Reviewerly/exHarmony huggingface dataset:
Black-box adversarial attacks using evolution strategies

In the last decade, deep neural networks have proven to be very powerful in computer vision tasks, starting a revolution in the computer vision and machine learning fields. However, deep neural networks, usually, are not robust to perturbations of the input data. In fact, several studies showed that slightly changing the content of the images can cause a dramatic decrease in the accuracy of the attacked neural network. Several methods able to generate adversarial samples make use of gradients, which usually are not available to an attacker in real-world scenarios. As opposed to this class of attacks, another class of adversarial attacks, called black-box adversarial attacks, emerged, which does not make use of information on the gradients, being more suitable for real-world attack scenarios. In this work, we compare three well-known evolution strategies on the generation of black-box adversarial attacks for image classification tasks. While our results show that the attacked neural networks can be, in most cases, easily fooled by all the algorithms under comparison, they also show that some black-box optimization algorithms may be better in "harder" setups, both in terms of attack success rate and efficiency (i.e., number of queries).
Entanglement-assisted quantum error-correcting codes from RS codes and BCH codes with extension degree 2

Entanglement-assisted quantum error-correcting codes (EAQECCs) constructed from Reed–Solomon codes and BCH codes are considered in this work. It is provided a complete and explicit formula for the parameters of EAQECCs coming from any Reed–Solomon code, for the Hermitian metric, and from any BCH code with extension degree 2 and consecutive cyclotomic cosets, for both the Euclidean and the Hermitian metric. The main task in this work is the computation of a completely general formula for c, the minimum number of required maximally entangled quantum states.
One-Dimensional Lazy Quantum Walk in Ternary System

Quantum walks play an important role for developing quantum algorithms and quantum simulations. Here, we introduce a first of its kind one-dimensional lazy quantum walk in the ternary quantum domain and show its equivalence for circuit realization in ternary quantum logic. Using an appropriate logical mapping of the position space on which a walker evolves onto the multiqutrit states, we present efficient quantum circuits for the implementation of lazy quantum walks in one-dimensional position space in ternary quantum system. We also address scalability in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -qutrit ternary system with example circuits for a three-qutrit state space.
E-commerce website service quality and customer loyalty using WebQual 4.0 with importance performances analysis, and structural equation model: An empirical study in Shopee

The rapid growth of business models in the form of online shops, Business to Consumer (B2C) and the Consumer to Consumer (C2C) concept, has an impact in the form of intense competition between online business owners. It is not easy to find a solution to this problem. The owner must provide good or even excellent service by improving the website quality so that customers can achieve satisfaction and loyalty. This research aimed to specify the influence of website quality on customer loyalty with satisfaction as an e-commerce website moderation variable and to investigate the WebQual 4.0 attributes that affect this relationship of the model. This study used 104 respondents of Shopee Consumer and analyzed with the WebQual with Importance Performance Analysis (IPA) method and Structural Equation Model (SEM). The result is that the service quality variable significantly influence e-commerce website customer satisfaction variable, customer satisfaction variable has no significant effect on e-commerce website customer loyalty variables, and website service quality variable significantly influence e-commerce website customer loyalty variable. The attributes of WebQual 4.0 that need to be considered by e-commerce management to impact the level of user satisfaction and customer loyalty are “design by the type of website”, “provide simple-to-understand information”, “provide information following the format”, “feel safe to complete transactions”, “the website makes the positive/absolute experience for me”, and “provide detailed information”. The research implications can be taken into consideration for e-commerce management.
RT-MongoDB: A NoSQL Database with Differentiated Performance

The advent of Cloud Computing and Big Data brought several changes and innovations in the landscape of database management systems.Nowadays, a cloud-friendly storage system is required to reliably support data that is in continuous motion and of previously unthinkable magnitude, while guaranteeing high availability and optimal performance to thousands of clients.In particular, NoSQL database services are taking momentum as a key technology thanks to their relaxed requirements with respect to their relational counterparts, that are not designed to scale massively on distributed systems.Most research papers on performance of cloud storage systems propose solutions that aim to achieve the highest possible throughput, while neglecting the problem of controlling the response latency for specific users or queries.The latter research topic is particularly important for distributed real-time applications, where task completion is bounded by precise timing constraints.In this paper, the popular MongoDB NoSQL database software is modified introducing a per-client/request prioritization mechanism within the request processing engine, allowing for a better control of the temporal interference among competing requests with different priorities.Extensive experimentation with synthetic stress workloads demonstrates that the proposed solution is able to assure differentiated per-client/request performance in a shared MongoDB instance.Namely, requests with higher priorities achieve reduced and significantly more stable response times, with respect to lower priorities ones.This constitutes a basic but fundamental brick in providing assured performance to distributed real-time applications making use of NoSQL database services.
Fourth Industrial Revolution and digital competences of teachers

