Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Network Embedding against Private Link Inference Attacks

Network embedding represents network nodes by a low-dimensional informative vector. While it is generally effective for various downstream tasks, it may leak some private information of networks, such as hidden private links. In this work, we address a novel problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">privacy-preserving network embedding against private link inference attacks</i> . Basically, we propose to perturb the original network by adding or removing links, and expect the embedding generated on the perturbed network can leak little information about private links but hold high utility for various downstream tasks. Towards this goal, we first propose general measurements to quantify privacy gain and utility loss incurred by candidate network perturbations; we then design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> rivacy- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> reserving <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> etwork <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> mbedding ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , PPNE) framework to identify the optimal perturbation solution with the best privacy-utility trade-off in an iterative way. Furthermore, we propose many techniques to accelerate PPNE and ensure its scalability. For instance, as the skip-gram embedding methods including DeepWalk and LINE can be seen as matrix factorization with closed-form embedding results, we devise efficient privacy gain and utility loss approximation methods to avoid the repetitive time-consuming embedding training for every candidate network perturbation in each iteration. Experiments on real-life network datasets (with up to millions of nodes) verify that PPNE outperforms baselines by sacrificing less utility and obtaining higher privacy protection.
Assessment of implementation of smart university management system: The case of Kazakh Ablai Khan University of International Relations and World Languages

The effective functioning of a smart university management system significantly improves higher education institutions’ competitiveness. This study aims to assess the implementation of a smart university management system on the example of Kazakh Ablai khan International Relations and World Languages University. The paper considers elements of a smart university system (smart learning technology infrastructure, smart faculty staff, and smart students) from the university managers’ and students’ viewpoints using the stepwise algorithm to raise the university’s competitiveness. During the study, 2700 students were surveyed to assess the implementation and operation of smart systems at the university, and ten experts (the expert team consists of deans and head of departments) were interviewed to identify the weights of each element. The survey results showed that the technology infrastructure for smart learning at the university is at a lower level (50%) than the competence of the teaching staff (60%) and students (89%). Furthermore, based on standardized factor scores and the weighted average, the implementation level of a smart university management system is low – C (0.498), where the weighted assessment of smart learning technology infrastructure is 0.2, smart teaching faculty – 0.19, and smart students – 0.108. According to the results, the university management needs to develop the smart learning technology infrastructure (free Wi-Fi, smart education environment, and smart classrooms) and improve the qualifications of its teaching staff (knowledge and skills, content of the courses, and educational process).
The need for joined-up thinking in critical raw materials research

Critical raw materials (CRMs) will be a cornerstone of the energy transition. CRMs were recognized early by the Intergovernmental Panel on Climate Change (IPCC) as a prime part of the mitigation effort for climate change and, as such, research into the genesis of key metals, as well as the sustainability of their mining, should be a priority. However, research is geopolitically influenced by the security of supply concerns of nations or economic groups of nations such as the EU, Japan, the USA and China. Many research networks and programmes are aimed at resource security; and where collaboration does exist it is along geopolitical lines, potentially disadvantaging developing countries and their efforts to implement UN sustainable development goals (SDGs). This contrasts with efforts in research such as those of the IPCC that are more collaborative and suited to rising to global challenges. We suggest that international organizations such as the International Union of Geological Sciences with its long international history, SDG focus and new focus on data (through the Deep-time Digital Earth (DDE) programme) should be involved in research prioritization unhindered by geopolitical considerations. Like climate change, the challenge of CRMs is too big to tackle in a competitive, geopolitically influenced manner. Thematic collection: This article is part of the energy-critical metals for a low carbon transition collection available at: https://www.lyellcollection.org/topic/collections/critical-metals
of the Russian Society of the Digital Age

