Reviewerly/exHarmony huggingface dataset:
Detecting unregistered users through semi-supervised anomaly detection with similarity datasets

Abstract Recent research has focused on exploring systems that incorporate anomaly detection models to automate the addition of users in user recognition systems. Anomaly detection, a method used to distinguish between new and existing users by identifying abnormal images, has gained significant attention. Researchers have been actively investigating the Semi-Supervised Learning method, which utilizes only existing user data to differentiate between existing and new users. However, existing semi-supervised learning based anomaly detection models exhibit high performance on datasets with low similarity but experience a sharp decline in performance on datasets with high similarity. Furthermore, their large model size makes it challenging to execute them on edge nodes. To address these limitations, this paper proposes a model that can be executed on edge nodes and guarantees good performance on both low and high similarity datasets. The proposed model utilizes the LeNet-5, a user recognition model with fewer weights and multiple images as input, for classifying new users. This study compared the existing anomaly detection models with the proposed model using three datasets with varying similarities. The performance evaluation involved comparing the accuracy, ROC curve, and AUC of each model on a training server. Subsequently, the top three models were optimized for execution on the edge node (STM32F207ZG MCU) and further evaluated by comparing their accuracy, inference speed, and model size. The results revealed that the proposed model achieved an approximate 53% improvement in accuracy compared to the existing anomaly detection models. Furthermore, when executed on the edge node, the proposed model demonstrated significant memory savings, with a maximum reduction of approximately 530% and approximately 40% reduction in flash memory usage compared to the existing models.
Geoinformatics (NSF)

Federal Grants & ContractsVolume 47, Issue 14 p. 4-4 Grants Alerts Geoinformatics (NSF) First published: 12 June 2023 https://doi.org/10.1002/fgc.33087Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue1415 June 2023Pages 4-4 RelatedInformation
Hardware accelerators for deep reinforcement learning

Recently, deep reinforcement learning (DRL) algorithms have been adapted for real-time control and policy-based decision for robots, drones, and autonomous vehicles. Traditional implementations of DRL use general purpose computing (CPU) and graphical processing units (GPU). In current work, we present HardCompress as an optimized hardware configuration for neural network (DNN) accelerators using High Level Synthesis (HLS) techniques.
Improving Student Learning Outcomes through Utilization of Interactive Learning Media with Creative Problem Solving

Acceptance of information technology changes the paradigm through the internet network used on smartphones. The results of observations at SMKN 7 Malang with teachers of basic graphic design subject in class during the COVID-19 pandemic which were held remotely through their respective homes, that the use of conventional models and PowerPoint media used by teachers is less easy to explain basic lessons graphic design, the PowerPoint produced through the material presented are difficult to accept, and the use of media in learning activities that is not maximized to be able to motivate students to improve learning outcomes. This study aims to design interactive learning media based on graphic design with creative problem-solving content in improving student learning outcomes in class X TKJ. This study uses the ADDIE development research model and Waterfall. The test subjects of this study included material experts, media experts, and X TKJ students in SMKN 7 Malang. From the results of development research, this interactive learning media obtained an average score of media experts with a value of 92.78 percent and material experts with a value of 82.97 percent. Then from the small group trial with a value of 91.20 percent, and from the large group trial with a value of 80.37 percent. The effectiveness of the product is evidenced by the student’s final post-test scores. In conclusion, creative problem-solving learning media content that was developed is very appropriate and effective for use in student learning activities at SMKN 7 Malang.
Quantum fuzzy genetic algorithm with Turing to solve DE

In this study, we create the quantum fuzzy Turing machine (QFTM) approach for solving fuzzy differential equations under Seikkala differentiability by combining it with a differential equation and a genetic algorithm. A theoretical model of computation called a quantum fuzzy Turing machine (QFTM) incorporates aspects of fuzzy logic and quantum physics.
The Effect of Sex Differences and Experience of Using Virtual Reality on Presence

