Reviewerly/exHarmony huggingface dataset:
Grid Computing for MSE during Volatile Economy

Medium Size Enterprise ventures into IT are dragged by financial, expertise, economies of scales plus the unfavourable market uncertainty with unsettling innovation with vanishing competitiveness towards infrastructure optimization. This mixed-method using survey, interview and focus group with dual data test-retest sequential design approach minimises grey perspectives and conclusions. The findings revealed the shocking fact that a third of total investments in IT were categorised as the unproductive ventures, playing catch-up among the industry competitors while leaving most equipment under-utilized and questionable commercial visibilities while the enterprise bleeds. The utilization of BOINC freeware grid computing centralized processing and storage features been used to improve the enterprise's efficiency by enhancing positive financials in the enterprises via enabling better inter-department work coordination hence improving user satisfaction and stronger brand recognition. The contribution of the study proposed a flexible organic IT infrastructure that will upsize or downsize along with the business performance.
AiR: Attention with Reasoning Capability

While attention has been an increasingly popular component in deep neural networks to both interpret and boost performance of models, little work has examined how attention progresses to accomplish a task and whether it is reasonable. In this work, we propose an Attention with Reasoning capability (AiR) framework that uses attention to understand and improve the process leading to task outcomes. We first define an evaluation metric based on a sequence of atomic reasoning operations, enabling quantitative measurement of attention that considers the reasoning process. We then collect human eye-tracking and answer correctness data, and analyze various machine and human attentions on their reasoning capability and how they impact task performance. Furthermore, we propose a supervision method to jointly and progressively optimize attention, reasoning, and task performance so that models learn to look at regions of interests by following a reasoning process. We demonstrate the effectiveness of the proposed framework in analyzing and modeling attention with better reasoning capability and task performance. The code and data are available at https://github.com/szzexpoi/AiR .
Fast and Accurate Inference on Microcontrollers With Boosted Cooperative Convolutional Neural Networks (BC-Net)

Arithmetic precision scaling is mandatory to deploy Convolutional Neural Networks (CNNs) on resource-constrained devices such as microcontrollers (MCUs), and quantization via fixed-point or binarization are the most adopted techniques today. Despite being born by the same concept of bit-width lowering, these two strategies differ substantially each other, and hence are often conceived and implemented separately. However, their joint integration is feasible and, if properly implemented, can bring to large savings and high processing efficiency. This work elaborates on this aspect introducing a boosted collaborative mechanism that pushes CNNs towards higher performance and more predictive capability. Referred as BC-Net, the proposed solution consists of a self-adaptive conditional scheme where a lightweight binary net and an 8-bit quantized net are trained to cooperate dynamically. Experiments conducted on four different CNN benchmarks deployed on off-the-shelf boards powered with the MCUs of the Cortex-M family by ARM show that BC-Nets outperform classical quantization and binarization when applied as separate techniques (up to 81.49% speed-up and up to 3.8% of accuracy improvement). The comparative analysis with a previously proposed cooperative method also demonstrates BC-Nets achieve substantial savings in terms of both performance (+19%) and accuracy (+3.45%).
Quantum-inspired learning vector quantizers for prototype-based classification

Abstract Prototype-based models like the Generalized Learning Vector Quantization (GLVQ) belong to the class of interpretable classifiers. Moreover, quantum-inspired methods get more and more into focus in machine learning due to its potential efficient computing. Further, its interesting mathematical perspectives offer new ideas for alternative learning scenarios. This paper proposes a quantum computing-inspired variant of the prototype-based GLVQ for classification learning. We start considering kernelized GLVQ with real- and complex-valued kernels and their respective feature mapping. Thereafter, we explain how quantum space ideas could be integrated into a GLVQ using quantum bit vector space in the quantum state space $${\mathcal {H}}^{n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mi>n</mml:mi></mml:msup></mml:math> and show the relations to kernelized GLVQ. In particular, we explain the related feature mapping of data into the quantum state space $${\mathcal {H}}^{n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mi>n</mml:mi></mml:msup></mml:math> . A key feature for this approach is that $${\mathcal {H}}^{n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mi>n</mml:mi></mml:msup></mml:math> is an Hilbert space with particular inner product properties, which finally restrict the prototype adaptations to be unitary transformations. The resulting approach is denoted as Qu-GLVQ. We provide the mathematical framework and give exemplary numerical results.
Three <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si96.svg"><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math>-secret image sharing schemes based on homogeneous linear recursion