In recent years, the problems of the modern civilizational choice of our country have been increasingly discussed. Various civilizational projects are considered from the social, economic, political, geopolitical, ideological, general cultural, confessional and other positions. At the same time, projects that pay attention to natural components that influence the formation of new civilizational orienta­tions take the least part in these discussions. And this is understandable. We live in a growing gap between the scientific and technical, humanitarian and social components of the development of modern society, the rapid formation of a new digital era created by the increased scientific and technical power of man. This era led to the emergence of global information networks that made global com­munications possible, which fundamentally changes the form of organization of modern society. The concept of the formation of such a digital era was compre­hensively developed in the works of D. Bell, E. Toffler, M. Castels, M. McLuhan and many other modern researchers. The paper considers the role of ecological thinking in the formation of civilizational orientations of the Russian society of the digital age. The prospects and risks of such a civilizational development are analyzed. The importance of the formation of environmentally oriented thinking for overcoming the risks and dangers of this path, for significant transforma­tions of the formation of civilizational orientations of our country is revealed. The historical prerequisites for the emergence of ecological approaches are considered. The civilizational perspectives of the functioning of ecological thinking in modern conditions are formulated. It is proved that ecological think­ing, leading to the formation of a broad ecological culture, fulfills the most im­portant strategic task of our time – the formation of an eco-oriented civilizational attitude of its development in our country.
Illumination correction via improved grey wolf optimizer for regularized random vector functional link network

In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons.In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO).Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO.Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation.Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images.The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.
An eye tracking study of the application of gestalt theory in photography

Photography is an art form where integration of the human visual perception and psychological experiences result in aesthetic pleasure. This research utilizes eye tracking to explore the impact of the properties of Gestalt in photography on people's visual cognitive process in order to understand the psychological processes and patterns of photography appreciation. This study found that images with Gestalt qualities can significantly affect fixation, sightline distribution, and subjective evaluation of aesthetics and complexity. Closure composition images seem to make cognition simpler, resulting in the least number of fixation and saccades, longer fixation duration, and more concentrated sightline indicating stronger feeling of beauty, while images which portray similarity results in the greatest fixation and saccades, longest saccade duration, and greater scattering of sightline, indicating feelings of complexity and unsightliness. The results of this research are closely related to the theories of art and design, and have reference value for photography theory and application.
Flipping the world upside down: Using eye tracking in virtual reality to study visual search in inverted scenes

Image inversion is a powerful tool for investigating cognitive mechanisms of visual perception. However, studies have mainly used inversion in paradigms presented on twodimensional computer screens. It remains open whether disruptive effects of inversion also hold true in more naturalistic scenarios. In our study, we used scene inversion in virtual reality in combination with eye tracking to investigate the mechanisms of repeated visual search through three-dimensional immersive indoor scenes. Scene inversion affected all gaze and head measures except fixation durations and saccade amplitudes. Our behavioral results, surprisingly, did not entirely follow as hypothesized: While search efficiency dropped significantly in inverted scenes, participants did not utilize more memory as measured by search time slopes. This indicates that despite the disruption, participants did not try to compensate the increased difficulty by using more memory. Our study highlights the importance of investigating classical experimental paradigms in more naturalistic scenarios to advance research on daily human behavior.
Rhizostoma optimization algorithm and its application in different real-world optimization problems

&lt;p&gt;In last decade, numerous meta-heuristic algorithms have been proposed for dealing the complexity and difficulty of numerical optimization problems in the realworld which is growing continuously recently, but only a few algorithms have caught researchers’ attention. In this study, a new swarm-based meta-heuristic algorithm called Rhizostoma optimization algorithm (ROA) is proposed for solving the optimization problems based on simulating the social movement of Rhizostoma octopus (barrel jellyfish) in the ocean. ROA is intended to mitigate the two optimization problems of trapping in local optima and slow convergence. ROA is proposed with three different movement strategies (simulated annealing (SA), fast simulated annealing (FSA), and Levy walk (LW)) and tested with 23 standard mathematical benchmark functions, two classical engineering problems, and various real-world datasets including three widely used datasets to predict the students’ performance. Comparing the ROA algorithm with the latest meta-heuristic optimization algorithms and a recent published research proves that ROA is a very competitive algorithm with a high ability in optimization performance with respect to local optima avoidance, the speed of convergence and the exploration/exploitation balance rate, as it is effectively applicable for performing optimization tasks.&lt;/p&gt;
Abstract 5442: SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images

