Reviewerly/exHarmony huggingface dataset:
From Release to Rebirth: Exploiting Thanos Objects in Linux Kernel

Vulnerability fixing is time-consuming, hence, not all of the discovered vulnerabilities can be fixed timely. In reality, developers prioritize vulnerability fixing based on exploitability. Large numbers of vulnerabilities are delayed to patch or even ignored as they are regarded as “unexploitable” or underestimated owing to the difficulty in exploiting the weak primitives. However, exploits may have been in the wild. In this paper, to exploit the weak primitives that traditional approaches fail to exploit, we propose a versatile exploitation strategy that can transform weak exploit primitives into strong exploit primitives. Based on a special object in the kernel named Thanos object, our approach can exploit a UAF vulnerability that does not have function pointer dereference and an OOB write vulnerability that has limited write length and value. Our approach overcomes the shortage that traditional exploitation strategies heavily rely on the capability of the vulnerability. To facilitate using Thanos objects, we devise a tool named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TAODE</monospace> to automatically search for eligible Thanos objects from the kernel. Then, it evaluates the usability of the identified Thanos objects by the complexity of the constraints. Finally, it pairs vulnerabilities with eligible Thanos objects. We have evaluated our approach with real-world kernels. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TAODE</monospace> successfully identified numerous Thanos objects from Linux. Using the identified Thanos objects, we proved the feasibility of our approach with 20 real-world vulnerabilities, most of which traditional techniques failed to exploit. Through the experiments, we find that in addition to exploiting weak primitives, our approach can sometimes bypass the kernel SMAP mechanism (CVE-2016-10150, CVE-2016-0728), better utilize the leaked heap pointer address (CVE-2022-25636), and even theoretically break certain vulnerability patches (e.g., double-free).
Cryptographic encoding in modern symmetric and asymmetric encryption

In this article, we address the global problem of improving the quality of cryptographic trans-forming information. There were revealed contradictions between the approaches to the development of encryption algorithms for information security systems and encoding algorithms for information protection in computer systems and networks. One of the variants to solve this problem is creation a theory of cryptographic encoding. This work will explain the origins of researching the information encoding processes under the control of cryptographic primitives. The encryption algorithms' synthesis and analysis through the prism of discrete devices synthesis and analysis allow us to consider the cryptographic algorithms' classification issues in a different way, to determine possible directions for developing the new crypto algorithms, and to improve existing crypto algorithms. The correlations between symmetric and asymmetric ciphers are being analyzed. A new approach to the construction of asymmetric stream ciphers is proposed. The methods for increasing the length of the block of information and the length of the key for both new and already known encryption algorithms, as well as options for their implementation, are presented. The given examples of encryption algorithms demonstrate new opportunities for improving the computer cryptography systems and the information security cryptographic systems based on applying the new knowledge being obtained in the process of constructing a cryptographic encoding theory.
RFID-based cultural performance progress management system

In this paper, a cultural performance progress management system using RFID tags and readers is proposed so that the RFID technology can be grafted onto the field of the cultural performance industry with a view to enhancing the efficiency of audience management and preventing ticket touting. This system is studied to use the advantages of RFID, which are the ability to store a lot of data, high recognition speeds, and reusability. In the case of the proposed system, when a tag has been recognised by its reader, the ID number of the RFID tag and that of the database are matched to identify the stored information. Thereafter, the tag is recharged with money and used as a prepayment method.
Feature Representation Learning for Unsupervised Cross-Domain Image Retrieval

Current supervised cross-domain image retrieval methods can achieve excellent performance. However, the cost of data collection and labeling imposes an intractable barrier to practical deployment in real applications. In this paper, we investigate the unsupervised cross-domain image retrieval task, where class labels and pairing annotations are no longer a prerequisite for training. This is an extremely challenging task because there is no supervision for both in-domain feature representation learning and cross-domain alignment. We address both challenges by introducing: 1) a new cluster-wise contrastive learning mechanism to help extract class semantic-aware features, and 2) a novel distance-of-distance loss to effectively measure and minimize the domain discrepancy without any external supervision. Experiments on the Office-Home and DomainNet datasets consistently show the superior image retrieval accuracies of our framework over state-of-the-art approaches. Our source code can be found at https://github.com/conghuihu/UCDIR .
On-demand state separation for cloud data warehousing