In this paper, three novel secret image sharing schemes based on homogeneous linear recursion are introduced. Simple sharing, various methods for the recovery, small shadow image size and high resolution are some of the merits of our schemes. Moreover, shadow images generated in schemes 2 and 3 are unrevealing and are secured against attacks based on correlation of adjacent pixels, without any need to perform extra computations such as permutation or encryption, which usually were used in the previous schemes.
Blended Grammar Network for Human Parsing

Although human parsing has made great progress, it still faces a challenge, i.e., how to extract the whole foreground from similar or cluttered scenes effectively. In this paper, we propose a Blended Grammar Network (BGNet), to deal with the challenge. BGNet exploits the inherent hierarchical structure of a human body and the relationship of different human parts by means of grammar rules in both cascaded and paralleled manner. In this way, conspicuous parts, which are easily distinguished from the background, can amend the segmentation of inconspicuous ones, improving the foreground extraction. We also design a Part-aware Convolutional Recurrent Neural Network (PCRNN) to pass messages which are generated by grammar rules. To train PCRNNs effectively, we present a blended grammar loss to supervise the training of PCRNNs. We conduct extensive experiments to evaluate BGNet on PASCAL-Person-Part, LIP, and PPSS datasets. BGNet obtains state-of-the-art performance on these human parsing datasets.
Transfer Collaborative Fuzzy Clustering in Distributed Peer-to-Peer Networks

The traditional collaborative fuzzy clustering can effectively perform data clustering in distributed peer-to-peer networks, which is an impossible task to complete for the centralized clustering methods due to privacy and security requirements or network transmission technology constraints. But it will increase the number of clustering iterations and lead to lower efficiency of the clustering. Moreover, the collaborative mechanism hidden in the iterative process of clustering cannot be well revealed and explained. In this article, a novel series of transfer collaborative fuzzy clustering algorithms are proposed to solve these issues. In the first basic algorithm, the transfer learning among neighbor nodes vividly expresses the collaborative mechanism and enhances the information collaboration to accelerate the convergence of fuzzy clustering. Meanwhile, neighbor nodes can learn the knowledge from each other to further promote their respective clustering performance. Then, an improved version, with the learning-rate-adjustable strategy instead of fixed values, is designed to highlight the different influence between neighbor nodes, and the appropriate learning rates between neighbor nodes are achieved to ensure the stable clustering accuracy. Finally, two extended versions with the attribute-weight-entropy regularization technique are presented for the clustering of high dimensional sparse data and the extraction of important subspace features. Experiments show the efficiency of the proposed algorithms compared with the related prototype-based clustering methods.
Teaching <scp>Turkish‐Dutch</scp> kindergartners Dutch vocabulary with a social robot: Does the robot's use of Turkish translations benefit children's Dutch vocabulary learning?

Abstract Providing first language (L1) translations in L2 vocabulary interventions may be beneficial for L2 vocabulary learning. However, in linguistically diverse L2 classrooms, teachers cannot provide L1 translations to all children. Social robots do offer such opportunities, as they can be programmed to speak any combination of languages. This study investigates whether providing L1 translations in a robot‐assisted L2 vocabulary training facilitates children's learning. Participants were Turkish‐Dutch kindergartners ( n = 67) who were taught six Dutch (L2) words for which they knew the L1 (Turkish), but not the L2 Dutch form. Half of these words were taught by a Turkish‐Dutch bilingual robot, alongside their Turkish translations; the other half by a monolingual Dutch robot. Children also completed Dutch and Turkish receptive vocabulary tests. Results of generalized linear regression models indicated better performance in the Dutch‐only condition than in the Turkish‐Dutch condition. Children with well‐developed Turkish and Dutch vocabulary knowledge outperformed children with less well‐developed vocabulary knowledge. The majority of children preferred working with the bilingual robot, but children's preference did not affect word learning. Thus, contrary to our prediction, we found no evidence for a facilitating effect of providing L1 translations through a robot on bilingual children's L2 word learning.
Bilateral skin temperature drop and warm sensibility decrease following modulation of body part ownership through mirror-box illusion

