Reviewerly/exHarmony huggingface dataset:
Profil self regulated learning dan peningkatan kemampuan kognitif peserta didik SMA melalui strategi writing to learn pada topik elastisitas bahan

The purpose of this study was to determine the effectiveness of writing to learn strategies in improving cognitive abilities and to identify students' self-regulated learning profiles. The research method used was a quasi experimental design with Nonequivalent Control Group Design. The population of this study were all students of class XI in a high school in Bandung. The research sample consisted of 60 students of class XI in a high school in Bandung who were selected by using purposive sampling technique. The instruments used were cognitive ability tests, self regulated learning questionnaires, and learning implementation observation sheets. The results showed that the implementation of the writing to learn strategy further improved the cognitive abilities of students with the n-gain value of the experimental class of 0.825 which was in the high category and the n-gain value of the control class of 0.623 which was in the moderate category. The implementation of the writing to learn strategy is effective in improving the cognitive abilities of students based on a significant difference between the experimental class and the control class and the effect size value of 0.823 which is in the high category. The self-regulated learning profiles of students in the experimental class and control class were mostly in the moderate category.
Understanding the Role of Population Experiences in Proximal Distilled Evolutionary Reinforcement Learning

Evolutionary Reinforcement Learning (ERL) combines the sample-efficiency property of Reinforcement Learning and exploration capabilities from the population-based search of Evolutionary Computation. These methods have shown promising performance on many continuous control tasks. However, one could observe the instability that may occur from such methods. Several works have shown that the experiences coming from the population individuals lead the state distribution shift in the RL policy updating process. A vanilla remedy method has been proposed to alleviate this issue by separating the experience transitions into two distinct replay buffers for the RL policy and the population and mixing the samples from the two buffers with a fixed ratio to update the RL policy. The effectiveness of this approach has been shown empirically on an ERL method where Evolution Strategies (ES) assists an external RL agent. Nevertheless, there has not been any thorough investigation on Genetic Algorithm (GA) based ERL to understand how this method performs on these ERL approaches. In this paper, we analyze the influence of off-policy data coming from the GA population to the RL policy and how the mixing method performs on a state-of-the-art ERL method, namely Proximal Distilled Evolutionary Reinforcement Learning (PDERL).
Improving test team performance through an Episodic Organizational Change implementation: A case study replication and extension

CONTEXT: Despite using organizational change interventions as adaptive mechanisms to meet external and internal demands, the software industry still faces challenges in implementing effective team restructuring and reorganizing their projects to maintain or improve the productivity and quality of their activities. GOAL: This paper presents the analysis of an Episodic Organizational Change (EOC) implemented in a matrix company in the software industry that culminated in the restructuring and reorganization of a multitasking test team responsible for ensuring coverage of different test scopes demanded by several projects simultaneously. METHOD: To compare and extend the findings regarding the implementation process of EOCs in the software industry, we replicated a case study that analyzed the motivations, action plan, critical success and failure factors, positive and negative impacts, lessons learned, and suggestions for improvements. RESULTS: The experience gained in this replication suggests that the steps and elements analyzed in this case study can become an EOC analysis process applied in the software industry. Compared to the original case study, the results of this replication corroborate with the motivations, goals, action plan implementation approach, and positive results. However, they diverge from generated negative results. Besides, they expand the findings related to critical factors. Finally, they suggest a pattern in the life cycle of this type of intervention. CONCLUSION: The findings substantiate that the implementation of EOCs that impact the dynamics of teams involved in the development of software projects directly influences the productivity and quality of their deliveries. These results underscore organizations' need for a careful and diligent approach in planning, managing, and implementing these interventions. The analysis of implemented EOCs can enhance the efficiency of this type of intervention, mitigating risks, seizing opportunities, and reducing the occurrence of failures.
Appraisal on Editorial Texts Related To Covid-19 in Newspaper in Dairi

This thesis aims to analyze the type and realization of Appraisal in the analysis of the content of the editorial texts of Dairi newspapers to find out the comparisons in each electronic newspaper by applying the three elements of Appraisal (attitude, positioning, and graduation). This research applies descriptive qualitative by using interactive model and document study as data collection. The data sources of this research are 20 phenomenal news texts from four different electronic newspapers and the data used in carrying out this research are the words, clauses contained in the text of the electronic newspaper. From the results of the analysis, it was found that the Appraisal contained in the editorial text of the regional electronic newspaper consisted of the engagement category with 97 clauses (29%), the judgment category with 94 clauses (28%), the appreciation category with 62 clauses (18%), the graduation category as much as 61 clauses (18%), and the last is the Affect category with 23 clauses (7%). Based on these findings, the subsystem that dominates the news text is the engagement subsystem.
Faster Bootstrapping via Modulus Raising and Composite NTT

