Reviewerly/exHarmony huggingface dataset:
TIKD: A Trusted Integrated Knowledge Dataspace For Sensitive Healthcare Data Sharing

This paper presents the Trusted Integrated Knowledge Dataspace (TIKD), a new dataspace, based on linked data technologies and trusted data sharing, that supports integrated knowledge graphs for sensitive application environments such as healthcare. State-of-the-art shared dataspaces do not consider sensitive data and privacy-aware log records as part of their solutions, defining only how to access data. TIKD complements dataspace security approaches through trusted data sharing that considers personal data handling, data privileges, pseudonymization of user activity logging, and privacy-aware data interlinking services. TIKD was implemented on the Access Risk Knowledge (ARK) Platform, a socio-technical risk governance system, and deployed as part of the ARK-Virus Project which aims to govern the risk management of Personal Protection Equipment (PPE) across a group of collaborating healthcare institutions. The ARK Platform was evaluated, both before and after implementing the TIKD, using the ISO 27001 Gap Analysis Tool (GAT) which determines compliance with the information security standard. The results of the evaluation indicated that compliance with ISO 27001 increased from 50% to 85%. The evaluation also provided a set of recommended actions to meet the remaining requirements of the ISO 27001 standard. TIKD provides a collaborative environment, based on knowledge graph integration and GDPR-compliant personal data handling, as part of the data security infrastructure. As a result of this work, a new trusted data security methodology, based on personal data handling, data privileges, access control context specification, and privacy-aware data interlinking, was developed using a knowledge graph approach.
Foreign interference and Australian electoral security in the digital era

Australian elections are digitising. Electronic ballots, electronic certified lists, electronic scrutiny, and electronic data are becoming part of the electoral status quo. The digitisation of elections in Australia induces new vulnerabilities that malign foreign entities can exploit to subvert the nation’s democratic sovereignty. Problems such as inauthenticity, data insecurity, and disinformation are amplified in today’s epoch of digital era governance. Since these problems have the potential to erode the legitimacy of Australian elections, it is imperative to deepen understanding of these risks to optimise the nation’s democratic resilience. Through applying a conceptual framework derived from democratic theory and public policy, this paper assesses the vulnerability of Australia’s elections to foreign interference based on the Australian electoral system irrespective of adversary capabilities. It finds that Australia’s federal elections are relatively secure from hard cyber security risks due to digital-analogue hybridity in electoral processes, but they are vulnerable to soft cyber risks such as digital disinformation.
Group Behavior Pattern Recognition Algorithm Based on Spatio-Temporal Graph Convolutional Networks

With the rapid growth of population, more diverse crowd activities, and the rapid development of socialization process, group scenes are becoming more common, so the demand for modeling, analyzing, and understanding group behavior data in video is increasing. Compared with the previous work on video content analysis, factors such as the increasing number of people in the group video and the more complex scene make the analysis of group behavior in video face great challenges. Therefore, a group behavior pattern recognition algorithm based on spatio-temporal graph convolutional network is proposed in this paper, aiming at group density analysis and group behavior recognition in the video. A crowd detection and location method based on density map regression-guided classification was designed. Finally, a crowd behavior analysis method based on density grade division was designed to complete crowd density analysis and video group behavior detection. In addition, this paper also proposes to extract spatio-temporal features of crowd posture and density by using the double-flow spatio-temporal map network model, so as to effectively capture the differentiated movement information among different groups. Experimental results on public datasets show that the proposed method has high accuracy and can effectively predict group behavior.
RETRACTED: Impact of Feature Selection for Data Classification Using Naive Bayes Classifier

Abstract In the field of data processing and analysis, the dataset may be a large set of features that restrict data usability and applicability, and thus the dimensions of data sets need to be reduced. Feature selection is the process of removing as much of the redundant and irrelevant features as possible from the original dataset to improve the mining process efficiency. This paper presented a study to evaluate and compare the effect of filter and wrapper methods as feature selection approaches in terms of classification accuracy and time complexity. The Naive Bayes Classifier and three classification datasets from the UCI repository are utilizing in the classification procedure. To investigate the effect of feature selection methods, they are applied to the different characteristics datasets to obtain the selected feature vectors which are then classified according to each dataset category. The datasets used in this paper are the Iris, Ionosphere, and Ovarian Cancer dataset. Experimental results indicate that the filter and wrapper methods provide approximately equal classification accuracy where the average accuracy value of the Ionosphere and Ovarian Cancer dataset is 0.78 and 0.91 for the same selected feature vectors respectively. For Iris dataset, the filter method outperforms the wrapper method by achieving the same accuracy value using only half number of selected features. The results also show that the filter method surpasses when considering the execution time.
Research on Event Extraction Method Based on A Lite BERT And Conditional Random Field Model

