Reviewerly/exHarmony huggingface dataset:
Dynamic Facial Expression Recognition Under Partial Occlusion With Optical Flow Reconstruction

Video facial expression recognition is useful for many applications and received much interest lately. Although some methods give good results in controlled environments (no occlusion), recognition in the presence of partial facial occlusion remains a challenging task. To handle facial occlusions, methods based on the reconstruction of the occluded part of the face have been proposed. These methods are mainly based on the texture or the geometry of the face. However, the similarity of the face movement between different persons doing the same expression seems to be a real asset for the reconstruction. In this paper we exploit this asset and propose a new method based on an auto-encoder with skip connections to reconstruct the occluded part of the face in the optical flow domain. To the best of our knowledge, this is the first work that directly reconstructs the movement for facial expression recognition. We validated our approach in the controlled CK+ datasets on which different occlusions were generated. Our experiments show that the proposed method reduces the gap in the recognition accuracy between occluded and unoccluded situations. We also compare our approach with existing state-of-the-art approaches. In order to lay the basis of a reproducible and fair comparison in the future, we also propose a new experimental protocol that includes occlusion generation and reconstruction evaluation.
Maximal Steered Coherence and Its Conversion to Entanglement in Multiple Bosonic Reservoirs

The remote control of coherence is a crucial step for its application in quantum computation. We investigate the maximal steered coherence (MSC) and its conversion to entanglement in multiple bosonic reservoirs. It is shown that the MSC decays with time in the Markovian regime and behaves as damped oscillations in the non-Markovian regime. The MSC can also be connected directly to the strength of non-Markovianity, through which we show that it can be noticeably enhanced by taking full advantage of the non-Markovian effects. Besides, the MSC can be converted completely to entanglement via optimal incoherent operation applied to the steered qubit and an incoherent ancilla which is immune of decoherence, and the generated entanglement is stronger than the shared entanglement in prior. These findings suggest a potential way for remotely generating and manipulating coherence and entanglement in noisy environments.
Robust Data Association Using Fusion of Data-Driven and Engineered Features for Real-Time Pedestrian Tracking in Thermal Images

Object tracking is an essential problem in computer vision that has been extensively researched for decades. Tracking objects in thermal images is particularly difficult because of the lack of color information, low image resolution, or high similarity between objects of the same class. One of the main challenges in multi-object tracking, also referred to as the data association problem, is finding the correct correspondences between measurements and tracks and adapting the object appearance changes over time. We addressed this challenge of data association for thermal images by proposing three contributions. The first contribution consisted of the creation of a data-driven appearance score using five Siamese Networks, which operate on the image detection and on parts of it. Secondly, we engineered an original edge-based descriptor that improves the data association process. Lastly, we proposed a dataset consisting of pedestrian instances that were recorded in different scenarios and are used for training the Siamese Networks. The data-driven part of the data association score offers robustness, while feature engineering offers adaptability to unknown scenarios and their combination leads to a more powerful tracking solution. Our approach had a running time of 25 ms and achieved an average precision of 86.2% on publicly available benchmarks, containing real-world scenarios, as shown in the evaluation section.
Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic

The COVID-19 pandemic has challenged many educational institutions around the world in 2020 and 2021 as traditional education has been interrupted to prevent the spread of the virus. This forced the transition from traditional education to fully distance learning envi-ronments for all levels of education. The widespread adoption of distance learning has led instructors to form new digital learning environments and methods. In response to this unexpected situation, data regarding engineering students and their interaction with the learning environment was accumulated and processed, generating a matrix of 129 × 165 variables. The motivation for this research is to identify new variables that impact student performance during the disorientation of the educational process due to the COVID-19 pandemic. Statistical analysis was performed and discussed in this paper including correla-tion analysis, factor analysis, and clustering. Reliability analysis was also performed and ANOVA (analysis of variance) was applied to clusters. The novelty of this work is to use student performance data and statistical analysis of online surveys to reveal patterns that can help reduce dropout rates and transform the educational process, under extenuating and imposed distance learning circumstances. A major finding is that by applying innovative teaching methods, thereby meeting the challenge of an imposed distance learning environ-ment, students' spatial conceptions improve, overcoming the absence of a physical learning space. Deep insights for individual students were discovered, as well as significant relation-ships between students' transition from secondary to higher education and their understand-ing of geometric features. Evidence of the effectiveness of the online learning framework that was integrated showed that it positively influenced students' learning styles.
Human dynamics from monocular video with dynamic camera movements

