Reviewerly/exHarmony huggingface dataset:
Video Domain Adaptation based on Optimal Transport in Grassmann Manifolds

Domain adaptation is a fundamental research field, which focuses on transforming knowledge between different domains. With the massive growth of video data, the video domain adaptation problem becomes increasingly significant for practical tasks. Motivated by the excellent performance of Grassmann manifolds representation in video recognition tasks, we propose an optimal transport based video domain adaptation model on Grassmann manifolds. The proposed model reduces the discrepancy between different domains for the frame and video level features. First, the frame level discrepancy is reduced by extracting domain consistency features. At the video level, a fixed number of frame features are formed and represented as points on Grassmann manifolds. These points are fused with predicted labels to form fusion features. Finally, the video level discrepancy is reduced by minimizing the distribution discrepancy of the fusion features between two domains. Cross-domain video recognition experiments demonstrate the validity of the proposed model. The experimental results demonstrate the excellent performance of the proposed algorithm compared with the state-of-art video domain adaptation models.
Facial Expression Recognition Through Cross-Modality Attention Fusion

Facial expressions are generally recognized based on handcrafted and deep-learning-based features extracted from RGB facial images. However, such recognition methods suffer from illumination/pose variations. In particular, they fail to recognize these expressions with weak emotion intensities. In this work, we propose a cross-modality attention-based convolutional neural network (CM-CNN) for facial expression recognition. We extract expression-related features from complementary facial images (gray-scale, local binary pattern, and depth images) to handle the illumination/pose variations and to capture appearance details that describe expressions with weak emotion intensities. Rather than directly concatenating the complementary features, we propose a novel cross-modality attention fusion network to enhance spatial correlations between any two types of facial images. Finally, the CM-CNN is optimized with an improved focal loss, which pays more attention to facial expressions with weak emotion intensities. The average classification accuracies on VT-KFER, BU-3DFE(P1), BU-3DFE(P2), and Bosphorus are 93.86%, 88.91%, 87.28%, and 85.16%, respectively. Evaluations on these databases demonstrate that our approach is competitive to state-of-the-art algorithms.
Incremental learning algorithm for large-scale semi-supervised ordinal regression

As a special case of multi-classification, ordinal regression (also known as ordinal classification) is a popular method to tackle the multi-class problems with samples marked by a set of ranks. Semi-supervised ordinal regression (SSOR) is especially important for data mining applications because semi-supervised learning can make use of the unlabeled samples to train a high-quality learning model. However, the training of large-scale SSOR is still an open question due to its complicated formulations and non-convexity to the best of our knowledge. To address this challenging problem, in this paper, we propose an incremental learning algorithm for SSOR (IL-SSOR), which can directly update the solution of SSOR based on the KKT conditions. More critically, we analyze the finite convergence of IL-SSOR which guarantees that SSOR can converge to a local minimum based on the framework of concave-convex procedure. To the best of our knowledge, the proposed new algorithm is the first efficient on-line learning algorithm for SSOR with local minimum convergence guarantee. The experimental results show, IL-SSOR can achieve better generalization than other semi-supervised multi-class algorithms. Compared with other semi-supervised ordinal regression algorithms, our experimental results show that IL-SSOR can achieve similar generalization with less running time.
Exploring the Benefits of Resource Disaggregation for Service Reliability in Data Centers

By overcoming the “server box” barrier, resource disaggregation in data centers (DCs) can significantly improve resource utilization. This may then provide a more cost-efficient approach for resource upgrade and expansion. The advantages of resource disaggregation have been explored in earlier research to improve the efficiency of resource usage. This paper investigates the potential benefits of resource disaggregation from the aspect of reliability, which has not been considered before. Resource disaggregation gives rise to a new failure pattern. For example, in a conventional server, the failure of one type of resource leads to the failure of the entire server, so that other types of resources in the same server also become unavailable. After disaggregating, the failure of different types of resources becomes more isolated so that other resources are still available. In this paper, we model the reliability of a resource allocation request in a server-based or disaggregated DC based on whether the request is allocated with only working resources or is also provisioned with backup resources. We then consider a resource allocation problem to maximize the number of requests accepted with guaranteed reliability. This is formulated as an integer linear programming (ILP) problem, and a more straightforward heuristic approach is also proposed. Our numerical studies demonstrate that it may be possible to significantly improve service reliability with this resource disaggregation approach.
File Text Recognition and Management System Based on Tesseract-OCR

Through the research of image preprocessing technology, this paper designs and implements a web archive file recognition management system based on open source Tesseract character recognition technology. The system first preprocesses the image with grayscale and binarization. Secondly, in order to improve the recognition accuracy of handwritten content, we trained the text recognition library of Tesseract. Finally, the characters are recognized and stored for later use. Archivists can use this system to convert paper documents into electronic documents, which can significantly improve the management level and digital efficiency of the file system.
Stablecoins versus traditional cryptocurrencies in response to interbank rates

