Reviewerly/exHarmony huggingface dataset:
Gait2Vec: Continuous Authentication of Smartphone Users Based on Gait Behavior

Since gait is hard to imitate and can be easily collected by smartphone inertial sensors, it can be applied to user authentication. Traditional neural network based methods tend to train feature extractor and classifier together for each user in user authentication. These methods are difficult to guarantee that the feature extractor designed for specific users is suitable for other users. The accuracy is low when the sample size of a legitimate user is small, and the time overhead is heavy when the total amount of legitimate users is large. Besides, there are often strong constraints on sensor position, walking route, walking speed and external scenario when collecting gait data. In this paper, in order to reduce the cost of time and improve the robustness of the model, we use the idea of transfer learning to design our Gait2Vec feature extractor. It is pre-trained in the user identification task and then transferred to user authentication task for feature extraction. Meanwhile, a gait dataset of 21 subjects is collected under weak constraints in 2 scenarios for experimental purposes. Extensive analysis demonstrates that our models achieve a high performance with the accuracy over 94% in user identification and 97% in user authentication.
Intelligent dynamic practical-sliding-mode control for singular Markovian jump systems

This paper is concerned with the problems of dynamic practical-sliding-mode control (SMC) and estimation of unknown functions for singular Markovian jump systems (MJSs) with system perturbations, by using an ellipsoidal-type interval type-2 fuzzy neural networks (FNNs). For the interval type-2 FNN, both the system states and control inputs are utilized as its inputs. Then, by using respectively the linear and the integral sliding variables depending on both the system state and control input, the design of novel interval type-2 FNN-integrated intelligent dynamic practical SMCs to achieve practical sliding modes in finite time is presented. The resulting practical sliding motions are proved to be semi-global practical finite-time stochastic stable based on the Lyapunov function. Furthermore, by exploring some adaptive laws based on the interval type-2 FNN, the unknown function can be approximated, and even the bound of the approximation errors of the interval type-2 FNN can be estimated, under the system perturbation. Finally, an application example is introduced to illustrate the validity of the presented intelligent dynamic practical SMC techniques.
FLARE

Federated learning (FL) has been shown vulnerable to a new class of adversarial attacks, known as model poisoning attacks (MPA), where one or more malicious clients try to poison the global model by sending carefully crafted local model updates to the central parameter server. Existing defenses that have been fixated on analyzing model parameters show limited effectiveness in detecting such carefully crafted poisonous models. In this work, we propose FLARE, a robust model aggregation mechanism for FL, which is resilient against state-of-the-art MPAs. Instead of solely depending on model parameters, FLARE leverages the penultimate layer representations (PLRs) of the model for characterizing the adversarial influence on each local model update. PLRs demonstrate a better capability to differentiate malicious models from benign ones than model parameter-based solutions. We further propose a trust evaluation method that estimates a trust score for each model update based on pairwise PLR discrepancies among all model updates. Under the assumption that honest clients make up the majority, FLARE assigns a trust score to each model update in a way that those far from the benign cluster are assigned low scores. FLARE then aggregates the model updates weighted by their trust scores and finally updates the global model. Extensive experimental results demonstrate the effectiveness of FLARE in defending FL against various MPAs, including semantic backdoor attacks, trojan backdoor attacks, and untargeted attacks, and safeguarding the accuracy of FL.
Taylor genetic programming for symbolic regression

Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR) problems. Compared with the machine learning or deep learning methods that depend on the pre-defined model and the training dataset for solving SR problems, GP is more focused on finding the solution in a search space. Although GP has good performance on large-scale benchmarks, it randomly transforms individuals to search results without taking advantage of the characteristics of the dataset. So, the search process of GP is usually slow, and the final results could be unstable. To guide GP by these characteristics, we propose a new method for SR, called Taylor genetic programming (TaylorGP)1. TaylorGP leverages a Taylor polynomial to approximate the symbolic equation that fits the dataset. It also utilizes the Taylor polynomial to extract the features of the symbolic equation: low order polynomial discrimination, variable separability, boundary, monotonic, and parity. GP is enhanced by these Taylor polynomial techniques. Experiments are conducted on three kinds of benchmarks: classical SR, machine learning, and physics. The experimental results show that TaylorGP not only has higher accuracy than the nine baseline methods, but also is faster in finding stable results.
Swarm-Intelligence Optimization Method for Dynamic Optimization Problem