FHEW-like schemes utilize exact gadget decomposition to reduce error growth and ensure that the bootstrapping incurs only polynomial error growth. However, the exact gadget decomposition method requires higher computation complexity and larger memory storage. In this paper, we improve the efficiency of the FHEWlike schemes by utilizing the composite NTT that performs the Number Theoretic Transform (NTT) with a composite modulus. Specifically, based on the composite NTT, we integrate modulus raising and gadget decomposition in the external product, which reduces the number of NTTs required in the blind rotation from 2(dg + 1)n to 2(⌈dg⌉/2 + 1)n. Furthermore, we develop a modulus packing technique that uses the Chinese Remainder Theorem (CRT) and composite NTT to bootstrap multiple LWE ciphertexts through one blind rotation process.We implement the bootstrapping algorithms and evaluate the performance on various benchmark computations using both binary and ternary secret keys. Our results of the single bootstrapping process indicate that the proposed approach achieves speedups of up to 1.7 x, and reduces the size of the blind rotation key by 50% under specific parameters. Finally, we instantiate two ciphertexts in the packing procedure, and the experimental results show that our technique is around 1.5 x faster than the two bootstrapping processes under the 127-bit security level.
TeeJam: Sub-Cache-Line Leakages Strike Back

The microarchitectural behavior of modern CPUs is mostly hidden from developers and users of computer software. Due to a plethora of attacks exploiting microarchitectural behavior, developers of security-critical software must, e.g., ensure their code is constant-time, which is cumbersome and usually results in slower programs. In practice, small leakages which are deemed not exploitable still remain in the codebase. For example, sub-cache-line leakages have previously been investigated in the CacheBleed and MemJam attacks, which are deemed impractical on modern platforms.In this work, we revisit and carefully analyze the 4k-aliasing effect and discover that the measurable delay introduced by this microarchitectural effect is higher than found by previous work and described by Intel. By combining the rediscovered effect with a high temporal resolution possible when single-stepping an SGX enclave, we construct a very precise, yet widely applicable attack with sub-cache-line leakage resolution. o demonstrate the significance of our findings, we apply the new attack primitive to break a hardened AES T-Table implementation that features constant cache line access patterns. The attack is up to three orders of magnitude more efficient than previous sub-cache-line attacks on AES in SGX. Furthermore, we improve upon the recent work of Sieck et al. which showed partial exploitability of very faint leakages in a utility function loading base64-encoded RSA keys. With reliable sub-cache-line resolution, we build an end-to-end attack exploiting the faint leakage that can recover 4096-bit keys in minutes on a laptop. Finally, we extend the key recovery algorithm to also work for RSA keys following the standard that uses Carmichael’s totient function, while previous attacks were restricted to RSA keys using Euler’s totient function.
High-assurance zeroization

In this paper we revisit the problem of erasing sensitive data from memory and registers during return from a cryptographic routine. While the problem and related attacker model is fairly easy to phrase, it turns out to be surprisingly hard to guarantee security in this model when implementing cryptography in common languages such as C/C++ or Rust. We revisit the issues surrounding zeroization and then present a principled solution in the sense that it guarantees that sensitive data is erased and it clearly defines when this happens. We implement our solution as extension to the formally verified Jasmin compiler and extend the correctness proof of the compiler to cover zeroization. We show that the approach seamlessly integrates with state-of-the-art protections against microarchitectural attacks by integrating zeroization into Libjade, a cryptographic library written in Jasmin with systematic protections against timing and Spectre-v1 attacks. We present benchmarks showing that in many cases the overhead of zeroization is barely measurable and that it stays below 2% except for highly optimized symmetric crypto routines on short inputs.
Improved 3D image segmentation for X-ray tomographic data of biaxial warp-knitted composites

Identifying different features and phases within the biaxial warp-knitted composites enables accurate characterization of the internal changes of the composites under complex loading conditions and different processing parameters. However, obtaining 3D reconstructions that can be accurately segmented and quantitatively analyzed is still a challenge, primarily due to the low attenuation of materials, especially the low contrast between the weft yarn and warp yarns. Here, we use deep learning approaches to identify the boundary of the section of the tow on the 2D image, extract individual warp or weft tows automatically, and this is followed by computational modeling of composite materials. The results show that the performance of ResUNet++ model is better than that of other models for the biaxial warp-knitted composite specimen segmentation tasks. The special improved dataset augmentation algorithm is also a positive effective way to enhance network performance.
The Robustness of Machine Learning Models Using MLSecOps: A Case Study On Delivery Service Forecasting

