Reviewerly/exHarmony huggingface dataset:
Conformer and Blind Noisy Students for Improved Image Quality Assessment

Generative models for image restoration, enhancement, and generation have significantly improved the quality of the generated images. Surprisingly, these models produce more pleasant images to the human eye than other methods, yet, they may get a lower perceptual quality score using traditional perceptual quality metrics such as PSNR or SSIM. Therefore, it is necessary to develop a quantitative metric to reflect the performance of new algorithms, which should be well-aligned with the person’s mean opinion score (MOS).Learning-based approaches for perceptual image quality assessment (IQA) usually require both the distorted and reference image for measuring the perceptual quality accurately. However, commonly only the distorted or generated image is available. In this work, we explore the performance of transformer-based full-reference IQA models. We also propose a method for IQA based on semi-supervised knowledge distillation from full-reference teacher models into blind student models using noisy pseudo-labeled data.Our approaches achieved competitive results on the NTIRE 2022 Perceptual Image Quality Assessment Challenge: our full-reference model was ranked 4th, and our blind noisy student was ranked 3rd among 70 participants, each in their respective track. https://github.com/burchim/IQA-Conformer-BNS.
An Experimental Survey of Missing Data Imputation Algorithms

Due to the ubiquity of missing data, data imputation has received extensive attention in the past decades. It is a well-recognized problem impacting almost all fields of scientific study. Existing imputation algorithms differ in problem settings, model selection, and data evaluation. There is a lack of systematic comparison study among imputation algorithms. In this paper, we survey this interesting and evolving research topic by broadly reviewing and experimentally comparing the state-of-the-art missing data imputation algorithms. We analyze and categorize 19 imputation algorithms. Extensive experiments over 15 real-world benchmark datasets are conducted under various settings of data types, missing mechanisms, missing rates, dataset/model parameters, as well as the post-imputation prediction task. We shed light on a series of constructive insights on imputation algorithms to tackle imputation problem in real-life scenarios. Moreover, we put forward promising future directions for data imputation problem.
Cyber–Information Security Compliance and Violation Behaviour in Organisations: A Systematic Review

Cyber and information security (CIS) is an issue of national and international interest. Despite sophisticated security systems and extensive physical countermeasures to combat cyber-attacks, organisations are vulnerable due to the involvement of the human factor. Humans are regarded as the weakest link in cybersecurity systems as development in digital technology advances. The area of cybersecurity is an extension of the previously studied fields of information and internet security. The need to understand the underlying human behavioural factors associated with CIS policy warrants further study, mainly from theoretical perspectives. Based on these underlying theoretical perspectives, this study reviews literature focusing on CIS compliance and violations by personnel within organisations. Sixty studies from the years 2008 to 2020 were reviewed. Findings suggest that several prominent theories were used extensively and integrated with another specific theory. Protection Motivation Theory (PMT), the Theory of Planned Behaviour (TPB), and General Deterrence Theory (GDT) were identified as among the most referred-to theories in this area. The use of current theories is discussed based on their emerging importance and their suitability in future CIS studies. This review lays the foundation for future researchers by determining gaps and areas within the CIS context and encompassing employee compliance and violations within an organisation.
Self-Supervised learning for Conversational Recommendation

Conversational recommender system (CRS) aims to model user preference through interactive conversations. Although there are some works, they still have two drawbacks: (1) they rely on large amounts of training data and suffer from data sparsity problem; and (2) they do not fully leverage different types of knowledge extracted from dialogues. To address these issues in CRS, we explore the intrinsic correlations of different types of knowledge by self-supervised learning, and propose the model SSCR, which stands for Self-Supervised learning for Conversational Recommendation. The main idea is to jointly consider both the semantic and structural knowledge via three self-supervision signals in both recommendation and dialogue modules. First, we carefully design two auxiliary self-supervised objectives: token-level task and sentence-level task, to explore the semantic knowledge. Then, we extract the structural knowledge based on external knowledge graphs from user mentioned entities. Finally, we model the inter-information between the semantic and structural knowledge with the advantages of contrastive learning. As existing similarity functions fail to achieve this goal, we propose a novel similarity function based on negative log-likelihood loss. Comprehensive experimental results on two real-world CRS datasets (including both English and Chinese with about 10,000 dialogues) show the superiority of our proposed method. Concretely, in recommendation, SSCR gets an improvement about 5%∼15% compared with state-of-the-art baselines on hit rate, mean reciprocal rank and normalized discounted cumulative gain. In dialogue generation, SSCR outperforms baselines on both automatic evaluations (distinct n-gram, BLEU and perplexity) and human evaluations (fluency and informativeness).
CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters

