Reviewerly/exHarmony huggingface dataset:
Efficient cross-validation traversals in feature subset selection

Sparse and robust classification models have the potential for revealing common predictive patterns that not only allow for categorizing objects into classes but also for generating mechanistic hypotheses. Identifying a small and informative subset of features is their main ingredient. However, the exponential search space of feature subsets and the heuristic nature of selection algorithms limit the coverage of these analyses, even for low-dimensional datasets. We present methods for reducing the computational complexity of feature selection criteria allowing for higher efficiency and coverage of screenings. We achieve this by reducing the preparation costs of high-dimensional subsets [Formula: see text] to those of one-dimensional ones [Formula: see text]. Our methods are based on a tight interaction between a parallelizable cross-validation traversal strategy and distance-based classification algorithms and can be used with any product distance or kernel. We evaluate the traversal strategy exemplarily in exhaustive feature subset selection experiments (perfect coverage). Its runtime, fitness landscape, and predictive performance are analyzed on publicly available datasets. Even in low-dimensional settings, we achieve approximately a 15-fold increase in exhaustively generating distance matrices for feature combinations bringing a new level of evaluations into reach.
Proofs of retrievability with tag outsourcing based on Goppa codes

In cloud storage, because the data owner loses the physical control of the data, the data may be tampered with or deleted. Although, it has been proposed to adopt provable data possession (PDP) or proofs of retrievability (POR) mechanism to ensure the integrity of cloud storage data. However, at present, most PDP/POR schemes are based on traditional cryptographic mechanisms and cannot resist quantum computer attacks. For this reason, the first POR scheme based on coding mechanism (BC-POR) is proposed in this paper. The scheme is constructed based on the difficulty assumptions of 2-regular word syndrome decoding (2-RWSD) problem and Goppa code distinguishing problem. Moreover, considering the low computing power of lightweight users, this paper adopts an audit scheme that supports the outsourcing of data tag calculation on the client side, that is, the calculation of data tag generation is outsourced to a third-party institution for execution. First of all, this scheme can prevent third-party institution from obtaining the real content of the data in the process of calculation tags and realize the privacy protection of user data. Secondly, the scheme uses a FSB hash function to generate a decodable syndrome, and this algorithm does not require iterative operations during the tagging process, thereby reducing the computational overhead of the tag. Finally, the provable security method is used to prove the security of the proposed scheme, and the performance of the proposed audit scheme is evaluated to prove the effectiveness of the scheme.
Modified RC4 Algorithm for Improve Data Protection in Cloud Environment

The rapid growth of digital technologies that depending on cloud computing has seriously increased the threats related to information security. This will certainly pose a major threat to the trust of users of the cloud environment. For this reason, the use of algorithms like Rivest Ciphers 4 (RC4) algorithms to encrypt data and then store in the cloud became essential issue. So, because of the simplicity and ease of RC4 algorithm and its suitability to encrypt most data form efficiently, it has been widely used. But, the RC4 algorithm suffers from some weaknesses in both of Key Schedule Algorithm (KSA) and Pseudo-Random Generation Algorithm (PRGA) stages. As, the intruder may be able to follow the traces and thus retrieve the key based on the internal state or the stream of output. There were a lot of modifications in many literature reviews to improve the security and performance of the RC4 algorithm. In this paper, the focus was placed on improving the RC4 algorithm in both of (KSA) and (PRGA) in order to increase the security level. The performance of our improved RC4 algorithm was tested and evaluated by increasing the randomness of the generated key. The experimental results prove that high security level for output based on the security average of ciphertext with various keys and various files size. Thus, get more security level for the personal data that is stored in the cloud.
Efficient Document-at-a-Time and Score-at-a-Time Query Evaluation for Learned Sparse Representations

Researchers have had much recent success with ranking models based on so-called learned sparse representations generated by transformers. One crucial advantage of this approach is that such models can exploit inverted indexes for top- k retrieval, thereby leveraging decades of work on efficient query evaluation. Yet, there remain many open questions about how these learned representations fit within the existing literature, which our work aims to tackle using four representative learned sparse models. We find that impact weights generated by transformers appear to greatly reduce opportunities for skipping and early exiting optimizations in well-studied document-at-a-time ( DaaT ) approaches. Similarly, “off-the-shelf” application of score-at-a-time ( SaaT ) processing exhibits a mismatch between these weights and assumptions behind accumulator management strategies. Building on these observations, we present solutions to address deficiencies with both DaaT and SaaT approaches, yielding substantial speedups in query evaluation. Our detailed empirical analysis demonstrates that both methods lie on the effectiveness–efficiency Pareto frontier, indicating that the optimal choice for deployment depends on operational constraints.
Designing for agricultural digital knowledge exchange: applying a user-centred design approach to understand the needs of users