Presence greatly affects user experience and comfort when using virtual reality (VR). Presence is often associated with personal factors such as sex differences and experience using the instrument. There is a research gap related to presence judging by several studies, so it is an interesting topic for further study. This research aims to identify the effect of sex differences and experience using VR on presence. This study used two approaches namely subjective indicators by employing an Igroup Presence Questionnaire (IPQ) and objective indicators in the form of heart rate (HR) and task scores. The study made use of Kruskal-Wallis and MANOVA to determine whether there is an effect of sex differences and experience in using VR on presence. This study found that the sex variable affects a person's presence when playing VR, especially spatial score on the IPQ test, where women have a higher marginal means value than men. Another finding is that the experience of playing VR affects the delta heart rate, with the result that someone with no experience using VR is higher than those who have used VR before.
Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analyze network traffic logs collected between January 2021 and August 2022 at the National Energy Research Scientific Computing Center (NERSC). Based on the observed patterns, we define a set of features primarily based on history for identifying low-performing data transfers. Typically, there are far fewer slow connections on well-maintained networks, which creates difficulty in learning to identify these abnormally slow connections from the normal ones. We devise several stratified sampling techniques to address the class-imbalance challenge and study how they affect the machine learning approaches. Our tests show that a relatively simple technique that undersamples the normal cases to balance the number of samples in two classes (normal and slow) is very effective for model training. This model predicts slow connections with an F1 score of 0.926.
State Disaggregation for Dynamic Scaling of Network Functions

Network Function Virtualization promises better utilization of computational resources by dynamically scaling resources on demand. However, most network functions (NFs) are stateful and require state updates on a per-packet basis. During a scaling operation, cores need to synchronize access to a shared state to avoid race conditions and to guarantee that NFs process packets in arrival order. Unfortunately, the classic approach to control concurrent access to a shared state with locks does not scale to today’s throughput and latency requirements. Moreover, network traffic is highly skewed, leading to load imbalances in systems that use only sharding to partition the NF states. To address these challenges, we present, a system that enables dynamic scaling of stateful NFs by disaggregating the states of network functions. By carefully coordinating actions between cores and a central controller, migrates shards and flows between cores for load balancing or traffic prioritization without resorting to locks or reordering packets. Also, ’s state disaggregation allows the offloading of stateful network functions to programmable NICs and makes it easier for exploring hardware-software tradeoffs that better suit specific service chains and traffic loads. Our experimental evaluation shows that reduces tail latency up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$32.04\%$</tex-math> </inline-formula> and increases throughput up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$19.36\%$</tex-math> </inline-formula> when compared to state-of-the-art competing solutions.
Hybrid self-attention NEAT: a novel evolutionary self-attention approach to improve the NEAT algorithm in high dimensional inputs

This article presents a “Hybrid Self-Attention NEAT” method to improve the original NeuroEvolution of Augmenting Topologies (NEAT) algorithm in high-dimensional inputs. Although the NEAT algorithm showed a significant result in different challenging tasks, as input representations are highly dimensional, it cannot create a well-tuned network. Accordingly, we decided to overcome this limitation by using the Self-Attention technique as an indirect encoding method to select the most important parts of the input. In order to tune the hyper-parameters of the self-attention module, we used the CMA-ES evolutionary algorithm. Also, an innovative method called Seesaw is presented in this article to evolve populations of the NEAT and CMA-ES algorithms simultaneously. Besides the evolutionary operators of the NEAT algorithm to update the weights, we used a combination method to reach more fitting weights. We tested our model on a variety of Atari games. The results showed that, compared to state-of-the-art evolutionary algorithms, Hybrid Self-Attention NEAT could eliminate the restriction of the original NEAT and achieve comparable scores with raw pixel input while using much smaller (e.g. approximately 300 × against HyperNEAT) number of parameters.
Evidence-Based Federated Learning for Set-Valued Classification of Industrial IoT DDos Attack Traffic

A novel Federated learning classifier is proposed using the Dempster-Shafer (DS) theory for the set-valued classification of industrial IoT Distributed Denial of Service (DDoS) attack traffic. The proposed classifier, referred to as the evidence-based federated learning classifier, employs convolution and pooling layers to extract high-dimensional features of Distributed Denial of Service (DDoS) traffic from the local data of private industrial clients. The characteristics obtained from the various participants are transformed into mass functions and amalgamated utilizing Dempster’s rule within the DS layer, situated on the federated server. Lastly, the set value classification task of attack mode is executed in the expected utility layer. A learning strategy is proposed to update the network parameters in a joint manner according to the weight of mass function value. Experiments on DDoS traffic classification tasks in CIC-DDoS2019 datasets prove that Federated learning, DS layer, and expected utility layer is effective in enhancing the multi-class classification accuracy, especially for those DDoS mixed attack traffic.
[Evaluation and Source Analysis of Soil Heavy Metal Pollution in a Planting Area in Wanquan District, Zhangjiakou City].