The new era of the 4IR requires changes in teachers’ initial education and in their professional advancement. Serbia is a developing country whose institutions of education are technologically underdeveloped and whose expansion in the field of digitalization is still insufficient to be on par with the developed western countries. The authors’ research goal was to determine whether potential improvement in teachers’ digital competences has occurred. The general hypothesis postulated implied a low level of the use of digital tools in schools on the territory of Serbia. The descriptive method and scaling technique were used. The research results show that science and technology teachers apply digital tools while teaching more frequently than the teachers of social sciences and humanities. The research implies that a strategic approach to Serbian teachers’ digital education is necessary even during their undergraduate studies by introducing digital technology courses and subjects in the conventional academic curricula.&#x0D; &#x0D; Keywords: Education, Digital competences, Teachers, Serbia, Fourth Industrial Revolution
Generic detection-based error mitigation using quantum autoencoders

Efficient error-mitigation techniques demanding minimal resources is key to quantum information processing. We propose a generic protocol to mitigate quantum errors using detection-based quantum autoencoders. In our protocol, the quantum data are compressed into a latent subspace while leaving errors outside, the latter of which is then removed by a measurement and postselection. Compared to previously developed methods, our protocol on the one hand requires no extra qubits, and on the other hand it has a near-optimal denoising power, in which under reasonable requirements all errors detected outside of the latent subspace can be removed, while those inside the subspace cannot be removed by any means. Our detection-based quantum autoencoders are therefore particularly useful for near-term quantum devices in which controllable qubits are limited while noise reduction is important.
Activity Recognition with Combination of Deeply Learned Visual Attention and Pose Estimation

While human activity recognition and pose estimation are closely related, these two issues are usually treated as separate tasks. In this thesis, two-dimension and three-dimension pose estimation is obtained for human activity recognition in a video sequence, and final activity is determined by combining it with an activity algorithm with visual attention. Two problems can be solved efficiently using a single architecture. It is also shown that end-to-end optimization leads to much higher accuracy than separated learning. The proposed architecture can be trained seamlessly with different categories of data. For visual attention, soft visual attention is used, and a multilayer recurrent neural network using long short term memory that can be used both temporally and spatially is used. The image, pose estimated skeleton, and RGB-based activity recognition data are all synthesized to determine the final activity to increase reliability. Visual attention evaluates the model in UCF-11 (Youtube Action), HMDB-51 and Hollywood2 data sets, and analyzes how to focus according to the scene and task the model is performing. Pose estimation and activity recognition are tested and analyzed on MPII, Human3.6M, Penn Action and NTU data sets. Test results are Penn Action 98.9%, NTU 87.9%, and NW-UCLA 88.6%.
Verifiable Data Mining Against Malicious Adversaries in Industrial Internet of Things

With the large-scaled data generated from various interconnected machines and networks, Industrial Internet of Things (IIoT) provides unprecedented opportunities for facilitating data mining for industrial applications. The current IIoT architecture tends to adopt cloud computing for further timely mining IIoT data, however, the openness of security-critical IIoT becomes challenging in terms of unbearable privacy issues. Most existing privacy-preserving data mining (PPDM) techniques are designed to resist honest-but-curious adversaries (i.e., cloud servers and data users). Due to the complexity and openness in IIoT, PPDM is significantly difficult with the presence of malicious adversaries in IIoT who may incur incorrect learned models and inference results. To solve the aforementioned issues, we propose a framework to extend existing PPDM to guard linear regression against malicious behaviors (hereafter referred to as GuardLR). To prevent dishonest computations of cloud servers and inconsistent inputs of data users, we first design a privacy-preserving verifiable learning scheme for linear regression, which guarantees the correctness of learning. In this article, to avoid malicious clouds from returning incorrect inference results, we design a privacy-preserving prediction scheme with lightweight verification. Our formal security analysis shows that GuardLR achieves privacy, completeness, and soundness. Empirical experiments using real-world datasets also demonstrate that GuardLR has high computational efficiency and accuracy.
Facial expression recognition for low resolution images using convolutional neural networks and denoising techniques

