Reviewerly/exHarmony huggingface dataset:
A knowledge representation learning model based on relation rotation in two-dimensional Minkowski space

The knowledge graph is a structured representation of knowledge, which typically employs a triplet to convey information.However, the longtail distribution that characterizes most large-scale knowledge bases poses serious challenges regarding entity and relationship information in the long-tail segment, such as data sparsity and incomplete knowledge.To address these challenges and enhance the completeness of the knowledge graph, this paper proposes a novel model for knowledge representation learning in the context of knowledge graph completion.Specifically, the model maps entities to a two-dimensional Minkowski space and defines relationships as rotations.Additionally, the Adam optimizer is employed to optimize the training process.Finally, the model's efficacy is evaluated via link prediction tasks using public datasets FB15k, WN18, FB15k-237, and WB18RR, with results being compared to those derived from classical knowledge representation models.The empirical analysis reveals that the proposed model achieves superior performance across most evaluation metrics.
Rescue Wear the Cutting Edge Wearable Technology for Emergency Operations

An electronic gadget that may be worn on the wrist of children or adults to monitor and keep an eye on them is called a wearable smart locator band. People are often explorers by nature, thus they like to try new things in life. However, unexpected event of breathing difficulty, choking symptoms, and loss of balance can all result in abnormal conditions or fatalities. Numerous fatalities frequently result from sudden organ failure or physical imbalance because people are unable to receive the proper care in time. The ultimate purpose of a surveillance system is to automatically interpret events occurring in such settings. At present there is a requirement for having a system that will consequently detect the individuals and alarm the caretaker at such risk, We would like to present a wearable prototype that would enable a person to send an alert message when they are in danger and record their vital signs in case of an emergency. The alert message assists us in locating the person so that the rescue effort can start as soon as feasible.
Platform for Teaching Speaking Skills Online in the Pandemic Era

Educators were faced with the unexpected difficulty of primarily teaching online. Many challenges arose as a result of the necessity to redesign instructional activities, including inconsistency, challenging time management, maintaining students' interest, and increasing cheating in online tests. Because of the COVID-19 epidemic, the Indonesian government has implemented policies in the school sector. Previously, the teaching and learning process, particularly in higher education, was conducted face to face, and it has now transitioned to entirely online learning. This scenario requires the teachers and students to use online learning, which has an impact on their learning experiences. Speaking skill is very essential for human beings in, with an order to be able to communicate with society. During the pandemic era, teachers and students face difficulties to study because of lockdown in some areas. Here, the platforms categorized as Vacaroo, Voki, Fotobubble, and VoiceThread for learning speaking in a fun way, and cherish.
Dynamic Tikhonov State Forecasting Based on Large-Scale Deep Neural Network Constraints

This work presents dynamic Tikhonov state forecasting based on large-scale deep neural network constraint for the solution to a dynamic inverse problem of electroencephalographic brain mapping. The dynamic constraint is obtained by using a large-scale deep neural network to approximate the dynamics of the state evolution in a discrete large-scale state-space model. An evaluation by using neural networks with several hidden layer configurations is performed to obtain the adequate structure for large-scale system dynamic tracking. The proposed approach is evaluated over two models of 2004 and 10,016 states in discrete time. The models are related to an electroencephalographic problem for EEG generation. A comparison analysis is performed by using static and dynamic Tikhonov approaches with simplified dynamic constraints. By considering the obtained results it can be concluded that the deep neural networks adequately approximate large-scale state dynamics by improving the dynamic inverse problem solutions.
Deep Learning Methods to Detect Image Falsification

The accessibility and advancement of digital image editing tools have enabled individuals to manipulate and create realistic images without a real basis, leading to novel forms of creative expression and new professions. However, this also raises concerns over the malicious use of these technologies in spreading disinformation and fabricated evidence. Deepfake videos, which are generated using deep learning techniques, have become a major concern due to their potential to spread false information and cause harm to individuals and society as a whole. Therefore, the development of accurate and efficient deepfake detection methods has become an urgent need. After a thorough review of deep learning-based approaches for detecting deepfake videos, the LRNet method was chosen as a basis for further research due to its high precision. The method is designed to analyze the temporal changes in a video and identify whether the video has been manipulated or not. Experiments were conducted using a publicly available dataset. The first step involved analyzing the impact of model parameters. A total of 135 combinations were analyzed by changing the block size, dropout rate, learning rate, and optimizer. Based on the results, the model’s performance was enhanced by reducing the initial dropout rate, decreasing the number of GRU hidden neurons, and adding additional linear and ReLU6 layers. Upon conducting and comparing the results, it becomes evident that the chosen and improved method achieves promising outcomes in deepfake detection. This demonstrates the effectiveness of the dual-stream RNNs and the calibration module in enhancing the accuracy of geometric feature detection over time. This approach offers a more reliable way to detect deepfake videos, which is essential for preventing their malicious use in various domains. The results show that our proposed improved method achieves higher accuracy in some scenarios and provides a valuable analysis on how detection models are affected by their parameters.
MFDANet: Multi-Scale Feature Dual-Stream Aggregation Network for Salient Object Detection

