain is a ground-breaking technology that is transforming supply chain management. This study aims to empirically investigate the impacts of blockchain technology on consumer behavior. We conduct this research in collaboration with a Chinese e-commerce company that has introduced a blockchain platform for tracing. We use a multimethod design by combining natural experiment- and case study methods. First, we collected data from four industries (i.e. milk powder, seafood, alcohol and nutrition) to conduct the experiment, and the findings reveal that the firms that adopted the blockchain tracing system have an increase in product sales and a decrease in product returns compared to those that did not. Second, we conducted a multiple case study with four cases from the four industries. The findings reveal that the adoption of a blockchain tracing system improves supply chain transparency and process management, which then enhances consumer service and trust. This study contributes to the literature by providing empirical evidence that adopting blockchain technology can improve firms’ marketing performance. The findings also reveal how the adoption of blockchain technology affects consumer behavior.
An extended autoencoder model for reaction coordinate discovery in rare event molecular dynamics datasets

The reaction coordinate (RC) is the principal collective variable or feature that determines the progress along an activated or reactive process. In a molecular simulation using enhanced sampling, a good description of the RC is crucial for generating sufficient statistics. Moreover, the RC provides invaluable atomistic insight into the process under study. The optimal RC is the committor, which represents the likelihood of a system to evolve toward a given state based on the coordinates of all its particles. As the interpretability of such a high dimensional function is low, a more practical approach is to describe the RC by some low-dimensional molecular collective variables or order parameters. While several methods can perform this dimensionality reduction, they usually require a preselection of these low-dimension collective variables (CVs). Here, we propose to automate this dimensionality reduction using an extended autoencoder, which maps the input (many CVs) onto a lower-dimensional latent space, which is subsequently used for the reconstruction of the input as well as the prediction of the committor function. As a consequence, the latent space is optimized for both reconstruction and committor prediction and is likely to yield the best non-linear low-dimensional representation of the committor. We test our extended autoencoder model on simple but nontrivial toy systems, as well as extensive molecular simulation data of methane hydrate nucleation. The extended autoencoder model can effectively extract the underlying mechanism of a reaction, make reliable predictions about the committor of a given configuration, and potentially even generate new paths representative for a reaction.
Emergence of In‐Materio Intelligence from an Incidental Structure of a Single‐Walled Carbon Nanotube–Porphyrin Polyoxometalate Random Network

Hardware‐based machine intelligence with the network architecture of reservoir computing (RC) is gaining interest because of its biological computational resemblance along with an easy and efficient neural network training approach. Herein, such a physical RC (in‐materio RC) platform consisting of a recurrent network formed by the single‐walled carbon nanotube (SWNT)–porphyrin polyoxometalate (Por–POM) complex is demonstrated. The network architecture executes the fundamental reservoir properties of nonlinearity, higher harmonic generation, and 1/ f γ power law information processing ability. Based on these functionalities, an RC benchmark task of waveform generation is performed where the device achieves maximum fitting accuracy of 99.4%. Furthermore, a supervised object classification task based on a one‐hot vector target is also executed using Toyota Human Support Robot tactile inputs. The successful classification of objects of different hardness is enhanced when the device output response follows the 1/ f γ power law of maximized information processing.
Using blockchain and semantic web technologies for the implementation of smart contracts between individuals and health insurance organizations

Blockchains and smart contracts are gaining momentum as enabling technologies for a wide set of applications where data distribution and sharing among decentralized infrastructures is required. In this work, we present a distributed application developed using blockchain technologies that allows individuals and health insurance organizations to come into agreement during the implementation of the healthcare insurance policies in each contract. For this purpose, health standards and semantic web technologies were used for the formal expression of both the insured individual's data and contract terms. Accordingly, a fine-grained data access policy was applied for evaluating contract terms on the basis of relevant data captured in healthcare settings. A prototype was implemented involving the development of several different smart contracts for the Ethereum platform as well as the necessary visual environment for accessing them. The developed system validates various features related to blockchain and smart contract features that are briefly discussed in this work, part of which can be mitigated or resolved through the use of a private permissioned blockchain. The application of well-established techniques for potential malfunctions of external services could also boost the security of the system and prevent it from potential attacks.
Expressive Talking Head Generation with Granular Audio-Visual Control

Generating expressive talking heads is essential for creating virtual humans. However, existing one- or few-shot methods focus on lip-sync and head motion, ignoring the emotional expressions that make talking faces realistic. In this paper, we propose the Granularly Controlled Audio-Visual Talking Heads (GC-AVT), which controls lip movements, head poses, and facial expressions of a talking head in a granular manner. Our insight is to decouple the audio-visual driving sources through prior-based pre-processing designs. Detailedly, we disassemble the driving image into three complementary parts including: 1) a cropped mouth that facilitates lip-sync; 2) a masked head that implicitly learns pose; and 3) the upper face which works corporately and complementarily with a time-shifted mouth to contribute the expression. Interestingly, the encoded features from the three sources are integrally balanced through reconstruction training. Extensive experiments show that our method generates expressive faces with not only synced mouth shapes, controllable poses, but precisely animated emotional expressions as well.
Philosophical and Methodological Foundations Post-non-classical Third-Order Cybernetics

The article presents a philosophical and methodological analysis of the evolu­tion of paradigms of cybernetics of the first, second and third-order. We used N. Bohr’s principle of correspondence, established in physical science, the ideas of scientific revolutions by T.S. Kuhn, J. Holton’s ideas about the use in new paradigms of cross-cutting thematic structures as peculiar trajectories of the historical development of science, as well as M. Polanyi’s ideas about the influence of personal knowledge of creators on the content of new scien­tific paradigms. An analysis of three examples proposed by third-order cyber­netics allowed us to conclude that they set particular directions of develop­ment, but cannot claim to be a qualitatively new step in the development of cybernetics, the transition to third-order cybernetics, which would include both private paradigms of first and second-order cybernetics. For the philo­sophical and methodological basis for the formation of new paradigms of cy­bernetics, we propose a construction of three types of scientific rationality (V.S. Stepin), which meets the requirements of the N. Bohr’s principle of cor­respondence and the criteria of T.S. Kuhn. From these positions, the article substantiates the correctness of the formation of post-non-classical cyber­netics of self-developing poly-subjective (reflexive-active) environments in which there are three stages of development of subjective paradigms, basic philosophical approaches, objects of control, control mechanisms, types of observers, activity and subjectness approaches, basic types of reflexion, ethi­cal regulators, ideas about information and knowledge, subjects and pseudo-subjectness of digital transformation and artificial intelligence, disciplinary scientific approaches. The presented logic of development gives arguments that post-non-classical cybernetics has grounds to be cybernetics of the third order.
Reversing the threat of artificial intelligence to opportunity: a discussion of ChatGPT in tourism education

The role of artificial intelligence (AI) in the education sector has received considerable attention from scholars; however, its potency and application among tourism learners are yet to be discerned. Accordingly, this research note articulates the potential impact and limitations of the AI chatbot, ChatGPT, in tourism education practices. In particular, the paper aims to raise awareness among tourism educators regarding the disruptive potential of ChatGPT and its impact on academic integrity and ethics. This paper contributes to tourism education practices on AI and robotics by critically discussing ChatGPT from a cognitive science perspective and providing recommendations on how to minimise AI plagiarism in tourism education. This research argues for adapting novel pedagogies as technology evolves and raises the need for AI ethical use guidelines in tourism education.
Adaptation Based on Generalized Discrepancy

We present a new algorithm for domain adaptation improving upon a discrepancy minimization algorithm, (DM), previously shown to outperform a number of algorithms for this problem. Unlike many previ...