Information extraction technology is used to extract hot information with high attention from unstructured text data. Event extraction technology is a challenging research direction in the field of information extraction. The purpose of event extraction is to extract key elements describing events from unstructured text data and present them in a structured way. Event extraction can be divided into two important stages: event trigger word extraction and event element extraction. Regarding event extraction as a sequence labeling task, the ALBERT pretraining model was used to learn the features, and the CRF feature function of conditional random field was introduced to learn the sequence features. By limiting the label relationship within a specific window, the performance of sequence labeling was improved. Event extraction experiments are carried out on ACE2005 standard corpus. the experimental results show that the ALBERT-CRF model has improved recall rate and F-score in the task of trigger word recognition and classification compared with the existing models.
Population-based coordinate descent algorithm with majority voting

Many real-world optimization problems belong to the class of expensive problems and the costly process of computing fitness value or gradient of the objective function may cause the failure of various optimization algorithms to solve them quickly. Because of the low computation and memory requirements of Coordinate Descent (CD) search methods they are suitable algorithms to optimize these problems. Despite the efficiency of CD methods, searching a large-scale search space just by using one candidate solution decreases the exploration capability of the algorithm. In this paper, a novel population-based version of the CD algorithm called Population-Based Coordinate Descent (PBCD) is proposed which is an efficacious method for tackling such problems using the collective intelligence and collaboration of the population. It takes advantage of three phases of locating the region of interest, folding the search space, and communication among the population members with majority voting to find more promising regions in the search space. As it shrinks the search space swiftly, it needs a low computational budget for finding the optimal value per coordinate and ultimately in overall. To investigate its performance, we benchmarked it on CEC-2017 test suite consisting of 29 low-scale problems with dimensions of 30, 50, and 100.
VPN-Zero: A Privacy-Preserving Decentralized Virtual Private Network

Distributed Virtual Private Networks (dVPNs) are new solutions aiming to solve the trust-privacy concern of a VPN's central authority by leveraging a distributed architecture. In this paper, we discuss the requirements of a successful dVPN system and we present VPN-Zero: a dVPN system with strong privacy guarantees that provides traffic accounting and has minimal performance impact on its users. VPN-Zero guarantees that a dVPN node only carries traffic it has “allowlisted”, without revealing its allowlist or knowing the traffic it tunnels. This is achieved via three main innovations: (a) an attestation mechanism which leverages TLS to certify a user visit to a specific domain, (b) a zero-knowledge proof to certify that some incoming traffic is authorized (e.g., falls in a node's allowlist, without disclosing the target domain), and (c) a dynamic chain of VPN tunnels to both increase privacy and guarantee service continuation while traffic certification is in place. The paper demonstrates VPN-Zero functioning when integrated with two production systems: BitTorrent's Distributed Hash Table and ProtonVPN. Early evaluation results show that the median setup time of VPN-Zero is about 10 seconds.
Identifying Valuable Pointers in Heap Data

Historically, attackers have sought to manipulate programs through the corruption of return addresses, function pointers, and other control flow data. However, as protections like ASLR, stack canaries, and no-execute bits have made such attacks more difficult, data-oriented exploits have received increasing attention. Such exploits try to subvert a program by reading or writing non-control data, without introducing any foreign code or violating the program’s legitimate control flow graph. Recently, a data-oriented exploitation technique called memory cartography was introduced, in which an attacker navigates between allocated memory regions using a precompiled map to disclose sensitive program data. The efficacy of memory cartography is dependent on inter-region pointers being located at constant offsets within memory regions; thus, cartographic attacks are difficult to launch against memory regions like heaps and stacks that have nondeterministic layouts. In this paper, we lower the barrier to successful attacks against nondeterministic memory, demonstrating that pointers between regions of memory often possess unique “signatures” that allow attackers to identify them with high accuracy. These signatures are accurate even when the pointers reside in non-deterministic memory areas. In many real-world programs, this allows an attacker that is capable of reading bytes from a single heap to access all of process memory. Our findings underscore the importance of memory isolation via separate address spaces.
Interfacing with Robots without the use of Touch or Speech