Currently, many theoretical as well as practically relevant questions towards the transferability and robustness of Convolutional Neural Networks (CNNs) remain unsolved. While ongoing research efforts are engaging these problems from various angles, in most computer vision related cases these approaches can be generalized to investigations of the effects of distribution shifts in image data. In this context, we propose to study the shifts in the learned weights of trained CNN models. Here we focus on the properties of the distributions of dominantly used 3×3 convolution filter kernels. We collected and publicly provide a dataset with over 1.4 billion filters from hundreds of trained CNNs, using a wide range of datasets, architectures, and vision tasks. In a first use case of the proposed dataset, we can show highly relevant properties of many publicly available pre-trained models for practical applications: I) We analyze distribution shifts (or the lack thereof) between trained filters along different axes of meta-parameters, like visual category of the dataset, task, architecture, or layer depth. Based on these results, we conclude that model pre-training can succeed on arbitrary datasets if they meet size and variance conditions. II) We show that many pre-trained models contain degenerated filters which make them less robust and less suitable for fine-tuning on target applications. Data & Project website: https://github.com/paulgavrikov/cnn-filter-db.
Privacy and Security Awareness for Sensitive/Non-sensitive Data based Wearable Devices

In recent years, technological industry has made a large investment in the design of wearable devices. Wearable devices are attractive to a variety of different age groups within the majority of population. The main reasons for such popularity are related to ease of wear and friendly use, affordable prices with competitive products, as well as providing different services. Usually, wearable devices are collecting different kinds of data. Some of these data are sensitive and personal data of the wearer/user. Such data can be attacked, leaked, misused or edited. Therefore, privacy and security issues are among the main important issues to be considered carefully and discussed clearly when wearable devices are designed and used. Presenting unclear privacy and security strategies will lead the user to mistrust wearable technology with its application. In this research, we present our proposed wearable security protocol to create a secure environment of wearable data and their processing. The main idea of our protocol is to secure the identity of people as well as hiding their sensitive and personal data. Meanwhile, it does not affect the quality of data when applying their mining techniques. The protocol can be used for any kind of data with any application while keeping the user’s privacy and security properties. Moreover, it can be easily understood, implemented, and processed, in addition to any update might be needed.
A Novel Deep Transfer Learning Approach Based on Depth-Wise Separable CNN for Human Posture Detection

Human posture classification (HPC) is the process of identifying a human pose from a still image or moving image that was recorded by a digicam. This makes it easier to keep a record of people’s postures, which is helpful for many things. The intricate surroundings that are depicted in the image, such as occlusion and the camera view angle, make HPC a difficult process. Consequently, the development of a reliable HPC system is essential. This study proposes the “DeneSVM”, an innovative deep transfer learning-based classification model that pulls characteristics from image datasets to detect and classify human postures. The paradigm is intended to classify the four primary postures of lying, bending, sitting, and standing. These positions are classes of sitting, bending, lying, and standing. The Silhouettes for Human Posture Recognition dataset has been used to train, validate, test, and analyze the suggested model. The DeneSVM model attained the highest test precision (94.72%), validation accuracy (93.79%) and training accuracy (97.06%). When the efficiency of the suggested model was validated using the testing dataset, it too had a good accuracy of 95%.
Chaghri - A FHE-friendly Block Cipher

The Recent progress in practical applications of secure computation protocols has also attracted attention to the symmetric-key primitives underlying them. Whereas traditional ciphers have evolved to be efficient with respect to certain performance metrics, advanced cryptographic protocols call for a different focus. The so called arithmetic complexity is viewed through the number and layout of non-linear operations in the circuit implemented by the protocol. Symmetric-key algorithms that are optimized with respect to this metric are said to be algebraic ciphers. Previous work targeting ZK and MPC protocols delivered great improvement in the performance of these applications both in lab and in practical use. Interestingly, despite its apparent benefits to privacy-aware cloud computing, algebraic ciphers targeting FHE did not attract similar attention.
Path-following control for aerial manipulators robots with priority on energy saving