In recent years, the vigorous rise in computational intelligence has opened up new research ideas for solving chemical dynamic optimization problems, making the application of swarm-intelligence optimization techniques more and more widespread. However, the potential for algorithms with different performances still needs to be further investigated in this context. On this premise, this paper puts forward a universal swarm-intelligence dynamic optimization framework, which transforms the infinite-dimensional dynamic optimization problem into the finite-dimensional nonlinear programming problem through control variable parameterization. In order to improve the efficiency and accuracy of dynamic optimization, an improved version of the multi-strategy enhanced sparrow search algorithm is proposed from the application side, including good-point set initialization, hybrid algorithm strategy, Lévy flight mechanism, and Student’s t-distribution model. The resulting augmented algorithm is theoretically tested on ten benchmark functions, and compared with the whale optimization algorithm, marine predators algorithm, harris hawks optimization, social group optimization, and the basic sparrow search algorithm, statistical results verify that the improved algorithm has advantages in most tests. Finally, the six algorithms are further applied to three typical dynamic optimization problems under a universal swarm-intelligence dynamic optimization framework. The proposed algorithm achieves optimal results and has higher accuracy than methods in other references.
Texture classification-based feature processing for violence-based anomaly detection in crowded environments

Anomaly detection from video surveillance inputs helps to improve security in crowded places and outdoors. The captured image is analyzed to identify human faces, objects, and abnormal events through computer-aided analytics. This article proposes a Texture-Classification-based Feature Processing (TCFP) technique for distinguishing anomalies in captured video inputs. The anomalies are identified as events from the sequence frames wherein the dynamic inputs are distinguished using their features. Deep learning is employed for temporal training features based on frame characteristics in this distinguishing process. The input frame is segregated using textural boundaries separated using non-dimensional features. The learning process trains dimensional and non-dimensional features for identifying anomalies and maximizing detection accuracy. The textural boundaries are defined using the non-dimensional vectors present in the frame series in the different face classifications. Therefore, the errors are confined within selective boundaries without impacting the preceding feature. This improves the F1score with less processing time.
Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation

Deep graph neural networks (GNNs) have been shown to be expressive for modeling graph-structured data. Nevertheless, the overstacked architecture of deep graph models makes it difficult to deploy and rapidly test on mobile or embedded systems. To compress over-stacked GNNs, knowledge distillation via a teacher-student architecture turns out to be an effective technique, where the key step is to measure the discrepancy between teacher and student networks with predefined distance functions. However, using the same distance for graphs of various structures may be unfit, and the optimal distance formulation is hard to determine. To tackle these problems, we propose a novel Adversarial Knowledge Distillation framework for graph models named GraphAKD, which adversarially trains a discriminator and a generator to adaptively detect and decrease the discrepancy. Specifically, noticing that the well-captured inter-node and inter-class correlations favor the success of deep GNNs, we propose to criticize the inherited knowledge from node-level and class-level views with a trainable discriminator. The discriminator distinguishes between teacher knowledge and what the student inherits, while the student GNN works as a generator and aims to fool the discriminator. Experiments on nodelevel and graph-level classification benchmarks demonstrate that GraphAKD improves the student performance by a large margin. The results imply that GraphAKD can precisely transfer knowledge from a complicated teacher GNN to a compact student GNN.
Single-View View Synthesis in the Wild with Learned Adaptive Multiplane Images

This paper deals with the challenging task of synthesizing novel views for in-the-wild photographs. Existing methods have shown promising results leveraging monocular depth estimation and color inpainting with layered depth representations. However, these methods still have limited capability to handle scenes with complex 3D geometry. We propose a new method based on the multiplane image (MPI) representation. To accommodate diverse scene layouts in the wild and tackle the difficulty in producing high-dimensional MPI contents, we design a network structure that consists of two novel modules, one for plane depth adjustment and another for depth-aware color prediction. The former adjusts the initial plane positions using the RGBD context feature and an attention mechanism. Given adjusted depth values, the latter predicts the color and density for each plane separately with proper inter-plane interactions achieved via a feature masking strategy. To train our method, we construct large-scale stereo training data using only unconstrained single-view image collections by a simple yet effective warp-back strategy. The experiments on both synthetic and real datasets demonstrate that our trained model works remarkably well and achieves state-of-the-art results.
Assessing Self-Adaptation Strategies Using Cost-Benefit Analysis