This study investigates the impacts of the United States (US) federal funds rate and Chinese interbank rate on the behaviors of stablecoins and traditional cryptocurrencies. We employ GARCH, EGARCH and Fixed Effects models for the daily sample of the top five stablecoins and the top five traditional cryptocurrencies in terms of market capitalization from December 2018 to December 2019. Our results show that a higher federal funds rate and Chinese interbank rate compress the prices and price volatility of stablecoins. In contrast, higher rates from both countries increase the prices and price volatility of traditional cryptocurrencies. Both rates also increase the trading value of both types of coins. The federal funds rate tends to have stronger impacts compared to the Chinese interbank rate on both types of coins, except for the price response of traditional cryptocurrencies. JEL: E43; E52; G10; F40
Object detection based on Yolov4-Tiny and Improved Bidirectional feature pyramid network

Abstract In the field of small object detection, Yolov4-Tiny is inadequate in feature extraction and does not make best of multi-scale features. In this paper, an improved BiFPN framework is proposed based on Yolov4-Tiny to increase object detection precision. Moreover, the Yolov4-Tiny is taken as the backbone network and introduce spatial pyramid pooling (SPP) to connect and merge multi-scale regions. Finally, our method can achieve 79.53% map on Pascal VOC dataset, which is 2.12% higher than the original Yolov4-Tiny model.
‘Pushing’ keys through quantum networks and the complexity of search of the true key

Abstract The question of the transfer of an external independent key through trusted nodes, between which the keys obtained during the quantum key distribution are located, is investigated. It is shown that when pushing external ɛ K -secret key through a chain of trusted nodes connecting separate segments with ɛ i -secret keys obtained by quantum key distribution, the final matched key between the extreme nodes of the network turns out to be <?CDATA $\varepsilon = \varepsilon_K+\sum_{i = 1}^{N_s}\varepsilon_i$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>ε</mml:mi> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>ε</mml:mi> <mml:mi>K</mml:mi> </mml:msub> <mml:mo>+</mml:mo> <mml:mrow> <mml:munderover> <mml:mo>∑</mml:mo> <mml:mrow> <mml:mi>i</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:msub> <mml:mi>N</mml:mi> <mml:mi>s</mml:mi> </mml:msub> </mml:mrow> </mml:munderover> </mml:mrow> <mml:msub> <mml:mi>ε</mml:mi> <mml:mi>i</mml:mi> </mml:msub> </mml:math> -secret (where i is the number of the network segment, N s is the number of network segments connecting the extreme nodes), provided that the external key is encrypted on the ɛ i –secret keys in the one-time pad mode. It is also shown that for any measurements of an eavesdropper providing him with additional information about the keys, the complexity of partial/complete search for an matched key is determined by the secrecy parameter ɛ .
Responsibility as a Key Competence of the 21st Century Engineer

The growing anthropogenic impact on the environment, the consequences of which are often written and spoken of as frightening, even apocalyptic, has actualized the discourse on responsibility – of humanity as a whole, states, their governments, parliaments and other institutions, individual corporations, each person. The environmental agenda, responsible nature management, ecosystem thinking are at the center of their conflicting interests. It seems that engineering education should also focus on this issue. However, when analyzing the Federal State Educational Standards, it is found that the development of social and professional responsibility of students is not recorded as a necessary result of their training. Recently, professional standards have become a benchmark for higher education in terms of regulating the professional competencies of university graduates. The professional standards should define the level of employee’s responsibility, which serves as a defining characteristic of the qualification. Thus, there is a discrepancy between FSES and professional standards iregarding one of the key engineering competencies. This article focuses on this topic.
Online Projector Deblurring Using a Convolutional Neural Network

Projector deblurring is an important technology for dynamic projection mapping (PM), where the distance between a projector and a projection surface changes in time. However, conventional projector deblurring techniques do not support dynamic PM because they need to project calibration patterns to estimate the amount of defocus blur each time the surface moves. We present a deep neural network that can compensate for defocus blur in dynamic PM. The primary contribution of this paper is a unique network structure that consists of an extractor and a generator. The extractor explicitly estimates a defocus blur map and a luminance attenuation map. These maps are then injected into the middle layers of the generator network that computes the compensation image. We also propose a pseudo-projection technique for synthesizing physically plausible training data, considering the geometric misregistration that potentially happens in actual PM systems. We conducted simulation and actual PM experiments and confirmed that: (1) the proposed network structure is more suitable than a simple, more general structure for projector deblurring; (2) the network trained with the proposed pseudo-projection technique can compensate projection images for defocus blur artifacts in dynamic PM; and (3) the network supports the translation speed of the surface movement within a certain range that covers normal human motions.
Graph embeddings in criminal investigation: towards combining precision, generalization and transparency