Moving data analysis and processing to the cloud is no longer reserved for a few companies with petabytes of data. Instead, the flexibility of on-demand resources is attracting an increasing number of customers with small to medium-sized workloads. These workloads do not occupy entire clusters but can run on single worker machines. However, picking the right worker for the job is challenging. Abstracting from worker machines, e.g., using stateless architectures, introduces overheads impacting performance. Solutions without stateless architectures resort to query restarts in the event of an adverse worker matching, wasting already achieved progress. In this paper, we propose migrating queries between workers by introducing on-demand state separation. Using state separation only when required enables maximum flexibility and performance while keeping already achieved progress. To derive the requirements for state separation, we first analyze the query state of medium-sized workloads on the example of TPC-DS SF100. Using this, we analyze the cost and describe the constraints necessary for state separation on such a workload. Furthermore, we describe the design and implementation of on-demand state separation in a compiling database system. Finally, using this implementation, we show the feasibility of our approach on TPC-DS and give a detailed analysis of the cost of query migration and state separation.
Research Work in the 1960s — 1980s as a Factor in the Creation of Library System

The authors analyse the basic principles and nature of research work (R&amp;D) in the library sphere in the 1960s-1980s. During that period, O.D. Golubeva, Deputy Director of the M.E. Saltykov-Shchedrin State Public Library (GPB), the main research and coordination centre in the field of library science, bibliography and book studies in the RSFSR, was engaged in organizing scientific activities. The article considers the regulatory framework defining the organization of R&amp;D in the field of librarianship, the nature of interaction of GPB with provincial, regional, republican (ASSR) libraries of Russia, as well as with institutions of other agencies. The authors show the importance of consolidated and coordination plans of research in library science, bibliography and book studies in the RSFSR as the most important tools of joint work. The authors characterize the main problems investigated during the study period and the principles of the formation of scientific work plans, as well as establish the role of centralized and regional studies. The paper presents information about the manuals prepared by the GPB on the methods and tools of library research. The authors consider the organization of work on the implementation of R&amp;D results, as well as identify and present the sources characterizing the flow of scientific publications of the GPB of that period. The article systematizes and formulates the basic principles of the organization of R&amp;D work. The authors consider the reasons for the creation of library system in the modern conditions as implementation of the task set in the “Strategy for the development of librarianship in the Russian Federation for the period up to 2030”. The article concludes on the usefulness of taking into account the work experience of O.D. Golubeva in the solution of this problem.
Analysis of Automatic Evaluation Metric on Low-Resourced Language: BERTScore vs BLEU Score

The accurate evaluation of machine translation (MT) is a difficult task. Human evaluation (judgment) is considered to be the best, but it is time-consuming. Hence, the importance of developing an automatic evaluation metric got researchers' attention. In this paper, we have done an in-depth analysis of the performance of the MT engine on low-resourced Bengali to English translations. We analyzed the scores generated by automatic metrics such as BLEU and BERTscore. We have computed the scores of the translation engine manually also based on the parameters used in the human evaluation. Finally, we have measured the correlation of BLEU and BERTScore with human judgment and found that BERTScore has a higher correlation with human judgment for our English to Bangla language pair.
Anomaly Detection as a Service: An Outsourced Anomaly Detection Scheme for Blockchain in a Privacy-Preserving Manner

Attacks against blockchain networks have proliferated in recent years. Due to its immense economic value, Bitcoin has been subject to numerous malicious theft activities through the exchange platforms. This poses a severe threat to the credibility of the entire Bitcoin ecosystem. Therefore, it is necessary to provide detection and prediction services of malicious events for Bitcoin Exchanges to prevent them in a precise and timely manner. Meanwhile, preserving the privacy of transaction data to prevent de-anonymization attacks during the detection process is also of great importance. In this paper, we present a general framework for privacy-preserving anomaly detection in blockchain networks. Based on this framework, we propose ADaaS, an anomaly detection service scheme that adopts a supervised machine learning model and achieves privacy preservation by using vector homomorphic encryption and matrix perturbation strategies. We also analyze the security, communication and computation costs of ADaaS. Experimental results demonstrate that ADaaS can achieve high detection effectiveness while providing privacy guarantees and is applicable in real scenarios of detecting Bitcoin transactions due to its reasonable efficiency.
Hierarchical Classification on the MNIST Dataset Using Truncated SVD and Kernel Density Estimation