Forecasting delivery services is a key aspect of modern delivery service operations that significantly contributes to the optimization of operations and the enhancement of customer satisfaction. Machine learning can assist in predicting the delivery time. One method to enhance security in machine learning is the implementation of MLSecOps. MLSecOps, or Machine Learning Security Operations, streamlines the process of deploying, monitoring, and maintaining machine learning models to ensure consistent and reliable performance in production environments. Cybersecurity was also integrated to enhance the security, robustness, and resilience of these models. This study applies MLSecOps to forecast delivery services to enhance the robustness of machine learning models. The MLSecOps tool utilized is the Adversarial Robustness Toolbox (ART). The results of testing the machine learning model on Forecasting Delivery Services show robustness to attacks such as boundary and backdoor attacks.
Evaluation of Derivative Free Kalman Filter for Non-linear State-Parameter Estimation and Fusion

The estimation of the states-parameters of non-linear system is often carried out using Extended Kalman Filter (EKF). The EKF is only reliable for systems that are almost linear on the time scale of the updates (i.e. sampling interval). The limitation of EKF can be overcome by use of another class of recursive estimator named derivative free Kalman filter (DFKF) or more popularly known as Unscented Kalman filter, a method that propagates mean and covariance using non-linear transformation. In this paper two methods: i) factorized version of EKF (UD Extended Kalman Filter or UDEKF) and ii) DFKF are studied and evaluated using various sets of simulated data of the non-linear systems as well as one real data set. Sensitivity study of DFKF with respect to tuning parameters such as α, β, and κ (used in creation of sigma points and their associated weights) is also carried out using one set of simulated data. DFKF as compared to EKF is more accurate, easier to implement and has same order of calculations. The concept of DFKF is extended to data fusion (DF) for similar sensors and algorithm is named DF-DFKF. Application of DFKF is demonstrated in parameter estimation problem.
FedPIA: Parameter Importance-Based Optimized Federated Learning to Efficiently Process Non-IID Data on Consumer Electronic Devices

Federated learning is a distributed machine learning method for learning consumer data generated by consumer electronic devices. It provides personalized intelligent services for users while maintaining data privacy by sharing models instead of user data. It is challenging for these methods to learn data efficiently and with high quality. The performance and quality of the model in federated learning are affected by differences in data from different devices, which can lead to divergence between local and global models. Therefore, this paper proposes a parameter importance-based optimization federated learning method, FedPIA. First, two key indicators, including the rate of parameter change and the acceleration rate of parameter change, are provided to evaluate the importance of model parameters, which can reflect the divergence of the data from different devices. Then, a regular term is created based on these indicators to construct a new loss function, reducing the divergence between local and global models. Last, this paper efficiently trains a high-quality model on consumer data with this optimization approach. Experiments show that FedPIA is significantly better than FedCL, FedAvg, and Scaffold in accuracy, F1 scores and communication overhead.
Decentralized Machine Learning Approach on ICU Admission Prediction for Enhanced Patient Care Using COVID-19 Data

The Intensive Care Unit (ICU) represents a constrained healthcare resource, involving invasive procedures and high costs, with significant psychological effects on patients and their families. The traditional approach to ICU admissions relies on observable behavioral indicators like breathing patterns and consciousness levels, which may lead to delayed critical care due to deteriorating conditions. Therefore, in the ever-evolving healthcare landscape, predicting whether patients will require admission to the ICU plays a pivotal role in optimizing resource allocation, improving patient outcomes, and reducing healthcare costs. Essentially, in the context of the post-COVID-19 pandemic, aside from many other diseases, this prediction not only forecasts the likelihood of ICU admission but also identifies patients at an earlier stage, allowing for timely interventions that can potentially mitigate the need for ICU care, thereby improving overall patient outcomes and healthcare resource utilization. However, this task usually requires a lot of diverse data from different healthcare institutions for a good predictive model, leading to concerns regarding sensitive data privacy. This paper aims to build a decentralized model using deep learning techniques while maintaining data privacy among different institutions to address these challenges.
Energy crop yield simulation and prediction system based on machinelearning algorithm