Abstract Criminal investigation adopts Artificial Intelligence to enhance the volume of the facts that can be investigated and documented in trials. However, the abstract reasoning implied in legal justification and argumentation requests to adopt solutions providing high precision, low generalization error, and retrospective transparency. Three requirements that hardly coexist in today’s Artificial Intelligence solutions. In a controlled experiment, we then investigated the use of graph embeddings procedures to retrieve potential criminal actions based on patterns defined in enquiry protocols. We observed that a significant level of accuracy can be achieved but different graph reformation procedures imply different levels of precision, generalization, and transparency.
Calibrating CNNs for Few-Shot Meta Learning

Although few-shot meta learning has been extensively studied in machine learning community, the fast adaptation towards new tasks remains a challenge in the few-shot learning scenario. The neuroscience research reveals that the capability of evolving neural network formulation is essential for task adaptation, which has been broadly studied in recent meta-learning researches. In this paper, we present a novel forward-backward meta-learning framework (FBM) to facilitate the model generalization in few-shot learning from a new perspective, i.e., neuron calibration. In particular, FBM models the neurons in deep neural network-based model as calibrated units under a general formulation, where neuron calibration could empower fast adaptation capability to the neural network-based models through influencing both their forward inference path and backward propagation path. The proposed calibration scheme is lightweight and applicable to various feed-forward neural network architectures. Extensive empirical experiments on the challenging few-shot learning benchmarks validate that our approach training with neuron calibration achieves a promising performance, which demonstrates that neuron calibration plays a vital role in improving the few-shot learning performance.
A multi-voter multi-commission nearest neighbor classifier

Many improved versions of k-nearest neighbor (KNN) have been proposed by minimizing total distances of multi k nearest neighbors (multi-voter) in each class instead of the majority voting, such as a local mean-based pseudo nearest neighbor (LMPNN) that give a better decision. In this paper, a new KNN variant called multi-voter multi-commission nearest neighbor (MVMCNN) is proposed to examine its benefits in enhancing the LMPNN. As the name suggests, MVMCNN uses some commissions: each calculates the total distance between the given query point (test pattern) and k pseudo nearest neighbors using the LMPNN scheme. The decision class is defined by minimizing those total distances. Hence, the decision in MVMCNN is obtained more locally than LMPNN. Examination based on 10-fold cross-validation shows that the proposed multi-commission scheme can enhance the original (single-commission) LMPNN. Compared with two single-voter models: KNN and Bonferroni Mean Fuzzy k-Nearest Neighbors (BM-FKNN), the proposed MVMCNN also gives lower mean error rates as well as higher Precision, Recall, and F1 Score, indicating that the multi-voter model provides a better decision than the single-voter ones.
Batch Normalization Tells You Which Filter is Important

The goal of filter pruning is to search for unimportant filters to remove in order to make convolutional neural networks (CNNs) efficient without sacrificing the performance in the process. The challenge lies in finding information that can help determine how important or relevant each filter is with respect to the final output of neural networks. In this work, we share our observation that the batch normalization (BN) parameters of pre-trained CNNs can be used to estimate the feature distribution of activation outputs, without processing of training data. Upon observation, we propose a simple yet effective filter pruning method by evaluating the importance of each filter based on the BN parameters of pre-trained CNNs. The experimental results on CIFAR-10 and ImageNet demonstrate that the proposed method can achieve outstanding performance with and without fine-tuning in terms of the trade-off between the accuracy drop and the reduction in computational complexity and number of parameters of pruned networks.
Ethical Issues in Software Requirements Engineering

Context: Ethics have broad applications in different fields of study and different contexts. Like other fields of study, ethics have a significant impact on the decisions made in computing concerning software artifact production and its processes. Hence, in this research, ethics is considered in the context of requirements engineering during the software development process. Objective: The aim of this paper is to discuss the investigation results regarding ethical problems of requirements engineering processes by taking sample software developing companies and exposing existing research gaps. Method: This research uses interviewing, focus group discussions, purposive sampling, and qualitative analysis research methods. Result: This research finds an absence of industry practices, professional responsibility code of conduct standards, and other guidelines within companies when integrating ethical concerns of software during requirements engineering. It also indicates that almost all companies have no identification methods and checking mechanisms for ethical concern considerations. Furthermore, the major identified ethical concerns are classified into six categories as requirements identification problems, quality-related problems, carrying out unpermitted activities, unwillingness to give requirements, knowledge gaps and lack of legal grounds/rules for accountability. Conclusion: From the findings of this research, it can be concluded that, in the case software companies, there is no specific method for identifying ethical concerns. Additionally, there are no standards and guidelines used within the companies. This implies the need to overcome the existing and emerging ethical issues of requirements engineering.
Securing Internet of Drones Networks Using AI-Envisioned Smart-Contract-Based Blockchain