With the development of deep learning, significant improvements and optimizations have been made in salient object detection. However, many salient object detection methods have limitations, such as insufficient context information extraction, limited interaction modes for different level features, and potential information loss due to a single interaction mode. In order to solve the aforementioned issues, we proposed a dual-stream aggregation network based on multi-scale features, which consists of two main modules, namely a residual context information extraction (RCIE) module and a dense dual-stream aggregation (DDA) module. Firstly, the RCIE module was designed to fully extract context information by connecting features from different receptive fields via residual connections, where convolutional groups composed of asymmetric convolution and dilated convolution are used to extract features from different receptive fields. Secondly, the DDA module aimed to enhance the relationships between different level features by leveraging dense connections to obtain high-quality feature information. Finally, two interaction modes were used for dual-stream aggregation to generate saliency maps. Extensive experiments on 5 benchmark datasets show that the proposed model performs favorably against 15 state-of-the-art methods.
XFilter: An Extension of the Integrity Measurement Architecture Based on Fine-Grained Policies

The Integrity Measurement Architecture subsystem on the Linux platform is a critical security component in the kernel to ensure the integrity of the running system. However, the default Integrity Measurement Architecture policy mechanisms based on options such as file owner and FSMAGIC cannot achieve a file-level configuration. Although Integrity Measurement Architecture supports the Linux Security Module policy rules to be close to the goal of fine-grained configuration, it is not easy to be managed because the Linux Security Module was not originally designed for integrity measurement. Moreover, the Linux Security Module-based policy does not apply in some use cases considering the type of Mandatory Access Control tools chosen by users. This paper presents a new policy configuration option, named XFilter, that achieves a fine-grained policy configuration method. The XFilter includes two policy matching mechanisms, XLabel and XList, which share the same policy token created for XFilter exclusively. XLabel marks the files for measurement using a label in the file’s extended attribute (xattr). By contrast, XList stores the measurement information in a list of file paths. To simplify the deployment, an automatic configuration process is implemented for integrating into the package management system. The evaluation results suggest that both mechanisms satisfy the requirements of file-level IMA policy control and create a performance burden for system operation in the acceptable range. They also reveal a positive correlation between the increment of the system latency and the growth of the length of file paths list for the XList mechanism.
Rapid and sustained contact tracing training for COVID-19 in San Francisco: a training model for developing an emergency public health workforce

The City and County of San Francisco was the first municipality in the United States to institute a COVID-19 contact tracing program. The San Francisco Department of Public Health (SFDPH) and the University of California, San Francisco (UCSF) created an outcome-based fully remote contact tracing curriculum using participatory learning methods to train non-public health emergency workers as contact tracers. Between April and December 2020, we trained over 300 individuals in contact tracing skills and procedures over three training phases. Using iterative curriculum design and Kirkpatrick’s evaluation methodology, we aimed to ensure high quality and successful person-centered contact tracing. The resulting curriculum consisted of 24 learning outcomes taught with six participatory skills development activities, asynchronous materials, and one-on-one contact tracer support. We collected more than 700 responses from trainees using various evaluation tools across the training phases, and contact tracers interviewed more than 24,000 contacts after training in our program. Our evaluations showed that knowledge and skills improved for most trainees and demonstrated the utility of the training program in preparing trainees to perform person-centered contact tracing in San Francisco. Local health jurisdictions and state health agencies can use this model of curriculum development and evaluation to rapidly train a non-public health workforce to respond to future public health emergencies.
An algebraic attack on stream ciphers with application to nonlinear filter generators and WG-PRNG

