Reviewerly/exHarmony huggingface dataset:
Energy-efficient optimization strategy for simulation task scheduling based on supercomputing platform

To address the problems of high energy consumption and underutilization of resources in the supercomputing platform of the grid center, a regression analysis is used to deal with the relationship between resource usage data and energy consumption, and an energy consumption evaluation model is created. The proposed energy consumption assessment model is combined with the existing simulation task scheduling strategy to propose an energy consumption-aware load balancing strategy to improve the overall resource utilization rate from the perspective of improving the load balancing of computing nodes on the platform; a node adjustment algorithm based on real-time CPU utilization is proposed to dynamically adjust the number of nodes involved in computing. The task-node mapping relationship is scheduled according to the load of computing nodes to reduce the energy consumption of the platform without significant impact on the execution time of existing power simulation tasks. The energy consumption modeling and performance testing of the scheduling scheme are carried out under Linux system and through simulated simulation environment such as CloudSim. The experimental results show that the energy consumption evaluation model based on the system resource utilization rate can achieve about 95% accuracy; the total energy consumption of the two scheduling strategies is basically the same, and there is about 16% energy saving compared with the basic scheduling strategy and about 11% energy saving compared with the Max-Min scheduling strategy.
A Double-Compensation-Based Federated Learning Scheme for Data Privacy Protection in a Social IoT Scenario

Nowadays, smart wearable devices are used widely in the Social Internet of Things (IoT), which record human physiological data in real time. To protect the data privacy of smart devices, researchers pay more attention to federated learning. Although the data leakage problem is somewhat solved, a new challenge has emerged. Asynchronous federated learning shortens the convergence time, while it has time delay and data heterogeneity problems. Both of the two problems harm the accuracy. To overcome these issues, we propose an asynchronous federated learning scheme based on double compensation to solve the problem of time delay and data heterogeneity problems. The scheme improves the Delay Compensated Asynchronous Stochastic Gradient Descent (DC-ASGD) algorithm based on the second-order Taylor expansion as the delay compensation. It adds the FedProx operator to the objective function as the heterogeneity compensation. Besides, the proposed scheme motivates the federated learning process by adjusting the importance of the participants and the central server. We conduct multiple sets of experiments in both conventional and heterogeneous scenarios. The experimental results show that our scheme improves the accuracy by about 5% while keeping the complexity constant. We can find that our scheme converges more smoothly during training and adapts better in heterogeneous environments through numerical experiments. The proposed double-compensation-based federated learning scheme is highly accurate, flexible in terms of participants and smooth the training process. Hence it is deemed suitable for data privacy protection of smart wearable devices.
A New Multi Chaos-Based Compression Sensing Image Encryption

The advancements in technology have substantially grown the size of image data. Traditional image encryption algorithms have limited capabilities to deal with the emerging challenges in big data, including compression and noise toleration. An image encryption method that is based on chaotic maps and orthogonal matrix is proposed in this study. The proposed scheme is built on the intriguing characteristics of an orthogonal matrix. Gram Schmidt disperses the values of pixels in a plaintext image by generating a random orthogonal matrix using logistic chaotic map. Following the diffusion process, a block-wise random permutation of the data is performed using multi-chaos. The proposed scheme provides sufficient security and resilience to JPEG compression and channel noise through a series of experiments and security evaluations. It enables Partial Encryption (PE) for faster processing as well as complete encryption for increased security. The higher values of the number of pixels change rates and unified average change intensity confirm the security of the encryption scheme. In contrast to other schemes, the proposed approach can perform full and partial encryption depending on security requirements.
The Strategies of Service Delivery Adopted by Private Security Firms in Crime Prevention in Nairobi County, Kenya