Taking the soil of a vegetable planting area in Wanquan District of Zhangjiakou City as the research object, 132 surface and 80 deep soil samples were collected to test and analyze the contents of eight heavy metals such as As, Cd, Cr, Hg, Cu, Ni, Pb, and Zn and the occurrence forms of Cr and Ni. By applying geostatistical analysis and the PMF receptor model and combining three heavy metal pollution evaluation methods, the spatial distribution characteristics of soil heavy metals in the study area, the degree of heavy metal pollution, and the distribution of Cr and Ni fugitive forms in the vertical layer were clarified, and the sources and contribution rates of soil heavy metal pollution were analyzed. The results showed that the average contents of Cd and Pb in surface soils were higher than the background values of soils in Hebei Province, and the spatial distribution characteristics of Cr, Ni, Cu, Cd, Pb, and Zn in surface soils were similar. The ground accumulation index method showed that the study area was mainly free of pollution, with a small number of lightly polluted sites, and most of them were polluted with Cd. The enrichment factor method showed that the study area was mainly free-weakly polluted, with medium pollution of all elements; the significantly polluted elements in the background area were As, Pb, and Hg, and the significantly polluted element in the key area was Cd. The potential ecological risk index method showed that the study area was mainly lightly polluted, with local distribution. The potential ecological risk index method showed that the study area was mainly lightly polluted, with a local distribution of "medium" and "strong" risk points, with "very strong" risk points for Hg in the background area and "very strong" risk points for Cd in the focus area. These three evaluation results indicated that the background area was dominated by Cd and Hg pollution, whereas the focus area was dominated by Cd pollution. The study on the fugitive morphology of vertical soil showed that Cr was dominated by the residue state (F4) and supplemented by the oxidizable state (F3), and the vertical direction was dominated by the surface aggregation type and supplemented by the weak migration type. Ni was dominated by the residue state (F4) and supplemented by the reducible state (F2), and the vertical direction was dominated by the strong migration type and supplemented by the weak migration type. The sources of heavy metals in the surface soil were divided into three categories; Cr, Cu, and Ni were mainly from natural geological background sources. The contributions of Cr, Cu, and Ni were 66.9%, 66.9%, and 76.1%, respectively. As, Cd, Pb, and Zn were mainly from anthropogenic sources, with contributions of 77.38%, 59.2%, 83.5%, and 59.5%, respectively. Hg was mainly from dry and wet atmospheric deposition, with a contribution of 87.8%.
Python Java joint implementation of internet-based public opinion information collection

Nowadays, with the rapid development of the Internet era, the network, as a new media form, has spread rapidly in daily news reports and public opinions, and has also penetrated into today's social life. Compared with real life, people's opinions on the Internet are more active. Major or popular events are often discussed quickly by network users, forming public opinion. Many people want to express their ideas and opinions through the Internet, and some of them will forward and follow-up relevant information, but also cause huge public opinion pressure on relevant departments and the speech itself, even to unpredictable results. Therefore, this paper uses Python Java technology to establish a network public opinion information collection system. This system is mainly based on the current popular UML modeling tools, and uses this technology as much as possible in all subsequent descriptions by analyzing some cases and scenarios. The test results show that MySQL's read performance is about 30 times higher than that of Lucene based files when persistence is required and the read performance is guaranteed, which further demonstrates the efficiency of the system. This article has made remarkable achievements in the collection of public opinion and relevant information by constantly optimizing the Python Java technology on the Internet.
A Survey of Clustering Algorithms for High-Dimensional Data Mining

The increasing complexity and dimensionality of data in numerous domains, including bioinformatics, text mining, multimedia, and social network analysis, pose significant challenges to traditional clustering algorithms. This paper provides a comprehensive review of various clustering algorithms suitable for high-dimensional data mining. We begin by elaborating on the challenges and difficulties intrinsic to high-dimensional spaces, such as the curse of dimensionality and the concentration of measure. Following that, we investigate a variety of different approaches to clustering, such as partitioning techniques, grid-based methods, hierarchical methods, density-based methods, and model-based methods. These algorithms are examined with a particular emphasis on how well they deal with high-dimensional data and how well they deal with data noise, as well as how well they can scale and how easily they can be interpreted. The most current developments in subspace and correlation clustering, as well as embedding approaches and the application of deep learning to the clustering of high-dimensional data, are also included in this overview. With the help of this in-depth analysis, our goals are to give insights into the benefits and drawbacks of each algorithm and to aid academics and practitioners in picking the optimal approach for the high-dimensional data mining projects they are working on.
Examining the Influence of Human Resource Practices on Entrepreneurial Orientation: A Quantitative Study

