Reviewerly/exHarmony huggingface dataset:
Adversarial robustness assessment: Why in evaluation both L0 and L∞ attacks are necessary

There are different types of adversarial attacks and defences for machine learning algorithms which makes assessing the robustness of an algorithm a daunting task. Moreover, there is an intrinsic bias in these adversarial attacks and defences to make matters worse. Here, we organise the problems faced: a) Model Dependence, b) Insufficient Evaluation, c) False Adversarial Samples, and d) Perturbation Dependent Results. Based on this, we propose a model agnostic adversarial robustness assessment method based on L0 and L∞ distance-based norms and the concept of robustness levels to tackle the problems. We validate our robustness assessment on several neural network architectures (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) and adversarial defences for image classification problem. The proposed robustness assessment reveals that the robustness may vary significantly depending on the metric used (i.e., L0 or L∞). Hence, the duality should be taken into account for a correct evaluation. Moreover, a mathematical derivation and a counter-example suggest that L1 and L2 metrics alone are not sufficient to avoid spurious adversarial samples. Interestingly, the threshold attack of the proposed assessment is a novel L∞ black-box adversarial method which requires even more minor perturbation than the One-Pixel Attack (only 12% of One-Pixel Attack's amount of perturbation) to achieve similar results. We further show that all current networks and defences are vulnerable at all levels of robustness, suggesting that current networks and defences are only effective against a few attacks keeping the models vulnerable to different types of attacks.
My Eyes Hurt: Effects of Jitter in 3D Gaze Tracking

Jitter, small fluctuations in the signal, is one of the major sources for a decrease in motor performance and a negative user experience in virtual reality (VR) systems. Current technologies still cannot eliminate jitter in VR systems, especially in the eye-gaze tracking systems embedded in many head-mounted displays. In this work, we used an HTC Vive Pro Eye, artificially added 0.5&#x00B0;, 1&#x00B0;, and 1.5&#x00B0; jitter to the eye-tracking data, and analyzed user performance in an ISO 9241: 411 pointing task with targets at 1 or 2 meters visual distance using angular Fitts&#x0027; law. The results showed that the user&#x0027;s error rate significantly increases with increased jitter levels. No significant difference was observed for time and throughput. Additionally, we observed a significant decrease in performance in terms of time, error rate, and accuracy for the more distant targets. We hope that our results guide researchers, practitioners, and developers towards better gaze-tracking-based VR applications.
Elite Directed Particle Swarm Optimization with Historical Information for High-Dimensional Problems

High-dimensional optimization problems are ubiquitous in every field nowadays, which seriously challenge the optimization ability of existing optimizers. To solve this kind of optimization problems effectively, this paper proposes an elite-directed particle swarm optimization (EDPSO) with historical information to explore and exploit the high-dimensional solution space efficiently. Specifically, in EDPSO, the swarm is first separated into two exclusive sets based on the Pareto principle (80-20 rule), namely the elite set containing the top best 20% of particles and the non-elite set consisting of the remaining 80% of particles. Then, the non-elite set is further separated into two layers with the same size from the best to the worst. As a result, the swarm is divided into three layers. Subsequently, particles in the third layer learn from those in the first two layers, while particles in the second layer learn from those in the first layer, on the condition that particles in the first layer remain unchanged. In this way, the learning effectiveness and the learning diversity of particles could be largely promoted. To further enhance the learning diversity of particles, we maintain an additional archive to store obsolete elites, and use the predominant elites in the archive along with particles in the first two layers to direct the update of particles in the third layer. With these two mechanisms, the proposed EDPSO is expected to compromise search intensification and diversification well at the swarm level and the particle level, to explore and exploit the solution space. Extensive experiments are conducted on the widely used CEC’2010 and CEC’2013 high-dimensional benchmark problem sets to validate the effectiveness of the proposed EDPSO. Compared with several state-of-the-art large-scale algorithms, EDPSO is demonstrated to achieve highly competitive or even much better performance in tackling high-dimensional problems.
Augmented and virtual reality in apparel industry: a bibliometric review and future research agenda