We propose a new method that reconstructs 3D human motion from in-the-wild video by making full use of prior knowledge on the laws of physics. Previous studies focus on reconstructing joint angles and positions in the body local coordinate frame. Body translations and rotations in the global reference frame are partially reconstructed only when the video has a static camera view. We are interested in overcoming this static view limitation to deal with dynamic view videos. The camera may pan, tilt, and zoom to track the moving subject. Since we do not assume any limitations on camera movements, body translations and rotations from the video do not correspond to absolute positions in the reference frame. The key technical challenge is inferring body translations and rotations from a sequence of 3D full-body poses, assuming the absence of root motion. This inference is possible because human motion obeys the law of physics. Our reconstruction algorithm produces a control policy that simulates 3D human motion imitating the one in the video. Our algorithm is particularly useful for reconstructing highly dynamic movements, such as sports, dance, gymnastics, and parkour actions.
Cross-modal complementary network with hierarchical fusion for multimodal sentiment classification

Multimodal Sentiment Classification (MSC) uses multimodal data, such as images and texts, to identify the users' sentiment polarities from the information posted by users on the Internet. MSC has attracted considerable attention because of its wide applications in social computing and opinion mining. However, improper correlation strategies can cause erroneous fusion as the texts and the images that are unrelated to each other may integrate. Moreover, simply concatenating them modal by modal, even with true correlation, cannot fully capture the features within and between modals. To solve these problems, this paper proposes a Cross-Modal Complementary Network (CMCN) with hierarchical fusion for MSC. The CMCN is designed as a hierarchical structure with three key modules, namely, the feature extraction module to extract features from texts and images, the feature attention module to learn both text and image attention features generated by an image-text correlation generator, and the cross-modal hierarchical fusion module to fuse features within and between modals. Such a CMCN provides a hierarchical fusion framework that can fully integrate different modal features and helps reduce the risk of integrating unrelated modal features. Extensive experimental results on three public datasets show that the proposed approach significantly outperforms the state-of-the-art methods.
Factors That Influence Virtual Tourism Holistic Image: The Moderating Role of Sense of Presence

The COVID-19 pandemic has dramatically restricted domestic and foreign travel; thus, virtual tourism has become a new option for traveling. As regards virtual tourism, the present study seeks to investigate the effect of tourist involvement on the three components of holistic image, namely cognitive, affective, and conative images; the influence of cognitive, affective, and conative images on the construction of the holistic image; and the moderating effect of sense of presence on the relationships between cognitive, affective, and conative images on the holistic image. Prospective tourists were chosen as research subjects in the present study on non-immersive virtual reality (VR) tourism, or virtual tourism. Participants first watched the 360° VR tour video of AirPano. Afterward, they filled out an online questionnaire, of which 386 valid samples were collected for analyses. Results show that tourist involvement considerably affects cognitive, affective, and conative images, which significantly influence the holistic image. Furthermore, a sense of presence was found to positively moderate the relationships between (1) cognitive and holistic, (2) affective and holistic, and (3) conative and holistic images. These results may allow tourism marketing organizations to better understand how to strengthen the holistic image in the context of virtual tourism.
Evaluating system usability of mobile augmented reality application for teaching Karnaugh-Maps

Abstract The education system evolves and transforms towards interactive and immersive learning tools in this digital age. Augmented reality has also evolved as a ubiquitous, robust, and effective technology for providing innovative educational tools. In engineering education, many abstract concepts require technological intervention for conceptual understanding and better instructional content. While learning through the immersive tools, system usability has great importance in terms of effectiveness, efficiency, and satisfaction. Effectiveness refers to users' accuracy and completeness in achieving defined goals; efficiency relates to expended resources about the precision and completeness with which users achieve their objectives; satisfaction deals with a positive attitude towards using the product. If the system fails to provide good usability, it may cause adverse effects such as increasing stress, lacking necessary features, increasing the users' cognitive load, and negatively impacting the student's motivation. In this study, two mobile augmented reality (MAR) applications were developed as an instructional tool to teach the students about Karnaugh maps in the digital electronics course. The first application is a Keypad-based MAR application that uses a keypad matrix for user interaction and the second application is a Marker-based MAR application that uses multiple markers to solve K-Map for producing an optimum solution of the given problem. An experimental study was conducted to determine the student's opinion of the developed MAR applications. The study was designed to determine the system usability of the two MAR applications using the System Usability Score (SUS) and Handheld Augmented Reality Usability Score (HARUS) models. 90 engineering students participated in the study, and they were randomly divided into two different groups: keypad-based group and Marker-based group. The keypad-based group included 47 students who had hands-on experience with a keypad-based MAR application, whereas the marker-based group included 43 students who had hands-on experience with multiple marker-based MAR applications. The experimental outcomes indicated that the keypad-based MAR application has better SUS and HARUS scores than the marker-based MAR application which suggests that the keypad-based MAR application has provided better user interaction.
Multi-channel convolutional neural network architectures for thyroid cancer detection

Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations due to unreliable human false-positive diagnostic rates. With the emergence of deep learning, advances in computer-aided diagnosis techniques have yielded promising earlier detection and prediction accuracy; however, clinicians’ adoption is far lacking. The present study adopts Xception neural network as the base structure and designs a practical framework, which comprises three adaptable multi-channel architectures that were positively evaluated using real-world data sets. The proposed architectures outperform existing statistical and machine learning techniques and reached a diagnostic accuracy rate of 0.989 with ultrasound images and 0.975 with computed tomography scans through the single input dual-channel architecture. Moreover, the patient-specific design was implemented for thyroid cancer detection and has obtained an accuracy of 0.95 for double inputs dual-channel architecture and 0.94 for four-channel architecture. Our evaluation suggests that ultrasound images and computed tomography (CT) scans yield comparable diagnostic results through computer-aided diagnosis applications. With ultrasound images obtained slightly higher results, CT, on the other hand, can achieve the patient-specific diagnostic design. Besides, with the proposed framework, clinicians can select the best fitting architecture when making decisions regarding a thyroid cancer diagnosis. The proposed framework also incorporates interpretable results as evidence, which potentially improves clinicians’ trust and hence their adoption of the computer-aided diagnosis techniques proposed with increased efficiency and accuracy.
Blockchain technology adoption in government organizations: a systematic literature review

Purpose Blockchain technology is spreading its roots and growing rapidly. With the advancement of blockchain technology, government organizations are focusing toward adopting blockchain technology. The purpose of this study is to identify the factors that affect the adoption of blockchain technology and provide a conceptual framework to adopt blockchain technology in government organizations. Design/methodology/approach To answer the research questions, this study has used systematic literature review (SLR). For achieving the quality and transparent process, this study has applied the PRISMA framework in the SLR. Findings With the help of SLR, this study has identified 72 factors that influence the adoption of blockchain technology. After critically examining the factors, this study has developed the conceptual framework for the adoption of blockchain technology in government organizations with four factors of the unified theory of acceptance and use of technology model and four additional factors, i.e. facilitating conditions, social influence, effort expectancy, performance expectancy, trust, transparency, cost and security. Research limitations/implications To perform SLR, this study has used two databases: Web of Science and Google Scholar; future studies can consider other databases. This study has provided the framework but not validated it; future studies can use structural equation modeling and artificial neural network to validate the framework. Practical implications This study helps to provide insights about blockchain technology and help government and policymakers to take decisions for the adoption of blockchain technology and in determining the future action plan. Originality/value Few studies are available in the literature that have used SLR to identify blockchain technology adoption in government organizations.
Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19

COVID-19 is a virus, which is transmitted through small droplets during speech, sneezing, coughing, and mostly by inhalation between individuals in close contact. The pandemic is still ongoing and causes people to have an acute respiratory infection which has resulted in many deaths. The risks of COVID-19 spread can be eliminated by avoiding physical contact among people. This research proposes real-time AI platform for people detection, and social distancing classification of individuals based on thermal camera. YOLOv4-tiny is proposed in this research for object detection. It is a simple neural network architecture, which makes it suitable for low-cost embedded devices. The proposed model is a better option compared to other approaches for real-time detection. An algorithm is also implemented to monitor social distancing using a bird's-eye perspective. The proposed approach is applied to videos acquired through thermal cameras for people detection, social distancing classification, and at the same time measuring the skin temperature for the individuals. To tune up the proposed model for individual detection, the training stage is carried out by thermal images with various indoor and outdoor environments. The final prototype algorithm has been deployed in a low-cost Nvidia Jetson devices (Xavier and Jetson Nano) which are composed of fixed camera. The proposed approach is suitable for a surveillance system within sustainable smart cities for people detection, social distancing classification, and body temperature measurement. This will help the authorities to visualize the fulfillment of the individuals with social distancing and simultaneously monitoring their skin temperature.
Secure key distribution and synchronization method in an OFDM-PON based on chaos