Purpose The aim of this study was to understand the needs of European users for successful development and uptake of a digital land-based knowledge platform.Methodology The ‘discover and define’ phases of the design thinking double diamond framework were applied to inform the construction of 16 user-personas and journeys. Qualitative and quantitative data were collected from users across Europe using a multi-step approach and thematically analysed.Findings User-personas and associated user journeys present useful methods for understanding the requirements of key stakeholders in the land-based sectors to aid the development of a digital knowledge reservoir, tailored to their needs. Problem urgency plays a key role in stakeholder touchpoint decisions, with acute problems approached by direct calls or interactions with important advisors. Digital platforms are more likely to be used for chronic problems or general knowledge exchange and upskilling. The touchpoints, challenges and emotions of users are not significantly linked to geographical location but are more likely to be influenced by age, gender and existing digital knowledge.Practical implications Design thinking can be used to understand the needs of stakeholders in the land-based sectors for the development of tools, products and services as well as understanding specific knowledge requirements.Theoretical implications This study provides a theoretical framework for understanding the digital knowledge needs of different land-based users.Originality The development of user-personas and journeys is limited in the land-based sectors, and this research provides a framework for use.
An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy

Abstract With the rapid development of point-of-interest (POI) recommendation services, how to utilize the multiple types of users’ information safely and effectively for a better recommendation is challenging. To solve the problems of imperfect privacy-preserving mechanism and insufficient response-ability to complex contexts, this paper proposes a hybrid POI recommendation model based on local differential privacy (LDP). Firstly, we introduce randomized response techniques k -RR and RAPPOR to disturb users’ ratings and social relationships, respectively and propose a virtual check-in time generation method to deal with the issue of missing check-in time after disturbance. Secondly, for simultaneously combining multiple types of information, we construct a hybrid model containing three sub-models. Sub-model 1 considers the effect of user preference, social relationship, forgetting feature, and check-in trajectory on similarity calculation. Sub-model 2 analyzes the geographical correlation of POIs. Sub-model 3 focuses on the categories of POIs. Finally, we generate the recommendation results. To test the performance of privacy-preserving and recommendation, we design three groups of experiments on three real-world datasets for comprehensive verifying. The experimental results show that the proposed method outperforms existing methods. Theoretically, our study contributes to the effective and safe usage of multidimensional data science and analytics for privacy-preserving POI recommender system design. Practically, our findings can be used to improve the quality of POI recommendation services.
The Quality of Education in the Conditions of Forced Distance Learning Caused by COVID-19

The article presents the results of a study of the quality of education in conditions of forced distance learning in the initial conditions of the introduction of quarantine restrictions caused by COVID-19. The study was conducted in two stages to identify the quality of education in conditions of forced distance learning for teachers and students from 03.30.2020 to 04.04.2020. The study involved teachers in the amount of 42 people. To study the perception of the image of students by teachers in the framework of forced distance and classroom learning, a questionnaire was developed that covers various aspects of student activity under quarantine restrictions. The survey involved 326 students from different faculties. Students were asked to answer questions regarding their assessment of the educational process in conditions of forced distance learning. This helped to determine some aspects of the quality of education in the conditions of forced distance learning. The results showed that the quality of education in the opinion of teachers after the transition to forced distance learning gives reason to talk about the emergence of perceptual-cognitive dissonance among teachers, because the information that they receive in the process of communicating with students online does not meet their own expectations in many ways perception, imagination, analysis (due to the minimum of information during communication), which is about the typical direct process of interaction with students, their personal experience regarding this interaction. Thus, for students of all courses, the quality of education is associated with both personal and professional growth. Our study showed that, despite the fact that students are aware of the need to form personal qualities, they do not fully understand what these qualities are and in what ways they need to be developed.
Studies on Parameters Affecting Temperature of Liquid Steel and Prediction Using Modified AdaBoost.RT Algorithm Ensemble Extreme Learning Machine