Background/Objectives: There is only limited research work is going on in the field of facial expression recognition on low resolution images. Mostly, all the images in the real world will be in low resolution and might also contain noise, so this study is to design a novel convolutional neural network model (FERConvNet), which can perform better on low resolution images. Methods: We proposed a model and then compared with state-of-art models on FER2013 dataset. There is no publicly available dataset, which contains low resolution images for facial expression recognition (Anger, Sad, Disgust, Happy, Surprise, Neutral, Fear), so we created a Low Resolution Facial Expression (LRFE) dataset, which contains more than 6000 images of seven types of facial expressions. The existing FER2013 dataset and LRFE dataset were used. These datasets were divided in the ratio 80:20 for training and testing and validation purpose. A HDM is proposed, which is a combination of Gaussian Filter, Bilateral Filter and Non local means denoising Filter. This hybrid denoising method helps us to increase the performance of the convolutional neural network. The proposed model was then compared with VGG16 and VGG19 models. Findings: The experimental results show that the proposed FERConvNet_HDM approach is effective than VGG16 and VGG19 in facial expression recognition on both FER2013 and LRFE dataset. The proposed FERConvNet_HDM approach achieved 85% accuracy on Fer2013 dataset, outperforming the VGG16 and VGG19 models, whose accuracies are 60% and 53% on Fer2013 dataset respectively. The same FERConvNet_HDM approach when applied on LRFE dataset achieved 95% accuracy. After analyzing the results, our FERConvNet_HDM approach performs better than VGG16 and VGG19 on both Fer2013 and LRFE dataset. Novelty/Applications: HDM with convolutional neural networks, helps in increasing the performance of convolutional neural networks in Facial expression recognition. Keywords: Facial expression recognition; facial emotion; convolutional neural network; deep learning; computer vision
Quantum k-uniform states for heterogeneous systems from irredundant mixed orthogonal arrays

Quantum multipartite entangled states play significant roles in quantum information processing. By using difference schemes and orthogonal partitions, we construct a series of infinite classes of irredundant mixed orthogonal arrays (IrMOAs) and thus provide positive answers to two open problems. The first is the extension of the method for constructing homogeneous systems from orthogonal arrays to heterogeneous multipartite systems with different individual levels. The second is the existence of k-uniform states in heterogeneous quantum systems. We present explicit constructions of two- and three-uniform states for arbitrary heterogeneous multipartite systems with coprime individual levels and characterize the entangled states in heterogeneous systems consisting of subsystems with nonprime power dimensions as well. Moreover, we obtain infinite classes of k-uniform states for heterogeneous multipartite systems for any $$k\ge 2$$ . The non-existence of a class of IrMOAs is also proved.
A Framework for Mobile-Assisted Formative Assessment to Promote Students’ Self-Determination

Motivation is an important issue to consider when designing learning activities, including mobile learning and assessment. While previous research provides evidence for the motivational impact of mobile learning, not many pedagogical frameworks exist for the design of mobile-assisted learning and assessment. The current study is grounded in the Self-Determination Theory of motivation and proposes a pedagogical framework for mobile-assisted formative assessment, aiming at enhancing student motivation. For a preliminary evaluation of the framework, fifty-one students from a public European high school participated in a series of formative assessment activities. The tasks that were implemented according to the proposed mobile-based formative assessment framework had a significant positive impact on student perceived levels of autonomy, competence, and relatedness, enhancing students’ intrinsic motivation levels. Study findings highlighted the capacity of the proposed framework to guide the design of mobile-based formative assessment activities that enhance and promote student motivation. The study makes a theoretical contribution by proposing a framework that aligns mobile learning and assessment with elements of the Self-Determination Theory of motivation and also has a practical contribution by implementing mobile learning and assessment practices that have the potential to promote student motivation.
OpenKG Chain: A Blockchain Infrastructure for Open Knowledge Graphs