Nowadays, performing active tasks with aerial robots has become a great challenge to execute them autonomously or semi-autonomously. In this context, there is great importance to emphasize the control and energy consumption that is produced at the time of performing these tasks with an aerial mobile manipulator robot. In this context, this paper presents a new control scheme for the task of path-following through the end-effector of an aerial mobile manipulator robot prioritizing energy consumption. The proposed controller is designed according to the kinematics of the system considering the high redundancy of the aerial mobile manipulator robot composed of an aerial platform and an anthropomorphic robotic arm. Therefore, the proposed controller allows two control objectives. The main objective is the path-following by the end-effector of the aerial mobile manipulator robot, considering a variable path-following speed according to the references of the desired task; and the secondary objective is the energy saving by modifying the internal structure of the robot according to the parameters of the desired path to follow. Three configurations are proposed to modify the internal structure of the robot: (i) configuration for paths with curvature variations on the Z-axis of the fixed reference system; (ii) configuration with curvature variations of the desired path in the XY plane and; and (iii) configuration for curvature variations of the desired path in the XYZ space. In order to validate the proposed controller different experimental tests are performed in an unstructured environment, the proposed controller is compared with a path following controller based on inverse kinematics that allows to complete the desired task at constant speed and constant internal configuration of the robot. Through the experimental results an approximate 10% energy saving is achieved with the proposed controller during path-following.
Explainable Artificial Intelligence (XAI) in Insurance

Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry’s vast stores of sensitive data on policyholders and centrality in societal progress and innovation. This paper analyses current Artificial Intelligence (AI) applications in insurance industry practices and insurance research to assess their degree of explainability. Using search terms representative of (X)AI applications in insurance, 419 original research articles were screened from IEEE Xplore, ACM Digital Library, Scopus, Web of Science and Business Source Complete and EconLit. The resulting 103 articles (between the years 2000–2021) representing the current state-of-the-art of XAI in insurance literature are analysed and classified, highlighting the prevalence of XAI methods at the various stages of the insurance value chain. The study finds that XAI methods are particularly prevalent in claims management, underwriting and actuarial pricing practices. Simplification methods, called knowledge distillation and rule extraction, are identified as the primary XAI technique used within the insurance value chain. This is important as the combination of large models to create a smaller, more manageable model with distinct association rules aids in building XAI models which are regularly understandable. XAI is an important evolution of AI to ensure trust, transparency and moral values are embedded within the system’s ecosystem. The assessment of these XAI foci in the context of the insurance industry proves a worthwhile exploration into the unique advantages of XAI, highlighting to industry professionals, regulators and XAI developers where particular focus should be directed in the further development of XAI. This is the first study to analyse XAI’s current applications within the insurance industry, while simultaneously contributing to the interdisciplinary understanding of applied XAI. Advancing the literature on adequate XAI definitions, the authors propose an adapted definition of XAI informed by the systematic review of XAI literature in insurance.
An Exploration of Hands-free Text Selection for Virtual Reality Head-Mounted Displays

Hand-based interaction, such as using a handheld controller or making hand gestures, has been widely adopted as the primary method for interacting with both virtual reality (VR) and augmented reality (AR) head-mounted displays (HMDs). In contrast, hands-free interaction avoids the need for users’ hands and although it can afford additional benefits, there has been limited research in exploring and evaluating hands-free techniques for these HMDs. As VR HMDs become ubiquitous, people will need to do text editing, which requires selecting text segments. Similar to hands-free interaction, text selection is underexplored. This research focuses on both, text selection via hands-free interaction. Our exploration involves a user study with 24 participants to investigate the performance, user experience, and workload of three hands-free selection mechanisms (Dwell, Blink, Voice) to complement head-based pointing. Results indicate that Blink outperforms Dwell and Voice in completion time. Users’ subjective feedback also shows that Blink is the preferred technique for text selection. This work is the first to explore handsfree interaction for text selection in VR HMDs. Our results provide a solid platform for further research in this important area.
Exploring Endogenous Shift for Cross-domain Detection: A Large-scale Benchmark and Perturbation Suppression Network