The present work aimed to develop a predictive model for the end temperature of liquid steel in advance to support the smooth functioning of a vacuum tank degasser (VTD). An ensemble model that combines extreme learning machine (ELM) with a self-adaptive AdaBoost.RT algorithm was established for the regression problem. Based on analyzing the energy equilibrium of the VTD system, the factors were determined for predicting the end temperature of liquid steel. To establish a hybrid ensemble prediction model, an ELM algorithm was selected as the ensemble predictor due to its strong performance and robustness, and a modification of the AdaBoost.RT algorithm is proposed to overcome the drawback of the original AdaBoost.RT by embedding statistical theory to dynamically self-adjust the threshold value. For efficient VTD operations, an ensemble model that combines ELM with the self-adaptive AdaBoost.RT algorithm was established to model the end temperature of liquid steel. The proposed approach was analyzed and validated on actual production data derived from a steelmaking workshop in Baosteel. The experimental results reveal that the proposed model can improve the generalization performance, and the accuracy of the model is feasible for the secondary steel refining process. In addition, a polynomial equation is obtained from the ensemble predictive model for calculating the value of the end temperature. The predicted results are in good agreement with the actual data with &lt;1.7% error.
Deep learning approach for segmentation and classification of blood cells using enhanced CNN

The main aim of our proposed work is to segment and classify blood cells using K means algorithm, and also with the help of image processing techniques. A complete blood cell count is very important in the medical analysis for evaluating the total health condition of the body. In the olden days, the blood cells are counted manually with the help of a hem cytometer with other lab equipment and certain chemicals. This technique both time-consuming and challenging. Deep Learning (DL) is an artificial intelligence subset of machine learning that can examine unsupervised information. RBC, or red blood cells, are red cells that are numerous in the blood and are noted for their dark red colour. White blood cells, commonly known as leukocytes, protect the body from infection. Processing tools like MATLAB is used to find the variations in area, perimeter and statistical parameters like mean and standard deviation that separates white blood cells from other blood components. Because of its high accuracy, Enhanced CNN can be used in this study for the categorization and recognition of normal and abnormal blood cell images. Average accuracy of 95% and average precision 0f 0.93 which is higher than existing CNN.
Physical imaging parameter variation drives domain shift

Statistical learning algorithms strongly rely on an oversimplified assumption for optimal performance, that is, source (training) and target (testing) data are independent and identically distributed. Variation in human tissue, physician labeling and physical imaging parameters (PIPs) in the generative process, yield medical image datasets with statistics that render this central assumption false. When deploying models, new examples are often out of distribution with respect to training data, thus, training robust dependable and predictive models is still a challenge in medical imaging with significant accuracy drops common for deployed models. This statistical variation between training and testing data is referred to as domain shift (DS).To the best of our knowledge we provide the first empirical evidence that variation in PIPs between test and train medical image datasets is a significant driver of DS and model generalization error is correlated with this variance. We show significant covariate shift occurs due to a selection bias in sampling from a small area of PIP space for both inter and intra-hospital regimes. In order to show this, we control for population shift, prevalence shift, data selection biases and annotation biases to investigate the sole effect of the physical generation process on model generalization for a proxy task of age group estimation on a combined 44 k image mammogram dataset collected from five hospitals.We hypothesize that training data should be sampled evenly from PIP space to produce the most robust models and hope this study provides motivation to retain medical image generation metadata that is almost always discarded or redacted in open source datasets. This metadata measured with standard international units can provide a universal regularizing anchor between distributions generated across the world for all current and future imaging modalities.
Introducing and Familiarising Older Adults Living with Dementia and Their Caregivers to Virtual Reality

Virtual Reality (VR) is increasingly being applied in dementia care across a range of applications and domains including health and wellbeing. Despite the commercial availability of VR, informants of design are not always aware of its functionality and capabilities, to meaningfully contribute to VR design. In designing VR applications for people living with dementia, it is recommended that older adults living with dementia and their support persons be involved in the design process using participatory approaches, thereby giving them a voice on the design of technology from the outset. A VR technology probe is a useful means of familiarising older adults living with dementia and their informal caregivers with the knowledge and understanding of interactive VR to employ technology that supports them to maintain their social health. This paper charts the implementation and evaluation of a VR technology probe, VR FOUNDations. To explore their experiences, nine older adults living with dementia and their nine informal caregivers trialled VR FOUNDations and completed semi-structured interviews after its use. Overall, older adults living with dementia and their informal caregivers perceived VR FOUNDations to achieve its aim of increasing understanding and inspiring future design decisions. The findings also identified promising positive experiences using a VR technology probe which may be indicative of its applicability to social health and wellbeing domains. This paper advocates for the structured design and implementation of VR technology probes as a pre-requisite to the participatory design of VR applications for the health and wellbeing of people living with dementia. The use of such technology probes may afford older adults living with dementia and their informal caregivers the best opportunity to contribute to design decisions and participate in technology design to support their health and wellbeing.
SLGAN