National security is vital for achieving social, economic and political objectives. Given these critical aspects of security, a country across the globe can only satisfy the security need with support from other participants, such as the community and by partnering with private security firms. With the particular requirement and level of training that PSFs offer their personnel, a partnership of police and PSFs in crime detection and prevention is critical in providing security across the Country. This study sought to establish the service delivery strategies adopted by private security firms in dealing with crime. This study was modelled by the resource-based view theory s that offered a discernment of what options the private security firms present in addressing the diverse challenges emanating from ineffective strategic decisions in service delivery. A descriptive survey research design employing mixed methods approach in data collection was utilized to undertake the study. The target population constituted managers, supervisors, clients, and private security guards drawn from a sorted group of private security firms in Nairobi County. A multistage sampling approach employing clustering, proportionate random sampling and purposive sampling were used to obtain a sample size of 90 respondents. Questionnaires and interview guides were the main tools of data collection. Quantitative data were analyzed using descriptive statistics and presented in tables and charts. Qualitative data was presented in quotes and analyzed thematically. The data findings showed that guarding and collaboration strategies were the main strategies of the PSFs. Based on the results, the study recommended implementing government policy and legislation on collaboration between security agencies and PSFs for effective service delivery, implementing an oversight control agency to check on professionalism and adopting modern and innovative technology by PSFs for effective crime prevention.
Text Complexity in Selected Newbery Medal Winners

Children who read for pleasure, as well as for information, do so because they have first learned the simple mechanics of reading. Once the mechanics are in place, children seek out stories that are of interest to them. Reading engaging materials holds the interest of children. As readers encounter increasingly complex reading material, their comprehension and reading abilities increase. It is important that children's literature is not only entertaining, but also complex in age-appropriate ways (Juel, 1988). This study looks at the text complexity of Newbery Medal winners. The books selected for this study cover a 100-year period (1922-2022). The study should be helpful to teachers and librarians in recommending Newbery Medal books based on text complexity. Educators and librarians may use award-winning book lists as suggested reading without regard to the complexity of text. This research highlights the variation found within one list.
PENGARUH PEMBELAJARAN DARING TERHADAP HASIL BELAJAR IPA SISWA SMP

&lt;p&gt;&lt;em&gt;This study aims to determine the results of science learning outcomes of students in online learning using Google Meet and Google Classroom during Covid-19 pandemic. Population in this study were all class VIII as many as 210 students consisting of 7 classes. The sample in this study consisted of 2 classes with a total of 60 students. The sampling technique used was cluster random sampling technique and obtained 2 classes VIII-1 (Google Meet) and VIII-2 (Google Classroom). The type of this research is descriptive quantitative research using primary data analysis (questionnaire data) distributed using google form. The secondary data analysis was obtained from the School Final Examination data. The data were analyzed using SPSS 21 to get the mean, standard deviation (S), normality test, homogeneity test and hypothesis testing (t-test) at a significant level = 0.05. The results of hypothesis testing using SPSS obtained a significant 0.012 from t count (2.679), significant level = 0.05 and (df) = 58 obtained t table (2.001). If t count &amp;gt; t table is 2.679 &amp;gt; 2.001, then Ho is rejected and Ha is accepted, which means, there is an effect of online learning on junior high school students' learning outcomes. In terms of learning outcomes, classes that use Google Meet have higher learning outcomes than classes that use Google Classroom.&lt;/em&gt;&lt;/p&gt;
Iterative Translation Refinement with Large Language Models

Large language models have shown surprising performances in understanding instructions and performing natural language tasks. In this paper, we propose iterative translation refinement to leverage the power of large language models for more natural translation and post-editing. We show that by simply involving a large language model in an iterative process, the output quality improves beyond mere translation. Extensive test scenarios with GPT-3.5 reveal that although iterations reduce string-based metric scores, neural metrics indicate comparable if not improved translation quality. Further, human evaluations demonstrate that our method effectively reduces translationese compared to initial GPT translations and even human references, especially for into-English directions. Ablation studies underscore the importance of anchoring the refinement process to the source input and a reasonable initial translation.
Causal interventions expose implicit situation models for commonsense language understanding

Accounts of human language processing have long appealed to implicit ``situation models'' that enrich comprehension with relevant but unstated world knowledge. Here, we apply causal intervention techniques to recent transformer models to analyze performance on the Winograd Schema Challenge (WSC), where a single context cue shifts interpretation of an ambiguous pronoun. We identify a relatively small circuit of attention heads that are responsible for propagating information from the context word that guides which of the candidate noun phrases the pronoun ultimately attends to. We then compare how this circuit behaves in a closely matched ``syntactic'' control where the situation model is not strictly necessary. These analyses suggest distinct pathways through which implicit situation models are constructed to guide pronoun resolution.
NFT.mine: An xDeepFM-based Recommender System for Non-fungible Token (NFT) Buyers