Existing cross-domain detection methods mostly study the domain shifts where differences between domains are often caused by external environment and perceivable for humans. However, in real-world scenarios (e.g., MRI medical diagnosis, X-ray security inspection), there still exists another type of shift, named endogenous shift, where the differences between domains are mainly caused by the intrinsic factors (e.g., imaging mechanisms, hardware components, etc.), and usually inconspicuous. This shift can also severely harm the cross-domain detection performance but has been rarely studied. To support this study, we contribute the first Endogenous Domain Shift (EDS) benchmark, X-ray security inspection, where the endogenous shifts among the domains are mainly caused by different X-ray machine types with different hardware parameters, wear degrees, etc. EDS consists of 14,219 images including 31,654 common instances from three domains (X-ray machines), with bounding-box annotations from 10 categories. To handle the endogenous shift, we further introduce the Perturbation Suppression Network (PSN), motivated by the fact that this shift is mainly caused by two types of perturbations: category-dependent and category-independent ones. PSN respectively exploits local prototype alignment and global adversarial learning mechanism to suppress these two types of perturbations. The comprehensive evaluation results show that PSN outperforms SOTA methods, serving a new perspective to the cross-domain research community.
Governance Quality and Financial Development in Africa

The study examines the effect of governance quality on financial sector development using an empirical panel dataset from 45 African countries for the period 1996-2018. To this end, four bundled governance quality indicators (i.e., general, political, economic, and institutional) variables are employed. The empirical estimation is based on Quantile Regression and other subsidiary Model. The inspiration for this estimation technique is based on the argument that the effect of governance quality is contingent on the existing level of financial development, and governance – financial development policies, to be effective, need to be tailored differently across countries with low, intermediate and high levels of financial sector development. The general findings are as follows: First, development in governance institutions promotes financial sector development. Second, in most of the cases, the impact of governance quality is higher in terms of magnitude in the upper quantiles of the general, political, economic and institutional governance distributions. Third, the effect of governance institutions is significantly higher in terms of magnitude in the top quantiles of the economic governance distribution. The results remain robust over alternative econometric estimation techniques.
The Impact of Augmented Reality (AR) on Vocabulary Acquisition and Student Motivation

Although there is a growing body of literature about the use of Augmented Reality (AR) in language learning, research regarding its effectiveness for vocabulary development among secondary students is scarce. This study aims to measure the perception toward AR technology (H1), the effect of its use on vocabulary development (H2), and its impact on student motivation (H3). In this mixed method research based on convenient sampling, 130 students aged 14 to 15 (9th-graders) from two secondary education schools were assigned to an experimental (n = 64) and a control (n = 66) group. Both groups needed to learn 30 geographic terms in English over four weeks. The control group used a traditional method based on a handbook, while the experimental group was exposed to an AR-based lesson containing the same lexical terms. The instruments involved an English pre-post-test about the target vocabulary, a pre-post-survey, and a class discussion. Quantitative and qualitative data were analyzed through SPSS 20 statistical software and a thematic analysis, respectively. The results evidenced positive attitudes and a strong interest in AR integration in language learning. However, no significant differences were observed regarding vocabulary learning performance between both groups of secondary students.
An Efficient System Based on Experimental Laboratory in 3D Virtual Environment for Students with Learning Disabilities

Virtual reality applications can enhance the education and training of students with learning disabilities, along with their quality of life. Virtual worlds offer opportunities for creating a highly interactive, complex simulation. Modeling and scripting tools can be applied in these worlds. Providing functionalities for managing presentations, administration as well as assessment of coursework in an educational context, virtual learning environments support teaching and learning in schools. Learning disabilities such as “dyslexia, dyscalculia, attention issues (ADHD), and disability in retrieving information”, which students need to do physics experiments, are a major source of concern since they impair a person’s ability to learn. Traditional teaching methods do not appear to support the same thought in the situation of students with learning disabilities, who normally need a significant investment of time, money, and people for individual tutoring. Students with this problem have difficulty envisioning or visualizing what they are being taught, in addition to breaking down and processing the material. To address the above issues, this paper develops and evaluates a 3D virtual physics laboratory to improve cognitive skills in physics experiments for students with learning disabilities. The environment is created based on specially designed criteria for disabled students. The study proves the effectiveness of the 3D virtual environment in improving the cognitive skills in the physics of students with learning disabilities.
Review on Deep Learning based Medical Image Processing

Deep learning (DL) has made extensive progress in many exploration regions. Computer vision is one of the most trending fields advancing due to extensive research in developing DL models, mainly focusing on image pattern recognition. These days, numerous specialists attempt to track down answers for issues in different fields under the illumination of DL techniques. In this review, important information is added about DL models and testing points specialists can use in DL. This study explored DL and concentrates on what advancements are made in the most famous fields, for example, Medical Image segmentation, automated disease classification, 4D Motion Estimation, etc. Moreover, this study discusses the leftover difficulties of these examination regions that DL can tackle and talks about future subjects to help scientists, especially in handling Medical Images.