There are five features to consider when using generative adversarial networks to apply makeup to photos of the human face. These features include (1) facial components, (2) interactive color adjustments, (3) makeup variations, (4) robustness to poses and expressions, and the (5) use of multiple reference images. To tackle the key features, we propose a novel style- and latent-guided makeup generative adversarial network for makeup transfer and removal. We provide a novel, perceptual makeup loss and a style-invariant decoder that can transfer makeup styles based on histogram matching to avoid the identity-shift problem. In our experiments, we show that our SLGAN is better than or comparable to state-of-the-art methods. Furthermore, we show that our proposal can interpolate facial makeup images to determine the unique features, compare existing methods, and help users find desirable makeup configurations.
Self-Attentive CLIP Hashing for Unsupervised Cross-Modal Retrieval

With the explosive growth of multi-modal data such as video, images, and text on the Internet, cross-modal retrieval has received extensive attention, especially the deep hashing method. Compared with the real-value method, deep hashing has shown promising prospects due to its low memory consumption and high searching efficiency. However, most existing studies have difficulties in effectively utilizing the raw image-text pairs to generate discriminative feature representations. Moreover, these methods ignore the latent relationship between different modalities and fail to construct a robust similarity matrix, resulting in suboptimal retrieval performance. In this paper, we focus on the unsupervised cross-modal hashing tasks and propose a Self Attentive CLIP Hashing (SACH) model. Specifically, we construct the feature extraction network by employing the pre-trained CLIP model, which has shown excellent performance in zero-shot tasks. Besides, to fully exploit the semantic relationships, an attention module is introduced to reduce the disturbance of redundant information and focus on important information. On this basis, we construct a semantic fusion similarity matrix that capable of preserving the original semantic relationships from different modalities. Extensive experiments show the superiority of SACH compared with recent state-of-the-art unsupervised hashing methods.
Understanding the Influence of AST-JS for Improving Malicious Webpage Detection

JavaScript-based attacks injected into a webpage to perpetrate malicious activities are still the main problem in web security. Recent works have leveraged advances in artificial intelligence by considering many feature representations to improve the performance of malicious webpage detection. However, they did not focus on extracting the intention of JavaScript content, which is crucial for detecting the maliciousness of a webpage. In this study, we introduce an additional feature extraction process that can capture the intention of the JavaScript content of the webpage. In particular, we developed a framework for obtaining a JavaScript representation based on the abstract syntax tree for JavaScript (AST-JS), which enriches the webpage features for a better detection model. Moreover, we investigated the influence of our proposed feature on improving the model’s performance by using the Shapley additive explanation method to define the significance of each feature category compared to our proposed feature. The evaluation shows that adding the AST-JS feature can improve the performance for detecting malicious webpage compared to previous work. We also found that AST significantly influences performance, especially for webpages with JavaScript content.
DCKT: A Novel Dual-Centric Learning Model for Knowledge Tracing

Knowledge tracing (KT), aiming to model learners’ mastery of a concept based on their historical learning records, has received extensive attention due to its great potential in realizing personalized learning in intelligent tutoring systems. However, most existing KT methods focus on a single aspect of knowledge or learner, not paying careful attention to the coupling influence of knowledge and learner characteristics. To fill this gap, in this paper, we explore a new paradigm for the KT task by exploiting the coupling influence of knowledge and learner. A novel model called Dual-Centric Knowledge Tracing (DCKT) is proposed to model knowledge states through two joint tasks of knowledge modeling and learner modeling. In particular, we first generate concept embeddings in abundant knowledge structure information via a pretext task (knowledge-centric): unsupervised graph representation learning. Then, we deeply measure learners’ prior knowledge the knowledge-enhanced representations and three predefined educational priors for discriminative feature enhancement. Furthermore, we design a forgetting-fusion transformer (learner-centric) to simulate the declining trend of learners’ knowledge proficiency over time, representing the common forgetting phenomenon. Extensive experiments were conducted on four public datasets, and the results demonstrate that DCKT could achieve better knowledge tracing results over all datasets via a dual-centric modeling process. Additionally, DCKT can learn meaningful question embeddings automatically without manual annotations. Our work indicates a potential future research direction for personalized learner modeling, which is of both accuracy and high interpretability.