Abstract Introduction: Artifacts are often introduced during tissue collection and processing, slide preparation, and/or when generating whole slide images (WSI). The presence of artifacts has a negative impact on the digital pathology workflow as artifacts may hinder diagnostic reporting and can lead to false positive and false negative results when using image analysis algorithms or computer-aided diagnosis systems. Manual quality control of WSI is a time-consuming procedure and therefore automated quality control tools, which report and exclude artifacts, are highly desirable to streamline digital pathology workflows. To automate the quality control step, we developed SlideQC, an AI-based quality control tool that automatically detects, reports, and outlines artifacts such as air bubbles, dust/debris, folds, out-of-focus,and pen marks, in both research and clinical workflows. Methods: SlideQC was trained with a DenseNet-based network using 1984 annotations for artifacts including air bubbles, dust/debris, folds, out-of-focus, and pen markers, across 254 Haematoxylin and Eosin (H&amp;E) stained WSI from more than 9 tissue types. A set of 2048 annotations from synthetically generated out-of-focus images was added to supplement the training data. The performance of the SlideQC was evaluated on an external test cohort of 49 WSI H&amp;E images sourced from the open-source database ‘HistoQCRepo’, across 375 annotations (tissue and artifact), and compared with the performance of HistoQC, an open-source quality control tool for digital pathology slides. Results: On the external test cohort, SlideQC showed high sensitivity, specificity, and F1-score with average values of 0.93, 0.99, and 0.93, across the five artifact types. In the same cohort, HistoQC attained an average sensitivity, specificity, and F1-score of 0.65, 0.79, and 0.54, respectively. Conclusions: SlideQC achieved high sensitivity, specificity, and F1-score on an external test cohort. SlideQC can add efficiency gains to a workflow by performing quality control on 100% of slides rather than the currently manually performed on only a subset of the slides in clinical pathology departments. SlideQC can allowthe triaging and alerting of slides containing a high level of artifact within a digital pathology workflow. The tool can also be used to exclude the artifact region from downstream analysis by subsequent image analysis algorithms. Citation Format: Daniela Rodrigues, Stefan Reinhard, Therese Waldburger, Daniel Martin, Suzana Couto, Inti Zlobec, Peter Caie, Erik Burlingame. SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5442.
A Critical Review of Inductive Logic Programming Techniques for Explainable AI

Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption. To instill confidence and trust in artificial intelligence (AI) systems, explainable AI (XAI) has emerged as a response to improve modern machine learning algorithms’ explainability. Inductive logic programming (ILP), a subfield of symbolic AI, plays a promising role in generating interpretable explanations because of its intuitive logic-driven framework. ILP effectively leverages abductive reasoning to generate explainable first-order clausal theories from examples and background knowledge. However, several challenges in developing methods inspired by ILP need to be addressed for their successful application in practice. For example, the existing ILP systems often have a vast solution space, and the induced solutions are very sensitive to noises and disturbances. This survey paper summarizes the recent advances in ILP and a discussion of statistical relational learning (SRL) and neural-symbolic algorithms, which offer synergistic views to ILP. Following a critical review of the recent advances, we delineate observed challenges and highlight potential avenues of further ILP-motivated research toward developing self-explanatory AI systems.
A comparison of U-Net series for CT pancreas segmentation

The problems of the large variation in shape and location, and the complex background of many neighboring tissues in the pancreas segmentation hinder the early detection and diagnosis of pancreatic diseases. The U-Net family achieve great success in various medical image processing tasks such as segmentation and classification. This work aims to comparatively evaluate 2D U-Net, 2D U-Net++ and 2D U-Net3+ for CT pancreas segmentation. More interestingly, We also modify U-Net series in accordance with depth wise separable convolution (DWC) that replaces standard convolution. Without DWC, U-Net3+ works better than the other two networks and achieves an average dice similarity coefficient of 0.7555. Specifically, according to this study, we find that U-Net plus a simple module of DWC certainly works better than U-Net++ using redesigned dense skip connections and U-Net3+ using full-scale skip connections and deep supervision and can obtain an average dice similarity coefficient of 0.7613. More interestingly, the U-Net series plus DWC can significantly reduce the amount of training parameters from (39.4M, 47.2M, 27.0M) to (14.3M, 18.4M, 3.15M), respectively. At the same time, they also improve the dice similarity compared to using normal convolution.
Personalized Privacy Protection-Preserving Collaborative Filtering Algorithm for Recommendation Systems