Purpose The purpose of the present study is to synthesize the extant literature on augmented reality and virtual reality in the apparel industry using bibliometric and network visualization techniques. This paper also highlights the existing gaps in the literature and sets out the future research trajectory. Design/methodology/approach This study investigated research articles in the domain of augmented and virtual reality in the apparel industry to assess global trends in research production in this area, and top contributors to research by way of authors, journals, countries and institutions. The study carried out an analysis of 239 research articles from the Scopus database during the period 1995 to 2021. The study used open-source bibliometric tools such as Biblioshiny and VOSviewer to analyze the research literature over the search period and also identify emerging research avenues. Findings The bibliometric analysis reveals that there is significant interest in this research domain. A total of 673 authors contributed to the 239 research articles analyzed and the number of multi-author documents exceeded those by single authors. Research in this domain is led by China with the maximum number of articles in the data set followed by the USA and France. However, the USA has received the highest number of citations. Donghua University from China is the largest contributor to research in this domain with 13 articles in the data set. The keyword co-occurrence analysis indicates that “virtual reality” has the most number of co-occurrences and linkages with other keywords. Other important keywords include “augmented reality,” “virtual try-on” and “cloth simulation.” The network visualization exercise also revealed significant collaboration between different countries in this research domain. Practical implications The gaps highlighted in this study will act as a reference point for researchers to conduct future studies in the field of augmented and virtual reality in apparel industry. Practitioners will also gain a comprehensive understanding of this research domain. Originality/value This study, to the best of the authors’ knowledge, is the first attempt to integrate the disjoint literature of augmented and virtual reality in apparel industry through a mapping of the intellectual structure of this research domain. The study also contributes by way of providing a snapshot of future research avenues in the knowledge domain of augmented and virtual reality in the apparel industry.
Parallel Ping-Pong: Exploring Parallel Embodiment through Multiple Bodies by a Single User

We propose Parallel Ping-Ping, a system that realizes "Parallel Embodiment", the experience of a single user controlling multiple bodies simultaneously. The user plays ping-pong by controlling 2 robot arms using a Virtual Reality (VR) controller, while looking at 2 tables through a Head Mounted Display (HMD). The computer constantly calculates the ball trajectory and the motion for the robot arm to hit the balls back automatically through color cameras. Based on this calculation, the system integrates the motion of the user's controller, maintaining the sense of agency of the robot arms even though single user plays ping-pong with two opponents. In addition, the user's view through the HMD is automatically switched to the appropriate table according to the calculated position and direction, so that the user can perceive both tables' situation smoothly. We exhibited a three-day Parallel Ping-Pong demonstration and surveyed 142 participants about their perception while controlling multiple bodies. In this paper, we introduce the Parallel Ping-Pong system framework and the insights from the survey of demonstration. We also discuss the design implication for Parallel Embodiment based on these results.
Double structure scaled simplex representation for multi-view subspace clustering

In the era of big data, there are an increasing number of multisource information data, and multi-view clustering (MVC) algorithms have developed rapidly. However, the affinity matrix learned by most MVC methods is not clean and precise enough and cannot describe the latent structure of multi-view data accurately, which results in poor clustering performance. In this paper, we propose a novel Double Structure Scaled Simplex Representation (DSSSR) method for MVC. Initially, we concatenate the multi-view data into a joint representation. Then, we use the scaled simplex representation (SSR) method on the concatenated data to obtain the affinity matrix. However, the affinity matrix is not clean and precise. Therefore, we use the SSR method again on the obtained affinity matrix to obtain a more accurate and clean affinity matrix. Furthermore, the two-step SSR is integrated into a unified optimization framework, a clean and accurate affinity matrix can be obtained, and the sum of each column vector of the affinity matrix is constrained to be nonnegative and equal to s (0<s<1), which can be adjusted to obtain the best clustering performance. Finally, an efficient optimization algorithm based on the augmented Lagrangian method (ALM) for solving the objective function is also designed. The experimental results on some datasets show that this algorithm has better clustering performance than some state-of-the-art algorithms.
Aligned visual semantic scene graph for image captioning