As the field of robotics develops, so do the methods of interfacing with and controlling those robots. Many modern robots can be communicated with using commands that require neither speech nor touch. The two main motivations behind this trend are the assistance of people with disabilities and the desire for more natural Human-Robot Interaction (HRI). In allowing nonverbal communication with robots, accessibility of the systems increases to allow more people to interact with and benefit from robotics systems. Additionally, nonverbal communication provides more natural communication, which can lead to many benefits in HRI. This paper provides an overview of existing technologies in touchless and nonverbal human-robot interfaces; it presents the most prevalent interface methods, their uses, and limitations. Discussions are provided on future directions for research.
Hierarchical Multihop Reasoning on Knowledge Graphs

Multihop knowledge reasoning aims to find missing entities for incomplete triples by finding paths on knowledge graphs. It is a fundamental and important task. In this article, we devise a hierarchical reinforcement learning algorithm to model the reasoning process more effectively. Unlike existing methods directly reason on entities and relations, we adopt a high-level reasoning layer to deal with abstract concepts, which guides the reasoning process conducted at the low level for concrete entities and relations. Our approach yields competitive results on link prediction on both NELL-995 and FB15k-237 datasets. The comparison to baselines also demonstrates the effectiveness of the hierarchical structure.
PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss

In this paper, we propose a self-supervised singleview pixel-level accurate depth estimation network, called PLADE-Net. The PLADE-Net is the first work that shows remarkable accuracy levels, exceeding 95% in terms of the δ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> metric on the challenging KITTI dataset. Our PLADENet is based on a new network architecture with neural positional encoding and a novel loss function that borrows from the closed-form solution of the matting Laplacian to learn pixel-level accurate depth estimation from stereo images. Neural positional encoding allows our PLADENet to obtain more consistent depth estimates by letting the network reason about location-specific image properties such as projection (and potentially lens) distortions. Our novel distilled matting Laplacian loss allows our network to predict sharp depths at object boundaries and more consistent depths in highly homogeneous regions. Our proposed method outperforms all previous self-supervised single-view depth estimation methods by a large margin on the challenging KITTI dataset, with unparalleled levels of accuracy. Furthermore, our PLADE-Net, naively extended for stereo inputs, outperforms the most recent self-supervised stereo methods, even without any advanced blocks like 1D correlations, 3D convolutions, or spatial pyramid pooling. We present extensive ablation studies and experiments that support our method’s effectiveness on the KITTI, CityScapes, and Make3D datasets.
Performance of Combining Hill Cipher Algorithm and Caesar Cipher Algorithm in Text Security

In data security, a technique is needed to secure data from experiments and attacks by cryptanalysts. The technique of securing data is also known as cryptography. In this research, it has been done to improve data security by combining the Hill Cipher Algorithm with the Caesar Cipher Algorithm. This process can be seen from the tests that have been carried out in this study. The result is in a different and sufficiently improved processing time both in terms of decryption and encryption. By combining the two cryptographic algorithms, it is known that message security is much better. It occurs because the encryption and decryption process of the message is carried out using 2 different keys. Based on the tests, it shows that the time of the encryption process with the decryption process on the 5×5 matrix key is very different from using the 3×3 matrix key, the difference is slightly different in time. It shows that the use of a larger matrix key affects the decryption process is very long and also influenced by other cryptographic algorithm processes. In this study, another cryptographic algorithm is the Caesar Cipher Algorithm.
Steganography using HTML Web Pages as a Carrier: A Survey

It is important to secure the personal information on web as people are enticed to share their information through the social media. So, it is very important to secure this data and obscure it. Some techniques such as Steganography, Cryptography and Watermarking are used to obnubilate the data. Steganography is the method to hide the secret data under a cover image where a cover image can be text, image or video. Cryptography is the technique of encrypting the message by using different methods. Watermarking is the technique to uniquely identify the images, videos or audios and prevent it from forgery. Steganography is preferred over cryptography as it is strenuous to detect and predict the hidden data from the cover files. HTML Steganography has advantage over other techniques as it hides the data under HTML web pages and the data does not look suspicious to the intruders as web pages are widely used over the internet.
An Effective Tumor Classification With Deep Forest and Self-Training