Drones, sometimes called unmanned aerial vehicles (UAVs), can be deployed in a flying Internet of Things (IoT)/Internet of Drones (IoD) environment to execute some specific tasks, like environmental monitoring, disaster management, aerial photography, monitoring and tracking of enemies at borders, and many more. For security reasons, the deployed drones can sense and collect the data from their surroundings, and then securely send the information to the ground station server. The ground station server then provides the collected data to the peer-to-peer cloud server (P2PCS) network after converting them into encrypted transactions in a secure way. Finally, the blocks are created from the encrypted transactions and added into a blockchain by applying consensus algorithms implemented by the P2PCS network. The deployed artificial intelligence (AI)-based big data analytics is required to predict the useful results from the collected and processed data. In this article, we propose a novel AI-envisioned smart-contract-based blockchain-enabled security framework for secure communication in IoD. The provided security analysis proves the security of the proposed framework against different potential attacks. The blockchain implementation of the proposed framework is then executed to identify its impact on the performance of the system.
Image-Adaptive Hint Generation via Vision Transformer for Outpainting

Image outpainting has recently received considerable attention because it can be useful in tasks such as image retargeting and panorama image generation. In general, the problem of extending an image beyond its given boundaries is still ill-posed. Conventional methods predominantly attempt image outpainting by using complex network structures. Some recent studies have tried to decrease the problem complexity through the conversion techniques from outpainting to inpainting. Although these methodologies work well in simple cases, their performance reduces considerably for asymmetrical images. This paper proposes a novel hint-based outpainting methodology that can adaptively select the most plausible patches as hints from a given image to reduce the difficulty of outpainting. To estimate high-quality hints, inspired by patch-based image inpainting methods, we utilize Vision Transformer that considers self-attention for each patch. The estimated hints are attached on both boundaries of the input image and the inside missing regions are predicted by using an inpainting network. After finishing the prediction, the output image is obtained by removing the hints. Experiments show that our image-adaptive hint framework, when employed in representative inpainting networks, can consistently improve its performance compared to the other conversion techniques from outpainting to inpainting on SUN and Beach benchmark datasets.
Ontology Development Methodology: A Systematic Review and Case Study

Ontology development methodologies are widely used to guide the process of constructing, deploying and maintaining ontologies. Several methodologies exist for this purpose, meaning the ontology developer must choose a suitable methodology. The process of choosing the best or most suitable methodology can itself be confusing and daunting to ontology developers. This paper aims to present and compare the five most representative existing methodologies of ontology development and provide an assessment that can be referenced by developers and researchers. Furthermore, this paper includes a case study in which an ontology is developed for the Department of Information Technology at the University of Tabuk using the Simple Knowledge-Engineering Methodology; this ontology is known as TITO. We propose future work that includes evaluating TITO using state of the art techniques such as competency questions and domain experts, and extending TITO to include the other departments.
Perils of embedding for quantum sampling

Given quantum hardware that enables sampling from a family of natively implemented Hamiltonians, how well can one use that hardware to sample from a Hamiltonian outside that family? A common approach is to minor embed the desired Hamiltonian in a native Hamiltonian. In Phys. Rev. Research 2, 023020 (2020) it was shown that minor embedding can be detrimental for classical thermal sampling. Here, we generalize these results by considering quantum thermal sampling in the transverse-field Ising model, i.e. sampling a Hamiltonian with non-zero off diagonal terms. To study these systems numerically we introduce a modification to standard cluster update quantum Monte-Carlo (QMC) techniques, which allows us to much more efficiently obtain thermal samples of an embedded Hamiltonian, enabling us to simulate systems of much larger sizes and larger transverse-field strengths than would otherwise be possible. Our numerics focus on models that can be implemented on current quantum devices using planar two-dimensional lattices, which exhibit finite-temperature quantum phase transitions. Our results include: i) An estimate on the probability to sample the logical subspace directly as a function of transverse-field, temperature, and total system size, which agrees with QMC simulations. ii) We show that typically measured observables (diagonal energy and magnetization) are biased by the embedding process, in the regime of intermediate transverse field strength, meaning that the extracted values are not the same as in the native model. iii) By considering individual embedding realizations akin to 'realizations of disorder', we provide numerical evidence suggesting that as the embedding size is increased, the critical point shifts to increasingly large values of the transverse-field.