Self-adaptive software systems stand out from traditional ones in that they are able to autonomously change their behavior and structure during their execution using one or more self-adaptation strategies. The main objective of such a strategy is to maintain or even improve the functionalities/qualities of the system despite uncertainty in its operational environment. To date, a number of self-adaptation strategies have been proposed – following the architectural, control-theoretic, or online search paradigm – for several application domains. However, it is still unclear when a particular self-adaptation strategy needs to be developed and when it needs to be used at runtime (when a system can choose among several available strategies). In this paper, we aim to answer the above questions by relying on the assessment of a strategy’s costs (e.g., development effort, performance decrease) and benefits (e.g., re-usability, performance improvement) at design time and runtime. The main novelty is that we provide a holistic view over the return on investment of a strategy and propose that the system itself uses cost-benefit analysis to decide on which strategy to apply at runtime.
Adaptive stochastic conjugate gradient for machine learning

Due to their faster convergence rate than gradient descent algorithms and less computational cost than second order algorithms, conjugate gradient (CG) algorithms have been widely used in machine learning. This paper considers conjugate gradient in the mini-batch setting. Concretely, we propose a stable adaptive stochastic conjugate gradient (SCG) algorithm via incorporating both the stochastic recursive gradient algorithm (SARAH) and second order information into the CG-type algorithm. Unlike most of existing CG algorithms that spend a lot of time in determining the step size by using line search and may fail in stochastic optimization, the proposed algorithms use a local quadratic model to estimate the step size sequence, but do not require computing the Hessian information, which make the proposed algorithms attain a low computational cost as first-order algorithms. We establish the linear convergence rate of a class of SCG algorithms, when the loss function is the strongly convex. Moreover, we show that the complexity of the proposed algorithm matches modern stochastic optimization algorithms. As a by-product, we develop a practical variant of the proposed algorithm by setting a stopping criterion for the number of inner loop iterations. Various numerical experiments on machine learning problems demonstrate the efficiency of the proposed algorithms.
Proof of Activity Protocol for IoMT Data Security

The Internet of Medical Things (IoMT) is an online device that senses and transmits medical data from users to physicians within a time interval.In, recent years, IoMT has rapidly grown in the medical field to provide healthcare services without physical appearance.With the use of sensors, IoMT applications are used in healthcare management.In such applications, one of the most important factors is data security, given that its transmission over the network may cause obtrusion.For data security in IoMT systems, blockchain is used due to its numerous blocks for secure data storage.In this study, Blockchain-assisted secure data management framework (BSDMF) and Proof of Activity (PoA) protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system.The main aim is to enhance the data security over the networks.The PoA protocol enhances high security of data from the literature review.By replacing the malicious node from the block, the PoA can provide high security for medical data in the blockchain.Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio, precision ratio, security, and efficiency and less response time for Blockchain-enabled healthcare systems.
Training personalized recommendation systems from (GPU) scratch

Personalized recommendation models (RecSys) are one of the most popular machine learning workload serviced by hyperscalers. A critical challenge of training RecSys is its high memory capacity requirements, reaching hundreds of GBs to TBs of model size. In RecSys, the so-called embedding layers account for the majority of memory usage so current systems employ a hybrid CPU-GPU design to have the large CPU memory store the memory hungry embedding layers. Unfortunately, training embeddings involve several memory bandwidth intensive operations which is at odds with the slow CPU memory, causing performance overheads. Prior work proposed to cache frequently accessed embeddings inside GPU memory as means to filter down the embedding layer traffic to CPU memory, but this paper observes several limitations with such cache design. In this work, we present a fundamentally different approach in designing embedding caches for RecSys. Our proposed ScratchPipe architecture utilizes unique properties of RecSys training to develop an embedding cache that not only sees the past but also the "future" cache accesses. ScratchPipe exploits such property to guarantee that the active working set of embedding layers can "always" be captured inside our proposed cache design, enabling embedding layer training to be conducted at GPU memory speed.
Ensemble Deep Learning for Human-Object Interaction Detection

Human-object interaction (HOI) detection is the task of predicting the visual relationships between humans and their surroundings in images and videos by locating and inferring the interactions between human-object pairs. The majority of existing models have approached this task by detecting human and object instances and predicting interactions between them using a single model and relying on visual features to differentiate between different actions. In this article, we propose a novel method for solving the HOI detection problem by employing ensemble voting on simple models that use only spatial features to predict human-object pair actions. Additionally, we solve the false-positive pairs generated by mis-grouping and non-interaction objects in the image using a bipartite matching. Our proposed approach outperforms many state-of-the-art models on the V-COCO dataset while requiring less inference time.
Exploring the applicability of virtual reality- enhanced education on extrovert and introvert EFL learners’ paragraph writing

