Reviewerly/exHarmony huggingface dataset:
SurgeNAS: A Comprehensive Surgery on Hardware-Aware Differentiable Neural Architecture Search

Differentiable neural architecture search (NAS) is an emerging paradigm to automate the design of top-performing convolutional neural networks (CNNs). Nonetheless, existing differentiable NAS methods suffer from several crucial weaknesses, such as inaccurate gradient estimation, high memory consumption, search fairness, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . In this work, we introduce a novel hardware-aware differentiable NAS framework, namely SurgeNAS, in which we leverage the one-level optimization to avoid inaccuracy in gradient estimation. To this end, we propose an effective identity mapping regularization to alleviate the over-selecting issue. Besides, to mitigate the memory bottleneck, we propose an ordered differentiable sampling approach, which significantly reduces the search memory consumption to the single-path level, thereby allowing to directly search on target tasks instead of small proxy tasks. Meanwhile, it guarantees the strict search fairness. Moreover, we introduce a graph neural networks (GNNs) based predictor to approximate the on-device latency, which is further integrated into SurgeNAS to enable the latency-aware architecture search. Finally, we analyze the resource underutilization issue, in which we propose to scale up the searched SurgeNets within <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Comfort Zone</i> to balance the computation and memory access, which brings considerable accuracy improvement without deteriorating the execution efficiency. Extensive experiments are conducted on ImageNet with diverse hardware platforms, which clearly show the effectiveness of SurgeNAS in terms of accuracy, latency, and search efficiency.
Sending-or-Not-Sending Twin-Field Quantum Key Distribution With Measurement Imperfections

Twin-field quantum key distribution (TF-QKD) protocol can drastically increase the secure transmission distance of quantum key distribution, changing the channel-loss dependence of key rate from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${O} (\eta)$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${O} (\sqrt {\eta })$ </tex-math></inline-formula> . To date, many revolutionary protocols have been invented to increase the practical performances of TF-QKD. However, most of them simply assume using symmetric detectors and ideal beam-splitters, which is inconsistent with the actual experiment. Here, we take experimental imperfections of the detection part in TF-QKD into account, for example, the two detectors own different detection efficiencies, different dark count rates, and the reflectivity of the beam-splitter is unbalanced. We employ the sending-or-not-sending twin-field quantum key distribution (SNS TF-QKD) protocol as an example, constructing a more systematic model to analyze its practical performance. Therefore, our work provides an useful reference for the practical implementation of TF-QKD.
Verifiable Outsourced Decryption of Encrypted Data From Heterogeneous Trust Networks

Cloud-based Internet of Things (IoT) management services can be utilized to acquire data from devices at any point on Earth. Accordingly, controlling access to data managed by possibly untrusted cloud servers is crucial. Attribute-based encryption (ABE) provides flexible access control and the capability to delegate, facilitating decryption operations with high computationally costs to be outsourced to cloud servers. Earlier studies discussed guarantees to the accuracy of delegated computation through various cryptographic encoding techniques, thus helping data receivers verify the precision of outsourced decryption operations. In this article, we investigate two state-of-the-art schemes addressing verifiable outsourced decryption of encrypted data, and show their vulnerability to our verification bypassing attacks. We then propose a securitywise enhanced encoding scheme that disables such attacks. In addition, a rigorous security analysis is conducted, demonstrating the capabilities of the proposed scheme against bypassing attacks. An experimental analysis finds that the method proposed in this article outperforms the two state-of-the-art works by 82% and 87%, respectively, on the encoding computation cost.
Comparison of Moderated and Unmoderated Remote Usability Sessions for Web-Based Simulation Software: A Randomized Controlled Trial

Usability studies are a crucial part of developing user-centered designs and they can be conducted using a variety of different methods. Unmoderated usability surveys are more efficient and cost-effective and lend themselves better to larger participant pools in comparison to moderated usability surveys. However, unmoderated usability surveys could increase the collection of unreliable data due to the survey participants' careless responding (CR). In this study, we compared the remote moderated and remote unmoderated usability testing sessions for a web-based simulation and modeling software. The usability study was conducted with 72 participants who were randomly assigned into a moderated and unmoderated groups. Our results show that moderated sessions produced more reliable data in most of the tested outcomes and that the data from unmoderated sessions needed some optimization in order to filter out unreliable data. We discuss methods to isolate unreliable data and recommend ways of managing it.
DeepCode: An Annotated Set of Instructional Code Examples to Foster Deep Code Comprehension and Learning