The yield of energy crops has been widely questioned by the public, but few researchers have analyzed the yield prediction of these crops, which has greatly limited their distribution and use. Based on this, in this study, a machine learning algorithm was used to design an energy crop yield simulation and prediction system, and this system was used to analyze the yields of four common energy crops. First the factors that affect the yield of energy crops are discussed, and then, the analysis of the machine learning algorithm and its application in the yield prediction of energy crops is presented, followed by an introduction to the framework of the energy crop yield simulation and prediction system. At the end of this paper, the effect of the energy crop yield simulation and prediction system was analyzed, and the feasibility conclusion was finally reached. The energy crop yield simulation and prediction system designed in this paper can greatly improve the accuracy of yield prediction when forecasting and analyzing energy crop yields. The sugarcane yield measured by the back propagation (BP) neural network model was 8694 kg/hm2, with a relative deviation of 0.86% from the actual. The BP neural network algorithm is widely used in energy crop yield prediction, and can greatly improve the accuracy. In the future, with the maturity and development of a BP neural network algorithm, it would promote the development of crop yield prediction.
Scientometric methods in the work of sci-tech libraries and science information centers

The Russian scientific community has been increasingly demanded to organize research and assess research output through various scientometric indicators. The scientometrics and bibliometrics have become an integral part of individual and corporate research endeavors. The authors point out priorities of bibliometrical activities, systems, methods and problems to be solved. The role and place of scientific and academic libraries and information centers in the lifecycle of scientific research are discussed. The authors emphasize that using scientometric instruments efficiently, top class professionals have to master relevant knowledge and competences while the indicators have to be applied delicately. The publication is prepared within the Framework of the Government Order to RNPLS&amp;T for 2023, project No. 720000F.99.1.BN60AA03000, theme No. 1021062311369-1-1.2.1;5.8.2;5.8.3.
The green libraries: Reviewing approaches and concepts

Ecological education has been a vector of library activities in some form or other. Recognizing and emphasizing the significance of ecological education, many libraries position themselves as the «green» libraries. The green libraries have been now thriving all round the world; however, Russia lacks the relevant criteria for green library characteristics to assess the institutional activities and to confirm the status of green libraries or to define vectors of sustainable development. The brief review of world experience in green library standardization demonstrates the trends in the library green movement in many countries and reveals the priorities for introducing similar standards in the national reality.
Personalization as a means of upgrading library and information user services in the digital environment of scientific and technical libraries

The paper describes the initial output of the author’s research project on personalization as a means of upgrading library and information service for patrons of scientific and technical libraries in the digital environment. The aim of the research is: to provide a rationale and validate the methods for upgrading the library and information service by means of personalization; to develop and test the scientifically proven theoretical model of introducing personalized library and information service for patrons of scientific and technical libraries in the digital environment; to develop methodological tools for building competence in library staff that would allow them to deal with personalized library and information service in the digital environment.
Subject-specific search in the library e-catalogs

The authors discuss the provision of subject-specific search in library e-catalogs (EC). For the purposes of analysis of information retrieval languages, the ECs of Russian libraries with the collections exceeding 4 m entries were examined. The representative list comprises libraries of various types and levels. It was found that each library used its own set of information retrieval languages for providing subject-specific search, with the most popular subject headings language, keywords language, Library Bibliographic Classification [RF LBC or BBK], Universal Decimal Classification (UDC), State Rubricator of Sci-tech Information (SRSTI, or GRNTI) and Supreme Attestation Commission Rubricator (VAK Rubricator). Beside the above-mentioned information retrieval languages, the libraries use their proprietary information retrieval languages, e.g. Rubricator of the Institute of Scientific Information for Social Sciences of the Russian Academy of Sciences, classification indexes of the Russian State Historical Library, or classification indexes of MSU Scientific Library. The authors conclude that the e-catalogs of the libraries under study enable subject-specific search with verbal languages (all the considered libraries) and classification languages (the majority of the considered libraries). The common features of subject-specific information retrieval in the library e-catalogs are formulated. The recommendations on how to develop provision of subject-specific retrieval when upgrading e-catalogs are suggested. The publication is prepared within the Framework of the Government Order to RNPLS&amp;T for 2023, project No. 720000F.99.1.BN60AA03000, theme No. 1021062311369-1-1.2.1;5.8.2;5.8.3.
Creativity competition as a project for building professional competences

The authors discuss the project activities of the students within and beyond learning process. The large-scale creative individual projects are presented for the college-level competitions as well as for the city or All-Russian competitions and contests.