Non-fungible token (NFT) is a tradable unit of data stored on the blockchain which can be associated with some digital asset as a certification of ownership. The past several years have witnessed the exponential growth of the NFT market. In 2021, the NFT market reached its peak with more than $40 billion trades. Despite the booming NFT market, most NFT-related studies focus on its technical aspect, such as standards, protocols, and security, while our study aims at developing a pioneering recommender system for NFT buyers. In this paper, we introduce an extreme deep factorization machine (xDeepFM)-based recommender system, NFT.mine, which achieves real-time data collection, data cleaning, feature extraction, training, and inference. We used data from OpenSea, the most influential NFT trading platform, to testify the performance of NFT.mine. As a result, experiments showed that compared to traditional models such as logistic regression, naive Bayes, random forest, etc., NFT.mine outperforms them with higher AUC and lower cross entropy loss and outputs personalized recommendations for NFT buyers.
Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short Term Memory and Autoencoder

Currently, the wide spreading of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspections (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the above methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmision requirements to many Internet users using SDN capability and the potential of Deep Learning. Specifically, DNN, CNN, LSTM and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that does not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13 . Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.
BokehOrNot: Transforming Bokeh Effect with Image Transformer and Lens Metadata Embedding

Bokeh effect is an optical phenomenon that offers a pleasant visual experience, typically generated by high-end cameras with wide aperture lenses. The task of bokeh effect transformation aims to produce a desired effect in one set of lenses and apertures based on another combination. Current models are limited in their ability to render a specific set of bokeh effects, primarily transformations from sharp to blur. In this paper, we propose a novel universal method for embedding lens metadata into the model and introducing a loss calculation method using alpha masks from the newly released Bokeh Effect Transformation Dataset(BETD) [3]. Based on the above techniques, we propose the BokehOrNot model, which is capable of producing both blur-to-sharp and sharp-to-blur bokeh effect with various combinations of lenses and aperture sizes. Our proposed model outperforms current leading bokeh rendering and image restoration models and renders visually natural bokeh effects. Our code is available at: https://github.com/indicator0/bokehornot.
Value Functions are Control Barrier Functions: Verification of Safe Policies using Control Theory

Guaranteeing safe behaviour of reinforcement learning (RL) policies poses significant challenges for safety-critical applications, despite RL's generality and scalability. To address this, we propose a new approach to apply verification methods from control theory to learned value functions. By analyzing task structures for safety preservation, we formalize original theorems that establish links between value functions and control barrier functions. Further, we propose novel metrics for verifying value functions in safe control tasks and practical implementation details to improve learning. Our work presents a novel method for certificate learning, which unlocks a diversity of verification techniques from control theory for RL policies, and marks a significant step towards a formal framework for the general, scalable, and verifiable design of RL-based control systems. Code and videos are available at this https url: https://rl-cbf.github.io/
Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making

This paper investigates the prospect of developing human-interpretable, explainable artificial intelligence (AI) systems based on active inference and the free energy principle. We first provide a brief overview of active inference, and in particular, of how it applies to the modeling of decision-making, introspection, as well as the generation of overt and covert actions. We then discuss how active inference can be leveraged to design explainable AI systems, namely, by allowing us to model core features of ``introspective'' processes and by generating useful, human-interpretable models of the processes involved in decision-making. We propose an architecture for explainable AI systems using active inference. This architecture foregrounds the role of an explicit hierarchical generative model, the operation of which enables the AI system to track and explain the factors that contribute to its own decisions, and whose structure is designed to be interpretable and auditable by human users. We outline how this architecture can integrate diverse sources of information to make informed decisions in an auditable manner, mimicking or reproducing aspects of human-like consciousness and introspection. Finally, we discuss the implications of our findings for future research in AI, and the potential ethical considerations of developing AI systems with (the appearance of) introspective capabilities.
Analysis of Mary Halvorson's Improvisational and Compositional Style