In this paper, we propose an algebraic attack on stream ciphers.Starting from a well-known attack due to Courtois and Meier, we design an attack especially effective against nonlinear filter generators.First, we test it on two toy stream ciphers and then we show that the level of security of WG-PRNG, one of the stream ciphers submitted to the NIST competition on Lightweight Cryptography, is less than that claimed until now.
Adaptive update handling for graph HTAP

Abstract Hybrid transactional/analytical processing (HTAP) workloads on graph data can significantly benefit from GPU accelerators. However, to exploit the full potential of GPU processing, dedicated graph representations are necessary, which mostly make in-place updates difficult. In this paper, we discuss an adaptive update handling approach in a graph database system for HTAP workloads. We discuss and evaluate strategies for propagating transactional updates from an update-friendly table storage to a GPU-optimized sparse matrix format for analytics.
The Appendix of Comment according to Language into Act Theory

The article deals with the détachement instances, an aspect of spoken language differing from the binary structure (Topic-Comment) considered to both semantically and informationally form the basic unit of spoken language. According to Language into Act Theory, détachment instances are considered specific information units called Appendix of Comment (APC), with a clear distinction from the Topic unit. The APC may be formally identified in the corpus through its distribution after the Comment and its prosodic performance via a suffix unit. The APC records a frequency value of 4.28% of reference units, which is significantly lower than that of the Topic (close to 20%). The morpho-syntactic fillings of the APC show a kind of “randomness”, that cannot truly be generalized, unlike Topic, since they are employed “in the moment”, as late adjunctions, echoes, repetitions, deictics, and formulas. APC doesn’t constitute a syntactic/semantic island, as Topic does, and its content is ultimately irrelevant, lacks an independent modality and is de-focused. The APC’s features confirm the specificity of its information function in order to textually integrate with the Comment, facilitating an agreement with the addressee, while the Topic serves as the field of application for the illocutionary force. The purpose of the article is to explain the distinction between the previous information functions by demonstrating the specific role of Appendix through examples derived from Italian corpus analysis.
Special Issue on Cryptography and Information Security

In today’s increasingly connected world, the demand for secure computing and data protection is soaring [...]
Aggregated mental models predict observed outcomes following Eurasian Beaver (Castor fiber) reintroduction

Outcome prediction is important for conservation; however, analysis may be hampered by specialist resource deficiencies. Mental modelling techniques offer a potential solution, drawing on accessible sources of knowledge held informally by local stakeholders. Mental models show linked social and ecological variables from the perspectives of community members, whose insights may otherwise be neglected. Currently, an important weakness in conservation mental modelling is inadequate attention paid to real-time model predictive validity. To address this knowledge gap, baseline mental model predictions concerning Beaver (Castor fiber) reintroduction in Southwest England were followed up at three years. Participants were invited to submit outcome observations for concept variables identified in their original models, blind to inferences based on model dynamic analysis, so that the two sets of data could be compared. Individual concept values and models were found to show weak and highly inconsistent predictive validity, however, multi-stakeholder aggregated mental models showed consistently strong predictive performance. This finding was enhanced by setting tighter thresholds for inclusion of individual model items in aggregation procedures. Threshold effects can be interpreted as a reflection of greater agreement: tighter thresholds retain more highly shared model components. It is proposed that enhanced real-time predictive validity for aggregated models is explained by a ‘wisdom of the crowd’ statistical effect, analogous to well-recognised crowd judgement effects observed in relation to much simpler questions. The findings show the scope for stakeholder mental modelling methods as an investigative tool, to supplement more conventional ecosystem assessments in predicting data-poor conservation outcomes.
Implementation of K-Means Clustering Method to Lecturers Based on Publications of National Journals and Accredited Sinta

Lecturer research or scientific results published both nationally and with Sinta accreditation. This is done to determine the level of publication of lecturer research results that have been published so that they can be easily disseminated so that the knowledge gained influences knowledge in general and has an impact on people's lives. The problem in this research is how to apply the K-Means clustering method with reference to the results of lecturer research publications based on national journals and sinta accredited journals. In this study, the researchers used the K-Means method, which has the goal of minimizing the objective function that has been set in the clustering process and maximizing the variation of data in other clusters. So researchers will look for the best number of clusters in the K-means method. The discussion is carried out based on the data that has been collected in the form of national journal weights and sinta accredited journal weights by utilizing Matlab R2013b software. From the results of system testing, lecturer clustering was obtained related to the results of journal weights, namely lecturers with high national scores and no sinta accreditation weights, lecturers with quite high national weights and high sinta accreditation weights, moderate national weights and low sinta accreditation weights, low national weights and high weights with low Sinta accreditation. This study resulted in the conclusion that the data was processed through several stages, starting with calculating the weight value of each issue of the national journal and sinta accredited journal. Then the results of these weights are recapitulated based on each lecturer so that it can be seen the total value of the lecturers' weights in publishing research results in the form of journals that have been published both in the form of national publications and sinta publications.
Smoothness-based consistency learning for macaque pose estimation