A physical layer key distribution scheme based on chaotic encryption and signal synchronization is proposed in this paper, which can achieve secure key distribution and enhance the security of an orthogonal frequency division multiplexing based passive optical network (OFDM-PON). The key is embedded into the synchronization header and then encrypted by using chaos. The receiver needs to utilize the correct chaotic parameters to successfully decrypt the synchronization information and extract the key. An experiment is conducted to verify the availability of this method by setting key sequences of various length over different transmission distances. The signals of 35.29 Gb/s are successfully transmitted over 5 km, 15 km and 25 km standard single-mode fiber (SSMF), respectively. It is proved that the proposed scheme is feasible and compatible with the traditional encryption algorithms, and it has almost no effect on the synchronization performance, which can then distribute keys with the sending signals without occupying additional channel resources and enhance the security performance of OFDM-PON simultaneously.
Mask-invariant Face Recognition through Template-level Knowledge Distillation

The emergence of the global COVID-19 pandemic poses new challenges for biometrics. Not only are contactless biometric identification options becoming more important, but face recognition has also recently been confronted with the frequent wearing of masks. These masks affect the performance of previous face recognition systems, as they hide important identity information. In this paper, we propose a mask-invariant face recognition solution (MaskInv) that utilizes template-level knowledge distillation within a training paradigm that aims at producing embeddings of masked faces that are similar to those of non-masked faces of the same identities. In addition to the distilled knowledge, the student network benefits from additional guidance by margin-based identity classification loss, ElasticFace, using masked and non-masked faces. In a step-wise ablation study on two real masked face databases and five mainstream databases with synthetic masks, we prove the rationalization of our MaskInv approach. Our proposed solution outperforms previous state-of-the-art (SOTA) academic solutions in the recent MFRC-21 challenge in both scenarios, masked vs masked and masked vs non-masked, and also outperforms the previous solution on the MFR2 dataset. Furthermore, we demonstrate that the proposed model can still perform well on unmasked faces with only a minor loss in verification performance. The code, the trained models, as well as the evaluation protocol on the synthetically masked data are publicly available: https://github.com/fdbtrs/Masked-Face-Recognition-KD.
What are weak links in the npm supply chain?

Modern software development frequently uses third-party packages, raising the concern of supply chain security attacks. Many attackers target popular package managers, like npm, and their users with supply chain attacks. In 2021 there was a 650% year-on-year growth in security attacks by exploiting Open Source Software's supply chain. Proactive approaches are needed to predict package vulnerability to high-risk supply chain attacks. The goal of this work is to help software developers and security specialists in measuring npm supply chain weak link signals to prevent future supply chain attacks by empirically studying npm package metadata.
Duel of the Titans: The Romulus and Remus Families of Lightweight AEAD Algorithms

In this article, we propose two new families of very lightweight and efficient authenticated encryption with associated data (AEAD) modes, Romulus and Remus, that provide security beyond the birthday bound with respect to the block-length n. The former uses a tweakable block cipher (TBC) as internal primitive and can be proven secure in the standard model. The later uses a block cipher (BC) as internal primitive and can be proven secure in the ideal cipher model. Both our modes allow to switch very easily from the nonce-respecting to the nonce-misuse scenario.Previous constructions, such as ΘCB3, are quite computationally efficient, yet needing a large memory for implementation, which makes them unsuitable for platforms where lightweight cryptography should play a key role. Romulus and Remus break this barrier by introducing a new architecture evolved from a BC mode COFB. They achieve the best of what can be possible with TBC – the optimal computational efficiency (rate-1 operation) and the minimum state size of a TBC mode (i.e., (n + t)-bit for n-bit block, t-bit tweak TBC), with almost equivalent provable security as ΘCB3. Actually, our comparisons show that both our designs present superior performances when compared to all other recent lightweight AEAD modes, being BC-based, TBC-based or sponge-based, in the nonce-respecting or nonce-misuse scenario. We eventually describe how to instantiate Romulus and Remus modes using the Skinny lightweight tweakable block cipher proposed at CRYPTO 2016, including the hardware implementation results