During the past decade, Virtual reality (VR)-enhanced education has been adopted as a pedagogically new learning approach to smooth the learning progress. With the rise of VR-enhanced activities, investigating the effect of personality types of EFL learners on their writing performance to use VR-based instruction for learning may be a crucial factor influencing their achievement. This study was an attempt to research the impact of VR- enhanced classes on paragraph writing of extrovert and introvert English foreign language (EFL) Learners. To achieve the purpose of the study, first, the Preliminary English Test (PET) was administered for ensuring the homogeneity of the sample. Once the homogeneity was established, a total of 52 EFL intermediate students were selected and placed into two comparative and two control groups, with 13 participants in each group. Four groups took part in the study, with two groups undergoing treatment with the non-VR teaching approach of paragraph writing and two groups undergoing treatment of VR-enhanced education. Among these four groups, two consisted of introverts and two extroverts that were identified through Eysenck Personality Inventory. The two comparative groups received 12 sessions of VR-enhanced education in online classes (instead of drawing pictures and sharing them within their group, they watched the process activities in the VR environment.), but the control group received learning writing paragraphs through using instructor's instructional materials. After 6-weeks of treatment sessions, all participants of the study took the post-test. According to the results, the VR-enhanced education was more effective than the non-VR teaching approach in developing paragraph writing of both introverts and extroverts. Moreover, the results of the research showed no significant differences between the performance of the introverts and extroverts, meaning that both had the same performance. The results of this study may pave the way for teachers to use VR-based technology in online and traditional classes without worrying about how learners with different personality traits respond.
Augmenting Scientific Creativity with an Analogical Search Engine

Analogies have been central to creative problem-solving throughout the history of science and technology. As the number of scientific articles continues to increase exponentially, there is a growing opportunity for finding diverse solutions to existing problems. However, realizing this potential requires the development of a means for searching through a large corpus that goes beyond surface matches and simple keywords. Here we contribute the first end-to-end system for analogical search on scientific articles and evaluate its effectiveness with scientists’ own problems. Using a human-in-the-loop AI system as a probe we find that our system facilitates creative ideation, and that ideation success is mediated by an intermediate level of matching on the problem abstraction (i.e., high versus low). We also demonstrate a fully automated AI search engine that achieves a similar accuracy with the human-in-the-loop system. We conclude with design implications for enabling automated analogical inspiration engines to accelerate scientific innovation.
On-chain behavior prediction Machine Learning model for blockchain-based crowdsourcing

In this paper, we address the problem of behavior prediction for task allocation in blockchain-based crowdsourcing framework. Centralized crowdsourcing frameworks complement workers' reputations with predicted behavior, through Machine Learning (ML) models, to improve the task allocation performance and maintain worker engagement. Existing blockchain-based crowdsourcing frameworks allocate tasks to workers using reputation solely, which neglects the impact of a task's context on the worker's behavior. Our contribution is an on-chain behavior prediction ML model for task allocation on top of a proposed blockchain-based crowdsourcing framework. The ML model, hosted on blockchain, reflects a worker's unique behavior for a task given its context. The proposed ML model is: (1) trained off-chain since it has lower monetary cost compared to on-chain training, and (2) deployed on-chain as a smart contract to enable transparent predictions. The task allocation mechanism in the proposed blockchain-based crowdsourcing framework considers workers' predicted behavior and a Quality of Information (QoI) metric that includes distance to the task, completion time, and workers' reputation. The evaluation conducted confirms that the proposed task allocation mechanism, implemented using Solidity, outperforms the benchmark in terms of percentage of allocation, workers' QoI, and reputation change.
A Study of Mobile App Use for Teaching and Research in Higher Education

Abstract The exponential growth in the use of digital technologies and the availability of mobile software applications (apps) has been well documented over the past decade. Literature on the integration of mobile technology into higher education reveals an increasing focus on how mobile devices are used within the classroom environment, both physical and online, rather than on how mobile applications may be used for either teaching or the research process. Our study surveyed staff and higher degree research students at a New Zealand university using an online questionnaire to gain insight into the use of mobile apps for tertiary teaching and research, seeking information, particularly on which apps were used for which tasks and what obstacles hindered their use. The online survey used 29 questions and ran in 2016/2017. 269 participants completed the survey, nearly 20% of the potential sample. We found that mobile apps were used by academics and students for both teaching and research, primarily in the form of document and data storage and exchange, and communication. Very little app use was recorded for in-class activities (teaching) or in-field activities (research). Apps use resulted from personal motivation rather than institutional planning. Both students and academics reported that institutional support and flexibility would likely provide motivation and lead to increased app use for both research and teaching.