Reviewerly/exHarmony huggingface dataset:
The Possibilities of Developing STEM Skills in Higher Education

The development of information and technology in recent decades has entailed a change of attitudes in higher education. In addition to academic knowledge, it is becoming increasingly important for students to acquire up-to-date, practical knowledge that will help them find their place in the world of work and in everyday life. Based on international analyses, the following characteristics, so-called soft skills, are essential on the labour market: higher-level thinking, communication skills, cooperation, self-control and a positive self-image. In our paper, we examine the possibilities of skills development in the field of STEM, especially in engineering education. In our university’s engineering teacher training, we strive to develop our students’ STEM skills using a variety of methods, and prepare them for the teacherly task of being able to effectively develop their own students’ skills and motivate them in STEM areas. This requires students to view their own learning process not as passive onlookers but as active participants. In our paper, we present some methods that can be effectively applied in STEM areas (e.g. discussion, collaborative learning, cooperative methods, project method, problem-based learning, inquiry-based learning, gamification, the use of robots in education). Keywords: STEM, skills development, educational methods, teacher training
Clustering of Monolingual Embedding Spaces

Suboptimal performance of cross-lingual word embeddings for distant and low-resource languages calls into question the isomorphic assumption integral to the mapping-based methods of obtaining such embeddings. This paper investigates the comparative impact of typological relationship and corpus size on the isomorphism between monolingual embedding spaces. To that end, two clustering algorithms were applied to three sets of pairwise degrees of isomorphisms. It is also the goal of the paper to determine the combination of the isomorphism measure and clustering algorithm that best captures the typological relationship among the chosen set of languages. Of the three measures investigated, Relational Similarity seemed to capture best the typological information of the languages encoded in their respective embedding spaces. These language clusters can help us identify, without any pre-existing knowledge about the real-world linguistic relationships shared among a group of languages, the related higher-resource languages of low-resource languages. The presence of such languages in the cross-lingual embedding space can help improve the performance of low-resource languages in a cross-lingual embedding space.
Impact of Government Intervention on Manufacturing Enterprises Innovation level, in Ethiopia

Besides alert devastation situation of Small Manufacturing Enterprises (SMEs) in the country, what support receives from the government, banking, microFinance institutions, technology transfer and their role in the local economic development are the main issues that need to be addressed. Hence, the main objective of the study is to evaluate the impact of government intervention on the Small Manufacturing Enterprises Innovation level in Tigray, Ethiopia. For this purpose, we conducted a survey of 464 small manufacturing enterprise (textile and garment, metal and wood works, input for construction, chemical products, jewellery, and agro-processing sectors) owners. The study is, stressed the primary data, adopt a Cross-sectional survey study with a mixed research approach, and the types of research are explanatory. Multistage cluster sampling was applied to select each business owner. Descriptive statistics, chi-square test, propensity score matching and logistic regression were relayed for the analysis part with the help of Stata version 12. The average score innovation level of the enterprise is found at 2.89 which is assembled under the low innovators in the sample of the enterprise analyzed. Innovations activity has performed with the highest score level in the customer dimension. With regard to the PSM estimated, government intervention plays a significant role in utilizing innovation and technology transfer. Likewise, in a binary logistic regression, the study found that the intervention of the program has a positive effect on the innovation process dimension. Before and after matching, the findings of both models are similar in terms of the owner’s innovation of offering dimension, solution dimension, customer dimension, and value capture, which is positive and significant. Designing a new government policy intervention of Small Manufacturing Enterprises is among the resolutions to be applied by the government for purpose of productivity enhancement, self-employability, sustainability of the business and transforming to produce innovative products to replace imported goods.
Scaling Subspace-Driven Approaches Using Information Fusion

In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the group subspace distribution of the information using the Convolutional Neural Networks (CNNs) formalism. Upon unfolding the set of subspaces constituting each data modality, and learning their corresponding encoders, an optimized integration of the generated inherent information is carried out to yield a characterization of various classes. Referred to as deep Multimodal Robust Group Subspace Clustering (DRoGSuRe), this approach is compared against the independently developed state-of-the-art approach named Deep Multimodal Subspace Clustering (DMSC). Experiments on different multimodal datasets show that our approach is competitive and more robust in the presence of noise.
Masked Visual Transformer for Efficient Training with Small Dataset