The MNIST dataset is considered a challenging problem for machine learning algorithms. The present paper introduces a novel approach based on a truncated SVD and kernel density estimation that outputs an error rate on MNIST comparable to classical machine learning approaches. In addition to that, a hierarchical classification framework is proposed, that allows to enhance the algorithm accuracy. The resulting algorithm outperforms the reproducible SVM accuracy, which is regarded as a state-of-the-art machine learning algorithm for MNIST. The key advantage of the proposed framework consists in a lower computational cost: training on MNIST takes 142 times less and prediction takes 4 times less than for SVM on a single run. Thus, the research results state that TSVD-KDE algorithm has the potential for being an efficient classification algorithm. The source code for the experiment is released on GitHub: https://github.com/ekplesovskaya/MNIST-Classification-Using-TSVD-and-KDE.
Impact of digitised ‘teaching-learning’ virtual platforms on tertiary students’ learning objectives and teaching outcomes

This study explores and understands the impact of remote teaching using digitised teaching-learning virtual platforms on students' learning objectives and outcomes in Ghanaian Universities. Using a self-administered questionnaire, quantitative data from four hundred and four graduate students whose courses were delivered online due to the COVID-19 pandemic were gathered. Data were analysed using a systemic analytical approach. Confirmatory factor analysis was conducted first, followed by an analysis of moment structures for multiple levels of variables using structural equation modelling. It was found that students' trust in the quality of information they received online, their interactions with instructors, and their interactions with virtual platforms positively affected their trust in the usability of digitised 'teaching-learning' virtual platforms; this had a positive impact on their attainment of learning objectives. In conclusion, students' perceptions of the usability of virtual platforms as classrooms for teaching and learning are influenced by a psychological dynamic between the quality of information they receive from their instructors and the quality of their interactions with instructors and the virtual platforms. The findings offer a good pedagogic understanding of the dynamics of virtual teaching-learning design practices, which may be applied to effectively design virtual classrooms and assignments to improve student learning.
A Novel Weights of Weights Approach for Efficient Transfer Learning in Artificial Neural Networks

Deep learning is often reputed to be slow at training due to large number of layers. There are several approaches including used for improving training mechanism. Transfer learning sometimes referred as transfer of knowledge is one such approach. However, Transfer learning is often moving weights or layers as a whole including weights that might not be significant. This research tries to implement a novel Weights of Weights approach where significant weights are extracted from a training network. Only the significant weights are transferred to new untrained network instead of transferring all the layers or weights. Experiments are carried out using a 7-layered neural networks on multivariate wine dataset to evaluate the efficiency of the model. The experiment results show that the transferable model has provided better accuracy of over 40%-60% (based on layers) without retraining compared to traditional approach of copying all the layers with same and less number of nodes.
Lexicon-based Sentence Emotion Detection Utilizing Polarity-Intensity Unit Circle Mapping and Scoring Algorithm

The study of identifying the emotion present in each text is one of the widely explored domains in the field of natural language processing (NLP). Sentiment Analysis (SA) and Affective Computing (AC) are generally explored by numerous researchers as they are proven to be cross-disciplinary with a huge horizon of potential applications. Emotion detection commonly comes in two main classifications of approaches - machine learning and lexicon-based models. The tradeoff between accuracy and complexity sets the obvious boundaries between these approaches. In this study, we explored an alternative approach to emotion recognition by representing the Plutchik's Wheel of Emotions in a unit circle and generating the appropriate polarity and intensity scores using the unit circle formula. The predicted class will be identified by computing the Euclidean distance of the test instance to the unit circle position of each emotion in the emotion wheel. The model was trained using the datasets from various data sources. Overall, an accuracy of 78% is achieved, outperforming other multiclass lexicon-based classification models. The t-test presents that the differences between the variations of the models (mean-based and median-based scoring) is statistically insignificant at ɑ = 0.05.
Variational Quantum Eigensolver with Mutual Variance-Hamiltonian Optimization

The zero-energy variance principle can be exploited in variational quantum eigensolvers for solving general eigenstates but its capacity for obtaining a specified eigenstate, such as ground state, is limited as all eigenstates are of zero energy variance. We propose a variance-based variational quantum eigensolver for solving the ground state by searching in an enlarged space of wavefunction and Hamiltonian. With a mutual variance-Hamiltonian optimization procedure, the Hamiltonian is iteratively updated to guild the state towards to the ground state of the target Hamiltonian by minimizing the energy variance in each iteration. We demonstrate the performance and properties of the algorithm with numeral simulations. Our work suggests an avenue for utilizing guided Hamiltonian in hybrid quantum-classical algorithms.
FLAAP: An Open Human Activity Recognition (HAR) Dataset for Learning and Finding the Associated Activity Patterns