In recent years, tumor classification based on the gene expression omnibus has become a continuous attention field in the area of bioinformatics. Integration machine learning techniques are an efficient methods to solve these problems. Generally, in order to obtain good performance in the supervised learning tasks, a large number of labelled samples will be required. However, in many cases, only a few labelled samples and abundant unlabelled samples exist in the training database. The process of labelling these unlabelled samples manually is difficult and expensive. Therefore, semi-supervised learning approaches have been proposed to utilize unlabelled samples to improve the performance of a model. However, noisy samples decrease the robustness of model in semi-supervised learning. We wish training style that samples can be implemented to train by from high- to low-confidence, self-training can meet this requirement, and the deep forest approach with the hyper-parameter settings used in this work can obtain good accuracy. Therefore, in this paper, we present a novel semi-supervised learning approach with a deep forest model to increase the performance of tumor classification, which employs unlabelled samples and minimizes the cost; that is, a updated unlabelled sample mechanism is investigated to expand the number of high-confidence pseudo-labelled samples. Multiple real-world experiments indicate that our proposed approach can obtain results up 0.96 accuracy and F1-Score, and 0.9798 AUCs.
The pilot application of geochemical sourcing to an inland Pilbara archaeological landscape in north-western Australia

Past human mobility, often reconstructed using stone artefact assemblages, is an important component of Australian archaeology. Tracing the transport of stone artefacts enables direct measures of the direction and distance of past Aboriginal movement across landscapes. Sourcing transported stone artefacts to their geological point of origin therefore has the potential to significantly improve our understanding of human mobility. Beyond this, the identification of artefact source locations is also critical for stone artefact analyses since raw material factors strongly condition assemblage formation. However, relatively little stone sourcing has been completed in Australia. This is particularly the case for the inland Pilbara, north-western Australia, where almost no sourcing work has been published. This paper presents the results of a limited pilot study which aimed to test the application of petrographic and in situ laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) analytical techniques to the sourcing of transported artefacts from three Pilbara quarried outcrops to two non-quarried open surface sites. The results indicate that geochemical interelement ratios (e.g., La/Nd and U/Th) can discriminate between both quarry sites and artefacts. A clear petrographic and geochemical match between a quarry and one stone artefact discarded at a non-quarried site was found, and different samples collected from the same quarry yielded overlapping geochemical signatures. This suggests that LA-ICPMS has the potential to elucidate artefact transport (as a proxy for the movement of people) between locations, effectively linking these places as part of a behavioural system. While a larger database of artefact and quarry specimens is needed to fully explore both the efficacy of the approach and the archaeological implications, this study suggests that sourcing techniques hold significant promise for reconstructing past human mobility in the Pilbara and for demonstrating landscape-scale connections between sites.
Axel Berg’s Legacy in Cybernetics and Education. From the Council on Cybernetics to Axel Berg Institute

The history of the Scientific Council on Complex Problem “Cybernetics” of the Academy of Sciences of the USSR created at the initiative of academician and navy admiral, Axel Ivanovich Berg, is covered. Axel Berg's brilliant predictions, relating to the cybernetics development and digital transformation in education are described. The current continuation of Berg's traditions is highlighted.
Interlayer Restoration Deep Neural Network for Scalable High Efficiency Video Coding

This paper applies an interlayer restoration deep neural network (IRDNN) for scalable high efficiency video coding (SHVC) to improve visual quality and coding efficiency. It is the first time to combine deep neural network (DNN) and SHVC. Considering the coding architecture of SHVC, we elaborate a multi-frame and multi-layer neural network to restore the interlayer of SHVC by utilizing both the adjacent reconstructed frames of the base layer (BL) and enhancement layer (EL). Moreover, we analyze the temporal motion relationship of frames in one layer and the compression degradation relationship of frames between different layers, and propose the synergistic mechanism of motion restoration and compression restoration in our IRDNN. The network can generate an interlayer with higher quality serving for the EL coding and thus enhance the coding efficiency. A large-scale and various-quality-degradation dataset is self-made for the task of interlayer restoration of SHVC. The experimental results show that with our implementation on SHVC, the EL Bj <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> ntegaard delta bit-rate (BD-BR) reduction is 9.291% and 6.007% in signal-to-noise ratio scalability and spatial scalability, respectively. The code is available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://github.com/icecherylXuli/IRDNN</uri></i> .