We present here a novel instructional resource, called DeepCode, to support deep code comprehension and learning in intro-to-programming courses (CS1 and CS2). DeepCode is a set of instructional code examples which we call a codeset and which was annotated by our team with comments (e.g., explaining the logical steps of the underlying problem being solved) and related instructional questions that can play the role of hints meant to help learners think about and articulate explanations of the code. While DeepCode was designed primarily to serve our larger efforts of developing an intelligent tutoring system (ITS) that fosters the monitoring, assessment, and development of code comprehension skills for students learning to program, the codeset can be used for other purposes such as assessment, problem-solving, and in various other learning activities such as studying worked-out code examples with explanations and code visualizations. We present here the underlying principles, theories, and frameworks behind our design process, the annotation guidelines, and summarize the resulting codeset of 98 annotated Java code examples which include 7,157 lines of code (including comments), 260 logical steps, 260 logical step details, 408 statement level comments, and 590 scaffolding questions.
A Systematic Review on the Influence of Virtual Reality on Language Learning Outcomes

With the rapid development of information technologies, virtual reality-assisted language learning (VRALL) has caught much attention across the world, particularly during the COVID-19 pandemic. Nevertheless, few review studies aimed to understand the influence of virtual reality (VR) on learning outcomes in terms of each language element. This literature review study selected and analyzed 23 studies from the perspective of language learning aspects, main users of VR technology, and the impact of VR on each learning aspect. The results revealed that (1) vocabulary learning and tertiary education gained the most attention in academia; (2) VR promoted pronunciation learning, listening skills, and speaking skills; (3) VR was a mixed blessing for vocabulary learning, reading skills, and writing skills. Future research could explore the solutions to improve the effectiveness of VR and highlight interdisciplinary cooperation.
Advertising in Virtual Reality: A Hierarchy of Effects Paradigm

Good adverts (ads) attract consumers and create a lasting and positive impression about a brand. Television has for years been the medium of choice for ads. Now, new digital innovations such as virtual reality (VR), a more immersive and engaging 3D digital representation of reality, allow for a first-person interactivity that television lacks, leading to a greater feeling of non-mediated “presence”. Advertisers can take advantage of VR’s unique characteristics to grow brand affinity and increase purchase. The Hierarchy of Effects model has traditionally been used to understand the decision-making journey of the consumer, from product awareness to purchase. Few attempts have been made to apply this model to VR. We suggest that the application of the Hierarchy of Effects model to VR ads can provide unique insights into the consumer’s process of thinking, feeling, and conation, towards brand affinity. The insights obtained should lead to more effective advertising strategies that would assist in predicting consumer behaviour. We offer suggestions with relevance for the global and African contexts.
Design and Implementation of an Immersive Network Collaborative Environment Using OpenPose with 360VR Camera and WebXR

Recently, VR goggles like Oculus Quest 2 have become popularized because such goggles are more portable and cheaper than ever. As a result, Metaverse to be realized using such goggles will become popular as one of the immersive network collaborative environments. So, in this paper, the authors propose the design and implementation for that. For multiple persons’ network collaborations, the functionality to capture their actions simultaneously is required. So, the authors propose the use of OpenPose with 360VR camera in their design because OpenPose generates multiple persons’ pose information from a video almost in real-time and 360VR camera generates a video that covers a wide area including multiple persons. On the other hand, such immersive network collaborative environments are very complicated and their development is not easy. So, the authors also propose the use of WebXR of Three.js in their design because Three.js is one of the popular 3D graphics libraries for web contents and it provides functionality required for WebXR that supports VR goggles. For justifying the proposed design, the authors implemented a prototype system of an immersive network collaborative environment. This paper explains the proposed design and the details of the prototype system.
Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches

Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time of a job gives prioritized job scheduling based on the shortest job first, which reduces the average waiting time of job execution. As a result, the proposed data retrieval approach outperforms the previous method using a deep autoencoder and Solr indexing, significantly improving the speed of data retrieval up to 53% and increasing system throughput by 53%. On the other hand, the proposed job scheduling algorithm defeats both first-in-first-out and memory-sensitive heterogeneous early finish time scheduling algorithms, effectively shortening the average waiting time up to 5% and average weighted turnaround time by 19%, respectively.
A Deep Learning Approach for Splicing Detection in Digital Audios