A significant quantity of research work has been completed to recognize human activities. The majority of the proposed learning algorithms have treated the activity data as the role of fuel in vehicles. The capacity of the learning algorithms to recognize the activity patterns can be improved by the adequate availability of activity data. In this paper, we introduced the FLAAP (Finding and Learning the Associated Activity Patterns) activity dataset and data acquired by using the smartphone (accelerometer and gyroscope) sensors placed at the waist of the subjects while performing the activities. This dataset contains the record of ten activities performed by eight distinct subjects. Between February 1st and May 31st, 2022, millions of raw sensor activity data samples were captured constantly at 100Hz sampling rates. The Human Activity Recognition (HAR) datasets, which keep a record of such activities and report associations in activity patterns (mostly used for recognizing the Activities of Daily Living (ADL)), were lacking. This paucity is addressed by the FLAAP dataset which can be useful in finding the associated patterns in ADL. The obtained experimental findings demonstrate that the learning algorithm Random Forest (RF), which was used, has recognized the activities with around 77.22% accuracy. The applied RF learning algorithm on the FLAAP dataset provides the research gap for the researchers in developing more delicate learning models for enhancing recognition rates. Furthermore, the research community could be particularly interested in examining the learning performance of algorithms while using various data pre-processing techniques, transferring knowledge to target domains, and other techniques.
Discriminator feature-based progressive GAN inversion

Generative Adversarial Networks (GANs) receive extensive attention due to its capability of high-fidelity image synthesis. The synthesized content can be edited by imposing transformations on the latent codes. To edit real images, the GAN inversion methods mainly focus on learning an inverse mapping from the data space to the latent space of a well-trained GAN through a separate encoder. In this paper, we propose a Discriminator Feature-based Progressive Inversion (DFPI) model for GAN-based image reconstruction and enhancement. We find that the expressiveness of the generator feature is richer than that of the latent code for a given input image. To achieve high-quality reconstruction, we estimate the generator features in a light-weight optimization process, conditioned on off-the-shelf discriminator features. Specifically, a recomposition matrix is learnt to map high-level discriminator features to the high-level generator features due to their reverse structure. To progressively improve the reconstruction quality, we adopt a convolutional correction module to alleviate the estimation error at a lower generator layer, conditioned on the features of a lower discriminator layer. By performing the progressive correction process, the input images can be accurately reconstructed. Note that the resulting recomposition matrix and correction module can be generalized to unseen images. The experiments demonstrate the superior performance of DFPI over competing methods on GAN inversion as well as a variety of image enhancement tasks.
Functional Resonance Analysis of Experts’ Monitoring Features in Steel Plate Processing

The advancement of automation technologies in the manufacturing industry has transformed human work into monitoring complex processes and intervening as needed. These operations necessitate the ability to adapt to variabilities that arise during the process. This ability concerns interactions between operators, automated machines, and the environment that constitutes socio-technical systems becoming more complex as technology advances. However, the process of demonstrating adaptive skills is unknown because the operators have acquired those skills as tacit knowledge. In this paper, we investigate the experts' adaptive skills to cope with multitasking focusing on the monitoring feature using the Functional Resonance Analysis Method (FRAM). First, we formulate a hypothesis based on the attention allocation characteristics during multitasking, using eye-tracking experiments and interviews. Next, we examine the hypothesis by incorporating the attention characteristics into FRAM models and conducting a simulation study that envisions behavior change caused by the encounter of multitasking. Compared with previous studies on approaches to tacit knowledge, this research's novel point is to analyze experts' features concerning interactions emerging with the environment using the function-based modeling method. The results show that the expert's attention features represented by the FRAM model structure are essential to the adaptive skill to manage variabilities in the working environment. Our research will contribute to elucidating the process of demonstrating adaptive skills in the manufacturing industry.
Legal Service Delivery and Support for the DAO Ecosystem

The Distributed Autonomous Organization (DAO) development community seeks to decentralize decision making and reduce the imposition of non-consensual constraints on organizational operations to the greatest extent possible. Deploying blockchain, cryptocurrency, smart contracts and Artificial Intelligence applications, a virtual organization can be created through emerging technologies which functions autonomously in a decentralized manner. A DAO allows its members to democratize decision making through approved rules and policies which are executed automatically consistent with the group’s decentralized decisions. In contrast, the world’s legal systems and the enforcement of their decisions represent a highly centralized approach to making agreements and resolving disputes that arise from them. This paper will make the case for and recommend the operational details of a new and complete legal ecosystem for the DAO community. Deploying the benefits of decentralized decision making and problem solving, LegalDAOs can be developed to augment distributed autonomous consensus serving the community in lieu of coercive, hierarchical and non-consensual decision making.