Vision Transformers (ViTs) are becoming an architectural paradigm replacing the convolutional neural networks (CNNs) in computer vision. ViTs offer competitive performances with respect to CNNs, but they, especially the vanilla ViTs, are hungrier for data than the typical CNNs as the short of the common inductive bias of convolution. Recently, few works focused on training vanilla ViTs efficiently with small datasets. In this paper, we perform research on training vanilla ViTs with small dataset containing thousands of images, and propose a method that is applied to self-supervised pretraining stage. The proposed method combines parametric instance discrimination with CutMix and Multi-crop. Furthermore, we introduce image masking to reduce the overfitting of pretraining on small dataset. State-of-the-art results are achieved by our method for training from scratch based on vanilla ViT backbones on seven small-scale datasets. The transferring performance of our method is also tested on small datasets, and results show that it is improved significantly.
Identifying Notable Tuples in Multi-Concept Web Tables

Identifying notable tuples in a web table is of great help for table understanding and table summarization. However, existing document-internal feature-based methods are inappropriate for identifying notable tuples in web tables. Additionally, for the web table describing multiple concepts, the notability evaluation of a tuple needs to take into account multiple entities as well as their importance in this tuple. In this paper, we investigate the task of identifying notable tuples in a multi-concept web table and propose a framework that includes three tasks: (1) identify multiple entity columns and their importance weights by building a column correlation graph based on types and relationships in the table; (2) obtain fine-grained entity notability scores based on entity link graph and provide solution for entity link failure and entity domain neglection; and (3) evaluate tuple notability by a weighted sum of notability scores of all entities in the tuple. Comprehensive evaluation of our approach is based on real-world web tables. The results demonstrate that our approach outperforms the state-of-the-art baselines by 4.6% on the precision of detecting multiple entity columns and by 12.5% on the metric normalized discounted cumulative gain (NDCG) of evaluating entity notability.
Simulation of reversible molecular mechanical logic gates and circuits

Landauer's principle places a fundamental lower limit on the work required to perform a logically irreversible operation. Logically reversible gates provide a way to avoid these work costs and also simplify the task of making the computation as a whole thermodynamically reversible. The inherent reversibility of mechanical logic gates would make them good candidates for the design of practical logically reversible computing systems if not for the relatively large size and mass of such systems. In this paper we outline the design and simulation of reversible molecular mechanical logic gates that come close to the limits of thermodynamic reversibility even under the effects of thermal noise, and outline associated circuit components from which arbitrary combinatorial reversible circuits can be constructed and simulated. We demonstrate that isolated components can be operated in a thermodynamically reversible manner, and explore the complexities of combining components to implement more complex computations. Finally, we demonstrate a method to construct arbitrarily large reversible combinatorial circuits using multiple external controls and signal boosters with a working half-adder circuit.
Hand pose estimation based on fish skeleton CNN: application in gesture recognition

The modern world contains a significant number of applications based on computer vision, in which human-computer interaction plays a crucial role, pose estimation of the hand is a crucial approach in the field of human-computer interaction. However, previous approaches suffer from the inability to accurately measure position in real-world scenes, difficulty in obtaining targets of different sizes, the structure of complex network, and the lack of applications. In recent years, deep learning techniques have produced state-of-the-art outcomes but there are still challenges that need to be overcome to fully exploit this technology. In this research, a fish skeleton CNN (FS-HandNet) is proposed for hand posture estimation from a monocular RGB image. To obtain hand pose information, a fish skeleton network structure is used for the first time. Particularly, bidirectional pyramid structures (BiPS) can effectively reduce the loss of feature information during downsampling and can be used to extract features from targets of different sizes. It is more effective at solving problems of different sizes. Then a distribution-aware coordinate representation is employed to adjust the position information of the hand, and finally, a convex hull algorithm and hand pose information are applied to recognize multiple gestures. Extensive studies on three publicly available hand position benchmarks demonstrate that our method performs nearly as well as the state-of-the-art in hand pose estimation. Additionally, we have implemented hand pose estimation for the application of gesture recognition.
TailWAG: Tail Latency Workload Analysis and Generation

Server performance has always been an active field of research, spanning topics including databases, network applications, and cloud computing. Tail latency is one of the most important performance metrics for server performance. In this work, we study how different timing components affect server workload performance, including thread contention and interrupt handling. We introduce a workload analysis and generation tool: TailWAG. TailWAG relies on measured timing components from applications to generate a synthetic workload that mimics the original behavior. TailWAG can help server designers and application developers explore performance bottlenecks and optimization opportunities at early stages.
Vision-Based Global Localization of Points of Gaze in Sport Climbing