The authenticity of digital audios has a crucial role when presented as evidence in the court of law or forensic investigations. Fake or doctored audios are commonly used for manipulation of facts and causing false implications. To facilitate passive-blind detection of forgery, the current paper presents a deep learning approach for detecting splicing in digital audios. It aims to eliminate the process of feature extraction from the digital audios by taking the deep learning route to expose forgery. A customized dataset of 4200 spliced audios is created for the purpose, using the publicly available Free Spoken Digit Dataset (FSDD). Unlike the other related approaches, the splicing is carried out at a random location in the audio clip that spans 1–3 s. Spectrograms corresponding to audios are used to train a deep convolutional neural network that classifies the audios as original or forged. Experimental results show that the model can classify the audios correctly with 93.05% classification accuracy. Moreover, the proposed deep learning approach also overcomes the drawbacks of feature engineering and reduces manual intervention significantly.
Event-related microblog retrieval in Turkish

Microblogs, such as tweets, are short messages in which users are able to share any opinion and information.Microblogs are mostly related to real-life events reported in news articles.Finding event-related microblogs is important to analyze online social networks and understand public opinion on events.However, finding such microblogs is a challenging task due to the dynamic nature of microblogs and their limited length.In this study, assuming that news articles are given as queries and microblogs as documents, we find event-related microblogs in Turkish.In order to represent news articles and microblogs, we examine encoding methods, namely traditional bag-of-words and word embeddings provided by BERT and FastText pretrained language models based on deep learning.We find the distance between the encoded news article and microblog to measure text similarity or relatedness between them.We then rank microblogs according to their relatedness to the input query.The experimental results show that (i) BERT-based model outperforms other encoding methods in Turkish, though bag-of-words with Dice similarity has a challenging performance in short text; (ii) news title is successful to represent event as query, and (iii) preprocessing Turkish microblogs has positive impact in bag-of-words and also FastText embeddings, while BERT embeddings are robust to noise in Turkish.
1-Perfect Codes Over the Quad-Cube

A vertex subset <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> of a graph <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> constitutes a 1-perfect code if the one-balls centered at the nodes in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> effect a vertex partition of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> . This paper considers the quad-cube <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CQ_{m}$ </tex-math></inline-formula> that is a connected <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(m+2)$ </tex-math></inline-formula> -regular spanning subgraph of the hypercube <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q_{4m+2}$ </tex-math></inline-formula> , and shows that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CQ_{m}$ </tex-math></inline-formula> admits a vertex partition into 1-perfect codes iff <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m=2^{k}-3$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k\ge 2$ </tex-math></inline-formula> . The scheme for that purpose makes use of a procedure by Jha and Slutzki that constructs Hamming codes using a Latin square. The result closely parallels the existence of a 1-perfect code over the dual-cube, which is another derivative of the hypercube.
Generative Facial Prior and Semantic Guidance for Iterative Face Inpainting

Image inpainting techniques have been greatly improved by relying on structure and texture priors. However, damaged original images or rough predictions cannot provide sufficient texture information and accurate structural priors, leading to a drop in image quality. Moreover, from the perspective of human eye perception, it is important to pay attention to facial symmetry and facial attribute consistency. In this paper, we present a face inpainting system with iteration structure, which uses generative facial priors contained in pretrained GANs and predicted semantic information for guidance. Specifically, generative facial priors generated by the GAN inversion techniques introduce sufficient textures and features to assist inpainting; semantic maps are able to provide facial structural information and semantic categories of different pixels for face reconstruction. In particular, we iteratively refine images multiple times, with the semantic map updated in each iteration. The Weighted Prior-Guidance Modulation layer (WPGM) is devised for incorporating priors into networks through spatial modulation. We also propose facial feature self-symmetry loss to restrict the symmetry of faces in feature space. Experiments on CelebA-HQ and LaPa datasets demonstrate the superiority of our model for facial detail and attributes consistency. Meanwhile, under the background of COVID-19, it is worth trying recognition via inpainting to deal with recognition challenges brought by mask occlusion. Relevant experiments show that our inpainting model does help to recognition tasks to a certain degree, with higher accuracy.
Genetic Programming for Ensemble Learning in Face Recognition