The implicit and explicit awareness of owning a body and its parts is a constant accompaniment in our everyday life and our interaction with the outside world. The way in which we build and maintain a coherent sense of body ownership is not fully understood. It has been postulated that the integration between exteroceptive, interoceptive, and proprioceptive signals may play a fundamental role in the sense of body ownership. For instance, studies on healthy subjects and brain-damaged patients have suggested that alterations in the sense of body ownership are coupled with autonomic signal changes, such as thermoregulatory reactions. However, the available evidence is conflicting, possibly due to shortcomings in the experimental paradigm that previous studies have adopted. In this study, we explore the relationship between body ownership, thermoregulation, and thermal sensitivity through a novel application of the mirror-box illusion paradigm, overcoming some of the limitations of previous studies. We find a bilateral decrease in hand skin temperature, together with reduced thermal sensitivity for warm thermal stimuli following the induction of the illusion of ownership towards the participant's reflected hand. These findings demonstrate the importance of the orchestration of exteroceptive (e.g., visual), autonomic (e.g., body temperature) and proprioceptive (e.g., position and movement of the body) signals in maintaining a coherent sense of body ownership.
SPB-Net: A Deep Network for SAR Imaging and Despeckling With Downsampled Data

Synthetic aperture radar (SAR) typically faces both large-scale data and speckle noise problems, which, respectively, induce enormous strains on transmission and storage and interfere with the analysis and interpretation of SAR images. To tackle these, we present a real-time processing deep network, called SPB-Net, to implement imaging and speckle suppression simultaneously. First, a novel imaging-despeckling observation model with the nonlogarithmic additive speckle noise is established. Subsequently, guided by the statistical properties of noise and sparse and detail-preserved requirements in SAR imaging and despeckling, we formulate an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> along with two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> regularizations as the fidelity, sparse, and image detail-preserved constraints, respectively. Convolution layers are employed to improve the feature representation capability in the latter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> term as well. Based on this, we construct a corresponding convex optimization problem. Then, the complex-valued split Bregman method, focusing on the complex-variable convex problem, is unfolded into a parameter-learnable and architecture-fixed SPB-Net to solve the proposed problem effectively and efficiently. Experimental results with the downsampled Radarsat-1 raw data demonstrate the validity in imaging and speckle suppression and the real-time processing capability of the proposed SPB-Net.
Improving Face Recognition by Clustering Unlabeled Faces in the Wild

While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation. Prior work has mostly been in controlled settings, where the labeled and unlabeled data sets have no overlapping identities by construction. This is not realistic in large-scale face recognition, where one must contend with such overlaps, the frequency of which increases with the volume of data. Ignoring identity overlap leads to significant labeling noise, as data from the same identity is split into multiple clusters. To address this, we propose a novel identity separation method based on extreme value theory. It is formulated as an out-of-distribution detection algorithm, and greatly reduces the problems caused by overlapping-identity label noise. Considering cluster assignments as pseudo-labels, we must also overcome the labeling noise from clustering errors. We propose a modulation of the cosine loss, where the modulation weights correspond to an estimate of clustering uncertainty. Extensive experiments on both controlled and real settings demonstrate our method's consistent improvements over supervised baselines, e.g., 11.6% improvement on IJB-A verification.
Attendance System Based on Dynamic Face Recognition

A video-based attendance system is designed by using the method of real-time face recognition. The system supports multi-user attendance and face liveness detection at the same time. The system can automatically collect face data, which will be saved in the database as well as attendance results. The face detection part of the system is based on MTCNN (Multitask Convolutional Neural Network) algorithm, and the face recognition part is based on FaceNet algorithm. The algorithm implementation is based on TensorFlow framework, and the face liveness detection part is based on ERT (Ensemble of Regression Tree) algorithm, which can judge whether the user blinks. The attendance system is written in Python language, and the user interface is designed by Qt library. The experimental results show that the system achieves a good performance in real-time face recognition. The false accept rate and false rejection rate of face recognition are within 2%, and the recognition rate can be stable at 20 FPS.
Influence of digital economy on youth unemployment in West Africa

(2021). Influence of digital economy on youth unemployment in West Africa. Transnational Corporations Review: Vol. 13, Emerging Multinationals in a Digital Era and Impact of COVID-19 on Foreign Investment, pp. 32-42.
Network Topology Inference From Heterogeneous Incomplete Graph Signals