Investigating realistic visual exploration is quite challenging in sport climbing, but it promises a deeper understanding of how performers adjust their perception-action couplings during task completion. However, the samples of participants and the number of trials analyzed in such experiments are often reduced to a minimum because of the time-consuming treatments of the eye-tracking data. Notably, mapping successive points of gaze from local views to the global scene is generally performed manually by watching eye-tracking video data frame by frame. This manual procedure is not suitable for processing a large number of datasets. Consequently, this study developed an automatic method for solving this global point of gaze localization in indoor sport climbing. Particularly, an eye-tracking device was used for acquiring local image frames and points of gaze from a climber’s local views. Artificial landmarks, designed as four-color-disk groups, were distributed on the wall to facilitate localization. Global points of gaze were computed based on planar homography transforms between the local and global positions of the detected landmarks. Thirty climbing trials were recorded and processed by the proposed methods. The success rates (Mean[Formula: see text]±[Formula: see text]SD) were up to 85.72%[Formula: see text]±[Formula: see text]13.90%, and the errors (Mean[Formula: see text]±[Formula: see text]SD) were up to [Formula: see text][Formula: see text]m. The proposed method will be employed for computing global points of gaze in our current climbing dataset for understanding the dynamics intertwining of gaze and motor behaviors during the climbs.
A Nonlinear Hybrid Diffusion Model for Image Denoising

Abstract In this paper, a denoising technique is proposed based on a nonlinear anisotropic diffusion model with two diffusivities parameters, i.e., Charbonnier and total variation. These diffusivities are dependent on the diffusivity function for balancing. Furthermore, proof of the existence and uniqueness theorem of the model are presented. The convergent iterative scheme is proposed for the diffusion model. To discretize the diffusion model, the finite difference method with forward‐backward diffusivities is used. The numerical results are given in terms of peak signal‐to‐noise ratio (PSNR) as a metric.
Research on a high-precision extraction algorithm for aircraft skin seam features

The precision measurement of the surfaces of aircraft based on structured light technology is an important development direction of aircraft skin assembly and maintenance. Based on the characteristics of the point cloud in the skin seam area, the height difference mutation method and the slope value maximization method are proposed to extract the edge points of the feature point cloud, achieving the high-precision extraction of the flush and gap of the aircraft skin. First, the 3D point cloud data of the feature region are generated with fringe projection profilometry, and an adaptive matching calibration algorithm is used to calibrate the feature region with high precision. A 3D feature data calculation model is established for both straight line and curve seams, and the edge points of the feature point cloud are extracted with the height difference mutation method and the slope value maximization method to achieve the high-precision extraction of 3D feature data of aircraft skin seams. In this study, an aircraft seam 3D data measurement model is systematically established. This model is suitable for both straight lines and curves. Compared with the algorithm for the feature seam extraction of the slope difference skin, the model has better robustness, and the average measurement error is reduced to approximately one-third of that of the classical algorithm.
Quantum Authentication Using Double Teleportation

Abstract A process involving quantum teleportation to teleport a state to two parties in superposition is presented. Then, the process is applied to cryptography. Concretely in this work, we present the process of cryptography authentication using an undefined qubit state and the double quantum teleportation presented previously. Since quantum cryptography protocols are susceptible of eavesdropping, then such a process is considered through an eavesdropper trying to steal the information to supplant the user of authentication in order to quantify certain limits of applicability.
Automatic 3D joint erosion detection for the diagnosis and monitoring of rheumatoid arthritis using hand HR-pQCT images

Rheumatoid arthritis (RA) is a chronic inflammatory disease. It leads to bone erosion in joints and other complications, which severely affect patients' quality of life. To accurately diagnose and monitor the progression of RA, quantitative imaging and analysis tools are desirable. High-resolution peripheral quantitative computed tomography (HR-pQCT) is such a promising tool for monitoring disease progression in RA. However, automatic erosion detection tools using HR-pQCT images are not yet available. Inspired by the consensus among radiologists on the erosions in HR-pQCT images, in this paper we define erosion as the significant concave regions on the cortical layer, and develop a model-based 3D automatic erosion detection method. It mainly consists of two steps: constructing closed cortical surface, and detecting erosion regions on the surface. In the first step, we propose an initialization-robust region competition methods for joint segmentation, and then fill the surface gaps by using joint bone separation and curvature-based surface alignment. In the second step, we analyze the curvature information of each voxel, and then aggregate the candidate voxels into concave surface regions and use the shape information of the regions to detect the erosions. We perform qualitative assessments of the new method using 59 well-annotated joint volumes. Our method has shown satisfactory and consistent performance compared with the annotations provided by medical experts.
Quantum Mechanics and Classical Optics: New Ways to Combine Classical and Quantum Methods