This study aims to investigate the influence of human resource practices on entrepreneurial orientation (EO) in the context of startups in Syria. The research focuses on 403 employees working in various startup organizations. By utilizing a quantitative approach, data will be collected through surveys to measure the perceptions of employees regarding human resource practices and entrepreneurial orientation. The findings of this study will contribute to the existing literature by providing insights into the relationship between human resource practices and entrepreneurial orientation in the unique context of startup organizations in Syria.
Pengaruh Metode SAS terhadap Hasil Belajar Membaca Bahasa Indonesia Kelas 1 SDIT Islamia

The method used in reading class must be able to provide maximum knowledge to students. This study aims to determine the effect of the SAS (Structural Analysis Synthetic) method application on reading outcomes in Indonesian Subject in grade 1st SDIT Islamia. This research using a quantitative experiment design with a sample of 24 students in experimental class and 24 students in control class and data analysis through the classical assumption test and t-test. Results of the study showed that the mean post-test score of the experimental class was higher than that of the control class. In addition, it is known that the value of tcount &gt; ttable, so it can be concluded that the SAS method application in reading class in 1st grade of SDIT Islamia has a significant influence on student learning outcomes.
How Shall a Machine Call a Thing?

This paper aims to investigate the feasibility of utilising Large Language Models (LLMs) and Latent Diffusion Models (LDMs) for automatically categorising word basicness and concreteness, i.e. two well-known aspects of language having significant relevance on tasks such as text simplification. To achieve this, we propose two distinct approaches: i) a generative Transformer-based LLM, and ii) a image+text multi-modal pipeline, referred to as stableKnowledge, which utilises a LDM to map terms to the image level. The evaluation results indicate that while the LLM approach is particularly well-suited for recognising word basicness, stableKnowledge outperforms the former when the task shifts to measuring concreteness.
Database of Rare Element Minerals of Ukraine

It is shown that the concept of "rare elements" is rather conditional. The list of rare elements, as well as their selection criteria, constantly changed over time. Geochemical and mineralogical criteria gradually became closely connected with technological and economic criteria. Such criteria as insignificant distribution in the earth's crust, weak mineral formation, and the lack of large deposits were joined by technological difficulties of extraction, minimal use in technology, unprofitable extraction, or artificial shortages due to monopolization of reserves by supplying companies. The list of critical mineral raw materials, which is based on rare chemical elements, is growing every year, with almost all the elements with a clarke of less than 100 ppm being in short supply. This encourages mineralogists to look out for a wider range of carrier minerals of rare elements than it was before, thus expanding the list of rare elements. It is reasonable to group carrier minerals of rare elements not by geochemical, but by chemical properties of the elements, since similar properties are crucial for their use in the same industries. The mineral database of Ukraine includes about 900 mineral species, and in six hundred of them rare elements are species- and speciation-forming. It is suggested to divide them into the following groups: minerals of alkaline (Li, Rb, Cs), alkaline earth (Be, Sr, Ra), transition (Sc, Zr, Hf, V, Nb, Ta, Mo, W, Re, Cd, and Hg), amphoteric (Ga, In, Sn, Tl, Bi), noble (Pt, Ru, Rh, Pd, Os, Ir, Au, Ag), rare earth (Y, La, Ce and other lanthanides) and radioactive (Th, U) metals, semi-metals (B, Ge, As, Sb, Te), non-metals and halides (Se, Br, I). The database should be supplemented with minerals of such low-clarke transition elements as Cu, Co, Cr, Ni, Zn, and Pb, which are predicted to be the elements of the future. Mineral resources of Ukraine contain significant potential for expanding the database of rare element minerals and increasing the reserves of critical raw materials.
A Genetic Algorithm-Based Framework for Learning Statistical Power Manifold

Statistical power is a measure of the replicability of a categorical hypothesis test. Formally, it is the probability of detecting an effect, if there is a true effect present in the population. Hence, optimizing statistical power as a function of some parameters of a hypothesis test is desirable. However, for most hypothesis tests, the explicit functional form of statistical power for individual model parameters is unknown; but calculating power for a given set of values of those parameters is possible using simulated experiments. These simulated experiments are usually computationally expensive. Hence, developing the entire statistical power manifold using simulations can be very time-consuming. We propose a novel genetic algorithm-based framework for learning statistical power manifolds. For a multiple linear regression F-test, we show that the proposed algorithm/framework learns the statistical power manifold much faster as compared to a brute-force approach as the number of queries to the power oracle is significantly reduced. We also show that the quality of learning the manifold improves as the number of iterations increases for the genetic algorithm. Such tools are useful for evaluating statistical power trade-offs when researchers have little information regarding a priori ‘best guesses’ of primary effect sizes of interest or how sampling variability in non-primary effects impacts power for primary ones.