Macaques are a rare substitute and play an important role in study of human psychology and spiritual science. Accurate estimation of macaque pose information is key to these studies, macaque pose estimation remains to be hindered by the scarcity of labeled images. To address this problem, this work introduces a novel semi-supervised approach called smoothness-based spatio-temporal consistency learning (SSTCL) and a dual network structure (DNS) to leverage the amounts of unlabeled real images. Specifically, the SSTCL introduces the smoothness assumption to help the model generalize from the labeled training images to the unlabeled images, and the spatio-temporal consistency is designed to leverage both spatial and temporal consistencies to pick the most reliable pseudo-labels. Moreover, a dual network structure (DNS) is proposed to empower the model the ability of self-correction, which can prevent the degeneration caused by the noisy pseudo-labels in semi-supervised learning. In ablation experiments, the effectiveness of DNS for pseudo-label quality assurance is demonstrated. We evaluate the proposed method on the public OpenMonkeyPose dataset, the results show that the proposed method can achieve competitive performance while using less labeled images, and the final accuracy surpasses the strong baseline HRNet-w48 of 2.1 AP.
Financial Development and Human Development in Nigeria

This study examines the relationship between financial development indicators and human development in Nigeria from 1990-2019. It investigates the effect of broad money supply/Gross Domestic Product (GDP) on Human development; it examines the impact of credit supply/GDP on human development and assesses the link between market capitalization and human development. The study employs expo-facto research design and Autoregressive Distributed Lag to examine the relationship between Financial Development and Human Development. Previous studies in Nigeria had focused on financial development and economic growth, financial deepening and economic growth. Therefore, this study is a response to the dearth of relevant empirical studies on financial development and human development in Nigeria. From the results, the long run net effect of broad money supply/GDP on human development is negligible and positive. M2/GDP in Nigeria only account for the extent of monetization rather than financial intermediation. The long run net effect of credit supply/GDP on human development is negligible and positive. The long-run effect of M2/GDP, CPS/GDP are statistically significant but has no power to substantially influence human development in Nigeria. The study suggests that banks should effectively perform their intermediation roles and effort should be made by the policy makers to widen/broaden the Nigeria capital market activity. Policy makers should concentrate on financial system and their roles for effective money supply and credit supply while implementing economic policies.
Protecting by attacking: A personal information protecting method with cross-modal adversarial examples

Recent years’ development of AI technology brings more convenience to our life while at the same time increasing the risk of personal information leakage. In this work, we try to protect personal information contained in the images by generating adversarial examples to fool the image captioning models. The generated adversarial examples are user-oriented which means the users can manipulate or hide sensitive information on the text output as they wish. By doing so, our personal information can be well protected from image captioning models. To fulfill the task, we adopt five kinds of adversarial attack. Experimental results show our method can successfully protect user security. The Pytorch® implementations can be downloaded from an open-source GitHub project (https://github.com/Dlut-lab-zmn/Image-Captioning-Attack/).
ANALISIS KEMAMPUAN SOSIOLINGUISTIK SISWA KELAS V DITINJAU DARI SELF CONCEPT PADA MODEL PROBLEM BASED LEARNING

Sociolinguistic competence is still classified as not good. This study aims to find patterns of sociolinguistic competence in terms of self-concept in Dilema Based Learning bentuk. The study used was a mixed method with concurrent embedded design. The study was conducted with fifth grade students at Cipocok Berhasil Public Elementary School in Serb Banten. The type of research information was obtained from pre- and post-tests, questionnaires, and interviews. The information analysis used in this study was normality test, homogeneity test, independent samples t-test, and gain n test. The results of this study revealed the following: 1) the post-test scores of the experimental class increased; 2) different patterns of sociolinguistic competence were found in the groups with low, biasa, and high self-concept.