Inferring network topologies from observed graph-structured data (also known as graph signals) is a crucial task in many applications of network science. Existing papers on network topology inference typically assume that the observations at all nodes are available. However, there are many situations where only partial observations can be collected due to application-specific constraints. To handle the missing data problem, we propose a framework that relies on heterogeneous incomplete data from a collection of related networks to identify multiple network topologies simultaneously. This work advocates the Gaussian graphical model (GGM) and casts the topology inference problem in terms of estimating the precision matrix that has a form of graph Laplacian. Firstly, an unbiased estimator for the covariance matrix of incomplete data is established and then algorithms based on the alternating direction method of multipliers (ADMM) are developed to jointly estimate graph topologies, rather than estimate each graph topology separately. So that we can borrow information across the multiple related networks to eliminate the impact of missing data. Moreover, non-asymptotic statistical analysis is provided, which proves the consistency of the graph estimator and enables us to investigate the effect of several key factors on the graph estimation error bound. Furthermore, based on the consistent graph estimator, an adaptive algorithm that utilizes the reweighting scheme is proposed to improve the estimation accuracy of the graph-edge structure. Finally, we evaluate our method on both real and synthetic datasets, and the experimental results demonstrate the advantage of our method in comparison with benchmarking algorithms.
Reduction in Robotic Arm Energy Consumption by Particle Swarm Optimization

Improved energy usage efficiency is a common goal for economic and environmental reasons. In this manuscript, we present a new approach for the execution of a point-to-point robot motion. The energy efficiency of an industrial or collaborative robot is increased by the reduction of the energy consumption during nontechnological, path-independent movements. The novel trajectory generation method relies on particle swarm optimization with a Bezier curve interpolator. We present the effectiveness of the algorithm on several chosen trajectories, where the best result yields up to 40% energy saving, while the worst is still at least 10%. We verified the results of our method by real-world tests on a UR3 robotic arm.
Mobile network traffic pattern classification with incomplete a priori information

In complex networks systems like mobile edge infrastructures, real-time traffic classification according to application types is an enabling technique for network resource optimization and advanced security management. State-of-the-art schemes take advantage of machine learning techniques to train classification models based on behavioral characteristics of network traffic flows. Nonetheless, most existing studies assume complete a priori information of the application classes and formulate the task as a standalone multi-class classification problem. Such classification models cannot properly handle the unknown applications that are absent from the training set during the time of training. In this work, we propose a practical mobile network traffic classification scheme that builds robust classifiers based on incomplete a priori information. Specifically, the core idea is to extract the unknown patterns emerging in the network periodically to complement the initial labeled data set that only consists of a limited number of known applications. We propose two algorithms for the unknown pattern extraction step. One is based on iterative asymmetric binary classification and the other is based on constrained clustering. Empirical results based on a public data set show that the proposed scheme can effectively detect both known and unknown applications.
Analysis on Approaches and Structures of Automated Machine Learning Frameworks

Due to the explosive and increasing demands of machine learning, automated machine learning is developed to handle machine learning tasks for non-experts. Lots of AutoML frameworks are introduced in past decades. Each of them has their unique contributions towards AutoML field. In this paper, four popular open source AutoML frameworks are selected and reviewed to show current development directions and common features for frameworks. This paper also analyzes innovative structures and designs of selected frameworks. The result shows that one of the newest frameworks, AutoGluon, has extraordinary performance and innovative structures when compared to others.
An End-to-End OCR Text Re-organization Sequence Learning for Rich-Text Detail Image Comprehension

Nowadays the description of detailed images helps users know more about the commodities. With the help of OCR technology, the description text can be detected and recognized as auxiliary information to remove the visually impaired users’ comprehension barriers. However, for lack of proper logical structure among these OCR text blocks, it is challenging to comprehend the detailed images accurately. To tackle the above problems, we propose a novel end-to-end OCR text reorganizing model. Specifically, we create a Graph Neural Network with an attention map to encode the text blocks with visual layout features, with which an attention-based sequence decoder inspired by the Pointer Network and a Sinkhorn global optimization will reorder the OCR text into a proper sequence. Experimental results illustrate that our model outperforms the other baselines, and the real experiment of the blind users’ experience shows that our model improves their comprehension.
3D Model-Based Gaze Tracking Via Iris Features With a Single Camera and a Single Light Source

Traditional 3D gaze estimation methods are usually based on the models of pupil refraction and corneal reflection. These methods typically rely on multiple light sources. The 3D gaze can be estimated using single-camera-single-light-source systems only when certain user-dependent eye parameters are available a priori, which is rarely the case. This article proposes a 3D gaze estimation method which works based on iris features using a single camera and a single light source. User-dependent eye parameters involving the iris radius and the cornea radius are user-calibrated. The 3D line-of-sight is estimated from the optical axis and the positional relationship between the optical axis and the visual axis, and then optimized using a binocular stereo vision model. The feasibility and robustness of the proposed method are assessed by simulations and practical experiments. The system configuration required by the method is simpler than that required by the state-of-the-art methods, which shows significant potential value, especially with regard to mobile device applications.