Image captioning is a multi-modal task to describe an image into natural language. Many state-of-the-art methods generally take the encoder–decoder architecture, encode an image by the convolution neural networks, or by the structured semantic scene graph that contains the object, relationship and the attribute information. The image scene graph constructed by the existing scene graph generation models are generally too noisy. To alleviate the phenomenon, we propose a multi-level cross-modal alignment (MCA) module to align the image scene graph with the sentence scene graph at different level. MCA can distill the redundant information of the image scene graph according to the sentence scene graph, and providing the commonsense knowledge for the decoder. Except for the semantic relationships, we take advantage of the bounding boxes with the visual objects to compute the implicit spatial relationships for the detected objects. With the aligned scene graph features and the implicit spatial relationship information, our decoder fused them via the dynamic mixtured attention to translate these features into descriptions. Extensive experiments on the MSCOCO dataset got the promising result compared with the state-of-the-art methods, which verified the effectiveness of our method.
A three-echelon based sustainable supply chain scheduling decision-making framework under the blockchain environment

The Supply Chain Scheduling (SCS) decision making is a challenging task under the blockchain environment because the supply chain is composed of multi-players in all layers. To utilise the information sharing by multiplayers with equal rights in all layers to maximise the supply chain’s utility for reducing the carbon emission, this study proposed a three-echelon supply chain integrated scheduling model that considers the production capacity and multi-product with different delivery time factors. The objective is to minimise the total cost incurred in production and transportation under the blockchain environment. To cope with the complexity arising from multiplayers with equal rights in all layers, a metaheuristic based sequential brain storm optimisation (SBSO) algorithm with a novel encoding scheme and the hybrid crossover and mutation strategy is proposed to enhance the performance. A case study comparing the proposed decision-making framework with the artificial bee colony algorithm (ABC) and the backtracking search algorithm (BSA) is conducted, and results show the superiority of the proposed framework. With the help of the proposed SCS decision-making framework, not only can the SCS decision be made, but also the low efficient nodes within the supply chain under the blockchain environment can be identified for potential sustainable upgrading.
Accelerating attention through gradient-based learned runtime pruning

Self-attention is a key enabler of state-of-art accuracy for various transformer-based Natural Language Processing models. This attention mechanism calculates a correlation score for each word with respect to the other words in a sentence. Commonly, only a small subset of words highly correlates with the word under attention, which is only determined at runtime. As such, a significant amount of computation is inconsequential due to low attention scores and can potentially be pruned. The main challenge is finding the threshold for the scores below which subsequent computation will be inconsequential. Although such a threshold is discrete, this paper formulates its search through a soft differentiable regularizer integrated into the loss function of the training. This formulation piggy backs on the back-propagation training to analytically co-optimize the threshold and the weights simultaneously, striking a formally optimal balance between accuracy and computation pruning. To best utilize this mathematical innovation, we devise a bit-serial architecture, dubbed LeOPArd, for transformer language models with bit-level early termination microarchitectural mechanism. We evaluate our design across 43 back-end tasks for MemN2N, BERT, ALBERT, GPT-2, and Vision transformer models. Post-layout results show that, on average, LeOPArd yields 1.9×and 3.9×speedup and energy reduction, respectively, while keeping the average accuracy virtually intact (< 0.2% degradation).
Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset

The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing.The National COVID Cohort Collaborative (N3C) table of laboratory measurement data-over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test.Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors' records lacked units).The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference.The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.
Query-efficient decision-based attack via sampling distribution reshaping

With a limited query budget and only the final decision of a target model, how to find adversarial examples with low-magnitude distortion has attracted great attention among researchers. Recent solutions to this issue made use of the estimated normal vector at a boundary data point to search for adversarial examples. However, since the sampling independence between two sampling epochs, they still suffer from a prohibitively high query budget, which will get worse when the dimensionality of the attacked samples get increased. To push for further development, in this paper, we pay attention to a query-efficient method to estimate the normal vector for decision-based attack in high-dimensional space. Specifically, we propose a simple yet effective normal vector estimation framework for high-dimension decision-based attack via Sampling Distribution Reshaping, dubbed SDR. Next, SDR is incorporated into general geometric attack framework. Briefly, SDR leverages all the historically sampled noise to build a guiding vector, which will be used to reshape the next sampling distribution. Besides, we also extend SDR to different ℓp norms for p={2,∞} and deploy low-frequency constraint to enhance the performance of SDR. Compared to peer decision-based attacks, SDR can reach the competitive ℓp norms for p={2,∞}, according to extensive experimental evaluations against both defended and undefended classifiers. Since the simplicity and effectiveness of SDR, we think that reshaping the sampling distribution deserves further research in future works.
Android malware concept drift using system calls: Detection, characterization and challenges