Mary Halvorson has established herself as one of the leading figures of free improvised music since the turn of the millennium, winning DownBeat Magazine’s Best Guitarist category in 2017, 2018 and 2019 and receiving a “Genius” Grant from the John D. and Catherine T. MacArthur Foundation in 2019. Despite these accolades—and the many reviews of her albums and performances, interviews, and opinion pieces in the jazz press—there has been a marked dearth of scholarly literature dedicated to analyzing her music and her guitar playing to date. This article is intended to open the door to a more critical discourse about Halvorson’s artistry, achievements, and her position in the intersecting jazz, avant-garde, and free improvising communities. Throughout, I draw extensively on interviews I conducted with Halvorson and one of her early mentors, guitarist/educator Joe Morris as well as my own transcriptions of her work to detail how Halvorson approaches composing and interpreting music in a meticulous manner while still preserving space for the adventurous and sometimes chaotic aesthetic of free improvised music.
NTKCPL: Active Learning on Top of Self-Supervised Model by Estimating True Coverage

High annotation cost for training machine learning classifiers has driven extensive research in active learning and self-supervised learning. Recent research has shown that in the context of supervised learning different active learning strategies need to be applied at various stages of the training process to ensure improved performance over the random baseline. We refer to the point where the number of available annotations changes the suitable active learning strategy as the phase transition point. In this paper, we establish that when combining active learning with self-supervised models to achieve improved performance, the phase transition point occurs earlier. It becomes challenging to determine which strategy should be used for previously unseen datasets. We argue that existing active learning algorithms are heavily influenced by the phase transition because the empirical risk over the entire active learning pool estimated by these algorithms is inaccurate and influenced by the number of labeled samples. To address this issue, we propose a novel active learning strategy, neural tangent kernel clustering-pseudo-labels (NTKCPL). It estimates empirical risk based on pseudo-labels and the model prediction with NTK approximation. We analyze the factors affecting this approximation error and design a pseudo-label clustering generation method to reduce the approximation error. We validate our method on five datasets, empirically demonstrating that it outperforms the baseline methods in most cases and is valid over a wider range of training budgets.
ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function

Self-supervised contrastive learning (CL) has achieved state-of-the-art performance in representation learning by minimizing the distance between positive pairs while maximizing that of negative ones. Recently, it has been verified that the model learns better representation with diversely augmented positive pairs because they enable the model to be more view-invariant. However, only a few studies on CL have considered the difference between augmented views, and have not gone beyond the hand-crafted findings. In this paper, we first observe that the score-matching function can measure how much data has changed from the original through augmentation. With the observed property, every pair in CL can be weighted adaptively by the difference of score values, resulting in boosting the performance of the existing CL method. We show the generality of our method, referred to as ScoreCL, by consistently improving various CL methods, SimCLR, SimSiam, W-MSE, and VICReg, up to 3%p in k-NN evaluation on CIFAR-10, CIFAR-100, and ImageNet-100. Moreover, we have conducted exhaustive experiments and ablations, including results on diverse downstream tasks, comparison with possible baselines, and improvement when used with other proposed augmentation methods. We hope our exploration will inspire more research in exploiting the score matching for CL.
CFDP: Common Frequency Domain Pruning

As the saying goes, sometimes less is more -- and when it comes to neural networks, that couldn't be more true. Enter pruning, the art of selectively trimming away unnecessary parts of a network to create a more streamlined, efficient architecture. In this paper, we introduce a novel end-to-end pipeline for model pruning via the frequency domain. This work aims to shed light on the interoperability of intermediate model outputs and their significance beyond the spatial domain. Our method, dubbed Common Frequency Domain Pruning (CFDP) aims to extrapolate common frequency characteristics defined over the feature maps to rank the individual channels of a layer based on their level of importance in learning the representation. By harnessing the power of CFDP, we have achieved state-of-the-art results on CIFAR-10 with GoogLeNet reaching an accuracy of 95.25%, that is, +0.2% from the original model. We also outperform all benchmarks and match the original model's performance on ImageNet, using only 55% of the trainable parameters and 60% of the FLOPs. In addition to notable performances, models produced via CFDP exhibit robustness to a variety of configurations including pruning from untrained neural architectures, and resistance to adversarial attacks. The implementation code can be found at https://github.com/Skhaki18/CFDP.
Progress on $(g-2)_μ$ from Lattice QCD

I review the status of lattice QCD calculations of the hadronic contributions to the muon's anomalous magnetic moment, focussing on the hadronic vacuum polarisation contribution which dominates the uncertainty of the Standard Model prediction.This quantity exhibits a tension between recent lattice QCD results and the traditional data-driven dispersive method. I discuss the implications for the running of the electromagnetic coupling and the consistency of global fits using electroweak precision data.