Abstract The early concept of knowledge graph originates from the idea of the Semantic Web, which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things. Currently publishing knowledge bases as open data on the Web has gained significant attention. In China, CIPS(Chinese Information Processing Society) launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs. Unlike existing open knowledge-based programs, OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure. This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain, a blockchain-based trust network. We have completed the test of the underlying blockchain platform, as well as the on-chain test of OpenKG's dataset and toolset sharing as well as fine-grained knowledge crowdsourcing at the triple level. We have also proposed novel definitions: K-Point and OpenKG Token, which can be considered as a measurement of knowledge value and user value. 1033 knowledge contributors have been involved in two months of testing on the blockchain, and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000. For the first time, We have tested and realized on-chain sharing of knowledge at entity/triple granularity level. At present, all operations on the datasets and toolset in OpenKG.CN, as well as the triplets in OpenBase, are recorded on the chain, and corresponding value will also be generated and assigned in a trusted mode. Via this effort, OpenKG chain looks to provide a more credible and traceable knowledge-sharing platform for the knowledge graph community.
HADF-Crowd: A Hierarchical Attention-Based Dense Feature Extraction Network for Single-Image Crowd Counting

Crowd counting is a challenging task due to large perspective, density, and scale variations. CNN-based crowd counting techniques have achieved significant performance in sparse to dense environments. However, crowd counting in high perspective-varying scenes (images) is getting harder due to different density levels occupied by the same number of pixels. In this way large variations for objects in the same spatial area make it difficult to count accurately. Further, existing CNN-based crowd counting methods are used to extract rich deep features; however, these features are used locally and disseminated while propagating through intermediate layers. This results in high counting errors, especially in dense and high perspective-variation scenes. Further, class-specific responses along channel dimensions are underestimated. To address these above mentioned issues, we therefore propose a CNN-based dense feature extraction network for accurate crowd counting. Our proposed model comprises three main modules: (1) backbone network, (2) dense feature extraction modules (DFEMs), and (3) channel attention module (CAM). The backbone network is used to obtain general features with strong transfer learning ability. The DFEM is composed of multiple sub-modules called dense stacked convolution modules (DSCMs), densely connected with each other. In this way features extracted from lower and middle-lower layers are propagated to higher layers through dense connections. In addition, combinations of task independent general features obtained by the former modules and task-specific features obtained by later ones are incorporated to obtain high counting accuracy in large perspective-varying scenes. Further, to exploit the class-specific response between background and foreground, CAM is incorporated at the end to obtain high-level features along channel dimensions for better counting accuracy. Moreover, we have evaluated the proposed method on three well known datasets: Shanghaitech (Part-A), Shanghaitech (Part-B), and Venice. The performance of the proposed technique justifies its relative effectiveness in terms of selected performance compared to state-of-the-art techniques.
SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising

On the WikiSQL 1 benchmark, most methods tackle the challenge of text-to-SQL with predefined sketch slots and build sophisticated sub-tasks to fill these slots.Though achieving promising results, these methods suffer from over-complex model structure.In this paper, we present a simple yet effective approach that enables auto-regressive sequenceto-sequence model to robust text-to-SQL generation.Instead of formulating the task of text-to-SQL as slot-filling, we propose to train sequence-to-sequence model with Schemaaware Denoising (SeaD), which consists of two denoising objectives that train model to either recover input or predict output from two novel erosion and shuffle noises.These modelagnostic denoising objectives act as the auxiliary tasks for structural data modeling during sequence-to-sequence generation.In addition, we propose a clause-sensitive execution guided (EG) decoding strategy to overcome the limitation of EG decoding for generative model.The experiments show that the proposed method improves the performance of sequence-to-sequence model in both schema linking and grammar correctness and establishes new state-of-the-art on WikiSQL benchmark.Our work indicates that the capacity of sequence-to-sequence model for text-to-SQL may have been under-estimated and could be enhanced by specialized denoising task.
Quantitative Data Visualisation on Virtual Globes

Geographic data visualisation on virtual globes is intuitive and widespread, but has not been thoroughly investigated. We explore two main design factors for quantitative data visualisation on virtual globes: i) commonly used primitives (2D bar, 3D bar, circle) and ii) the orientation of these primitives (tangential, normal, billboarded). We evaluate five distinctive visualisation idioms in a user study with 50 participants. The results show that aligning primitives tangentially on the globe's surface decreases the accuracy of area-proportional circle visualisations, while the orientation does not have a significant effect on the accuracy of length-proportional bar visualisations. We also find that tangential primitives induce higher perceived mental load than other orientations. Guided by these results we design a novel globe visualisation idiom, Geoburst, that combines a virtual globe and a radial bar chart. A preliminary evaluation reports potential benefits and drawbacks of the Geoburst visualisation.