The majority of Android malware detection solutions have focused on the achievement of high performance in old and short snapshots of historical data, which makes them prone to lack the generalization and adaptation capabilities needed to discriminate effectively new malware trends in an extended time span. These approaches analyze the phenomenon from a stationary point of view, neglecting malware evolution and its degenerative impact on detection models as new data emerge, the so-called concept drift. This research proposes a novel method to detect and effectively address concept drift in Android malware detection and demonstrates the results in a seven-year-long data set. The proposed solution manages to keep high-performance metrics over a long period of time and minimizes model retraining efforts by using data sets belonging to short periods. Different timestamps are evaluated in the experimental setup and their impact on the detection performance is compared. Additionally, the characterization of concept drift in Android malware is performed by leveraging the inner workings of the proposed solution. In this regard, the discriminatory properties of the important features are analyzed at various time horizons.
Geometric quantum speed limits for Markovian dynamics in open quantum systems

Abstract We study theoretically the geometric quantum speed limits (QSLs) of open quantum systems under Markovian dynamical evolution. Three types of QSL time bounds are introduced based on the geometric inequality associated with the dynamical evolution from an initial state to a final state. By illustrating three types of QSL bounds at the cases of presence or absence of system driving, we demonstrate that the unitary part, dominated by system Hamiltonian, supplies the internal motivation for a Markovian evolution which deviates from its geodesic. Specifically, in the case of unsaturated QSL bounds, the parameters of the system Hamiltonian serve as the eigen-frequency of the oscillations of geodesic distance in the time domain and, on the other hand, drive a further evolution of an open quantum system in a given time period due to its significant contribution in dynamical speedup. We present physical pictures of both saturated and unsaturated QSLs of Markovian dynamics by means of the dynamical evolution trajectories in the Bloch sphere which demonstrates the significant role of system Hamiltonian even in the case of initial mixed states. It is further indicated that whether the QSL bound is saturated is ruled by the commutator between the Hamiltonian and reduced density matrix of the quantum system. Our study provides a detailed description of QSL times and reveals the effects of system Hamiltonian on the unsaturation of QSL bounds under Markovian evolution.
Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network

Mammography screening is one of the common techniques that help identify suspicious masses' malignancy of breast cancer at an early stage. Yet, the early diagnosis of masses in mammograms is still challenging for dense and extremely dense breast categories and requires efficient and automated systems to assist radiologists in their diagnosis. Deep learning methods have widely been used for medical imaging applications, especially for breast masses classification. A novel capsule concept has recently been proposed to solve the drawbacks of deep learning models based on classic convolutional networks by learning the hierarchical structures in images. However, the originally proposed capsule network suffers from issues due to high number of parameters and high computational time. This paper proposes a new architecture of the capsule network that significantly reduces the computational time of the original capsule network by 6.5 times and makes the training of breast masses ROIs on affordable GPUs possible. The proposed architecture was fine-tuned according to the number of kernels and capsules and by using data augmentation. The results of the evaluation of the four breast density categories show that our capsule-based model outperforms existing techniques in classifying suspicious breast masses in one stage. The binary classification of masses into normal and abnormal achieves an accuracy of 96.03%, a F1-score of 96.27%, a precision of 94.28%, a recall of 98.38%, a specificity of 93.97%, and an AUC of 0.997. The multi-classification of breast masses into normal, benign, and malignant scores an accuracy of 77.78%, a F1-score of 77.45%, a precision of 71.54%, a recall of 84.54%, a specificity of 83.15%, and an AUC of 0.9.
Scaled agile framework. Dealing with software process‐related challenges of a financial group with the action research approach

Abstract This article reports on a domain‐specific software development venture at Nordea. We explore organizational constraints, challenges, and corrective actions undertaken when scaling the agile development approach of their Core Banking Platform program. The fit and required customizations of the rather complex and rigid SAFe framework in a policy‐heavy financial institution are audited against the organic growth of the program. In a qualitative study with three cycles using the Action Research method, both organizational and technical restrictions were tackled jointly with the company employees. Thirty‐four potential solutions were developed—some of which turned out to be dead‐ends. Ultimately, the effectiveness of prior recommendations was tested in a highly challenging implementation environment, and the list of guidelines was extended. We discovered several meeting and collaboration schemes that are not fully aligned with previous reports.