Ensemble learning has recently been explored to achieve a better generalization ability than a single base learner through combining results of multiple base learners. Genetic programming (GP) can be used to design ensemble learning via different strategies. However, the challenge remains to automatically design an ensemble learning model due to complex search space. In this paper, we propose a new automated ensemble learning framework, based on GP for face recognition, called Evolving Genetic Programming Ensemble Learning (EGPEL). This method integrates feature extraction, base learner selection, and learner hyperparameter optimization, into several program trees. To this end, multiple program trees, a base learner set, and a hyperparameter set are developed in EGPEL. Meanwhile, an evolutionary approach to results integration is proposed. The performance of EGPEL is verified on face benchmark datasets of difficulty and compared with a large number of commonly used peer competitors, including state-of-the-art competitors. The results show that EGPEL performs better than most competitive ensemble learning methods.
Genetic Programming (GP): An Introduction and Practical Application

The focus of this chapter is some general knowledge and application of Genetic Programming (GP) especially in water and environmental science. A brief introduction and literature review of the GP and Genetic Algorithm (GA) are presented. Then the natural process, the basic GP algorithm iteration procedure, and the computational steps of GP algorithm are detailed. Moreover, several main steps of problem-solving in GP process explained. Finally, a pseudo code of GP algorithm is also stated to demonstrate the implementation of this technique.
Improved MM-MADRL Algorithm for Automatic Tuning of Multiparameter Control Systems

Control systems are widely used in our lives, and good control can be achieved by obtaining the optimal tuning parameters of the control system. The number of parameters that need to be adjusted for different control systems varies. With an increase in tuning parameters, the difficulty of tuning grows. Therefore, this paper proposes an improved monkey multiagent DRL (IMM-MADRL) algorithm and selects 3 test functions to test the setting environment of 2-7 parameters. Thus, these parameters are adjusted. The IMM-MADRL algorithm is based on the modified monkey-multiagent DRL (MM-MADRL) algorithm, and its initialization method, position update method and somersault operation are further improved so that it can perform good parameter tuning for a control system with many parameters. The simulation part of this paper proves the advantage of the IMM-MADRL algorithm in a multiparameter control system.
Search Engine Optimization

This chapter discusses how an organization can get its website, blog, or other social media pages higher on the (natural or organic) results page of search engines. This topic is highly relevant as Internet users are more likely to visit search results that are at the top of a results page. In order to decide which web pages appear higher on a results page, search engines typically evaluate which web pages are more relevant after applying an indexation mechanism. Such an indexation mechanism will rank web pages by means of so-called spiders. In response, web page owners can try to obtain higher ranks in search engines by applying search engine optimization (SEO) for internal improvements (e.g., on-page or on-site) and external improvements (e.g., link related). Alternatively, the importance of user experience optimization is increasing. This chapter offers the reader some tips and tricks to apply SEO and to evaluate which websites, blogs, or social media pages are more SEO friendly than others are.
Toward Concept Realization of Digital Health Technologies

With the exponential growth of technology in the past decade, the healthcare industry is entering a new digital paradigm. The healthcare industry is uniquely structured, hence, it is crucial that it be enriched with well-designed and prudently structured technology solutions. Due to the unique and demanding nature of its end-users and its coherently integrated stakeholders, traditional platform-centric systems are of limited effectiveness. This chapter proposes an innovative patient-centric system designed to fulfill the needs of multiple stakeholders while providing better access to quality data in real time to add value. The patient of the future digital health era is also envisaged. The chapter presents the concept of future healthcare-supported patients, aided by innovative Internet of Things (IoT)-based technologies. A prototype IoT-based smart garment is presented for elderly patients in at-home care as an example application. This system monitors and assists elderly people to live independently in their own homes. Such uniquely designed technology solutions, driven by a large number of motivators and facilitators, will inevitably be required to overcome various barriers. Particularly in the journey from idea generation to concept realization, the technology development process must navigate key challenges. This chapter discusses such challenges in the innovation life cycle based on the technology readiness levels model to derive recommendations.