Abstract Quantum Mechanics and classical optics feature similar phenomena such as superposition, interference and even entanglement. Hence, techniques from optics can be used in quantum mechanics and vice versa. In this article I address the question: What can we learn from formulating optics in the language of quantum mechanics? It is argued that the solutions of the wave equations for the electromagnetic field form a tensor product of Hilbert spaces corresponding to the degrees of freedom of classical light. Therefore, it comprises non-separable solutions reminiscent of entanglement. Moreover, the two spatial degrees of freedom each carry non-commuting position and momentum variables forming a Heisenberg algebra like quantum particles moving in a two dimensional space. An analogy between the dynamics of a quantum harmonic oscillator and paraxial light propagating through a converging lens is drawn. This article presents a modern formulation of optics in the language of state vectors and operators (based on Dirac’s notation) along the lines of an earlier contribution [1], specifying and explaining its results.
A bi-level metric learning framework via self-paced learning weighting

Distance metric learning (DML) has achieved great success in many real-world applications. However, most existing DML models characterize the quality of tuples on the tuple level while ignoring the anchor level. Therefore, the models are less accurate to portray the quality of tuples and tend to be over-fitting when anchors are noisy samples. In this paper, we devise a bi-level metric learning framework (BMLF), which characterizes the quality of tuples more finely on both levels, enhancing the generalization performance of the DML model. Furthermore, we present an implementation of BMLF based on a self-paced learning regular term and design the corresponding optimization algorithm. By weighing tuples on the anchor level and training the model using tuples with higher weights preferentially, the side effect of low-quality noisy samples will be alleviated. We empirically demonstrate that the effectiveness and robustness of the proposed method outperform the state-of-the-art methods on several benchmark datasets.
Global and Bipartite Entanglement for Three-Qubit System Local Unitary Classes

Abstract Entanglement of multipartite quantum systems is an important resource in the field of quantum information processing. Using a measure of global entanglement and the notion of concurrence, the properties of entanglement for a three-qubit system are studied and classified in terms of local unitary classes. The connection of such classification with the three-qubit entanglement polytope is also discussed.
A sparse convolution neural network accelerator with bandwidth-efficient data loopback structure

Convolutional neural network has become one of the mainstream algorithms of deep learning with high computational resource consumption. The model inferencing delay and power consumption can be effectively reduced by the sparsity-aware accelerator with low resource overhead and almost lossless accuracy. However, the computation fragmentation problem caused by sparse operations will greatly reduce convolution operation efficiency. In order to alleviate the above problems, an input channel expansion method is proposed to improve the resource utilization rate in this paper. Considering that bandwidth often becomes the bottleneck of accelerator performance, a bandwidth-efficient data loopback structure is designed to reduce data transmission between accelerator and off-chip memory. The proposed hardware architecture is implemented on Xilinx VC709. It contains up to 1024 multiplication and accumulation units, providing 409.6 GOP/s peak computing power. Its computation speed reaches 315.8 GOP/s in VGG-16 model, which is equivalent to 788 GOP/s of accelerator without sparse activation optimization. 54.2% of activation data transmission is canceled through data loopback bus, which eases the dependence on off-chip bandwidth. This flexible sparsity-aware accelerator architecture can be widely applied to deep convolutional neural networks for large-scale inferencing.
Classification and variable selection using the mining of positive and negative association rules

Association rules (ARs) have been applied to classification and variable selection. However, currently, only positive ARs are used for variable selection, while only special forms of positive and negative association rules (PNARs) are used for classification. The purpose of this work was to investigate variable selection and classification methods by mining another, more general form of PNARs, one that is more suitable for binary classification and variable selection problems. The algorithm for mining such PNARs exploits the downward closure property of negative itemsets. It is built based solely on items in a transactional database and on equivalence classes under the support–confidence framework. The algorithm combines the process of mining frequent itemsets and rule generation and is both sound and complete. Experimental results on 10 binary datasets of the variable selection and classification methods using the PNARs mined by the proposed algorithm show that these methods are superior to variable selection methods that use the mutual information measure and the chi-squared test and 10 popular classification algorithms, respectively.