With the rapid development of ubiquitous data collection and data analysis, data privacy in a recommended system is facing more and more challenges. Differential privacy technology can provide strict privacy protection while reducing the risk of privacy leakage, but it also introduces unwanted noise, which makes the performance of the recommender system worsen. Among different users, the degree of their sensitivity to privacy is usually different. Thus, through considering the impact of users’ personalized requirements, the collaborative filtering algorithm can be designed to reduce the amount of unwanted noise. Taking the above assertions into account, we propose a collaborative filtering algorithm based on personalized privacy protection. First, it locally classifies ratings by privacy sensitivity on the user side, then utilizes the random flip mechanism to protect the privacy-sensitive ratings. Then, after the server catches the perturbed rating data, we reconstruct the joint item-item distribution through the Bayesian estimation method. Experimental results show that our proposed algorithm can significantly improve the recommendation performance of recommendation systems while protecting users’ privacy.
Automatically Identifying CVE Affected Versions with Patches and Developer Logs

While vulnerability databases are important sources of information for software security, it is known that information in these databases is inconsistent. How to rectify these incorrect data is a challenging issue. In this paper, we employ developer logs and patches to automatically identify vulnerable source code versions that each CVE really affects. Our tool organizes all versions of a piece of software into a version tree, and identifies the first vulnerable version, and the last vulnerable versions in the version tree trunk and branches. For evaluation, we took Linux Kernel as the case study and quantified the error rate of the vulnerable versions reported by the NVD. The total number of vulnerable Linux Kernel versions reported by the NVD was 43,727 (as of September 2020), of which the total number of false positives reached 2,497 and the total number of false negatives reached 9,330, accounting for 5.7% and 21.34%, respectively. In addition, we compare our tool with two vulnerability detection tools and show that our tool could achieve high detection accuracy.
User-Customizable Transpilation of Scripting Languages

A transpiler converts code from one programming language to another. Many practical uses of transpilers require the user to be able to guide or customize the program produced from a given input program. This customizability is important for satisfying many application-specific goals for the produced code such as ensuring performance, readability, ease of exposition or maintainability, compatibility with external environment or analysis tools, and so on. Conventional transpilers are deterministic rule-driven systems often written without offering customizability per user and per program. Recent advances in transpilers based on neural networks offer some customizability to users, e.g. through interactive prompts, but they are still difficult to precisely control the production of a desired output. Both conventional and neural transpilation also suffer from the "last mile" problem: they produce correct code on average, i.e., on most parts of a given program, but not necessarily for all parts of it. We propose a new transpilation approach that offers fine-grained customizability and reusability of transpilation rules created by others, without burdening the user to understand the global semantics of the given source program. Our approach is mostly automatic and incremental, i.e., constructs translation rules needed to transpile the given program as per the user's guidance piece-by-piece. Users can rely on existing transpilation rules to translate most of the program correctly while focusing their effort locally, only on parts that are incorrect or need customization. This improves the correctness of the end result. We implement the transpiler as a tool called DuoGlot, which translates Python to Javascript programs, and evaluate it on the popular GeeksForGeeks benchmarks. DuoGlot achieves 90% translation accuracy and so it outperforms all existing translators (both handcrafted and neural-based), while it produces readable code. We evaluate DuoGlot on two additional benchmarks, containing more challenging and longer programs, and similarly observe improved accuracy compared to the other transpilers.
Automated Translation of Functional Big Data Queries to SQL

Big data analytics frameworks like Apache Spark and Flink enable users to implement queries over large, distributed databases using functional APIs. In recent years, these APIs have grown in popularity because their functional interfaces abstract away much of the minutiae of distributed programming required by traditional query languages like SQL. However, the convenience of these APIs comes at a cost because functional queries are often less efficient than their SQL counterparts. Motivated by this observation, we present a new technique for automatically transpiling functional queries to SQL. While our approach is based on the standard paradigm of counterexample-guided inductive synthesis, it uses a novel column-wise decomposition technique to split the synthesis task into smaller subquery synthesis problems. We have implemented this approach as a new tool called RDD2SQL for translating Spark RDD queries to SQL and empirically evaluate the effectiveness of RDD2SQL on a set of real-world RDD queries. Our results show that (1) most RDD queries can be translated to SQL, (2) our tool is very effective at automating this translation, and (3) performing this translation offers significant performance benefits.