Reviewerly/exHarmony huggingface dataset:
Applying REAP (Read, Encode, Annotate, Ponder) Learning Method to Improve the Students’ Reading Literacy

This research is a Classroom Action Research (CAR) conducted at SDN 104 Wiwitan which aims to improve students' reading literacy of class V in Indonesian learning through the REAP learning method. The sample of this research is 30 students in academic year 2021/2022. The instruments used to get the data are observation and test. Data analysis used quantitative and qualitative descriptive statistics. The result of this research showed that the average value of students in the first cycle was 51% with a classical learning percentage of 13%, and in the second cycle the average value obtained by students was 85% with a percentage of 93%. Judging from these data, it can be concluded that there is an enhancement in students' reading literacy in Indonesian learning after they being taught using REAP (Read, Encode, Annotae, Ponder) method, where from cycle I to cycle II it increased by 34%.
Bang-Bang Boosting of RRTs

This paper presents methods for dramatically improving the performance of sampling-based kinodynamic planners. The key component is the first-known complete, exact steering method that produces a time-optimal trajectory between any states for a vector of synchronized double integrators. This method is applied in three ways: 1) to generate RRT edges that quickly solve the two-point boundary-value problems, 2) to produce a (quasi)metric for more accurate Voronoi bias in RRTs, and 3) to iteratively time-optimize a given collision-free trajectory. Experiments are performed for state spaces with up to 2000 dimensions, resulting in improved computed trajectories and orders of magnitude computation time improvements over using ordinary metrics and constant controls.
Semantics-based Privacy by Design for Internet of Things Applications

As Internet of Things (IoT) technologies become more widespread in everyday life, privacy issues are becoming more prominent. The aim of this research is to develop a personal assistant that can answer software engineers' questions about Privacy by Design (PbD) practices during the design phase of IoT system development. Semantic web technologies are used to model the knowledge underlying PbD measurements, their intersections with privacy patterns, IoT system requirements and the privacy patterns that should be applied across IoT systems. This is achieved through the development of the PARROT ontology, developed through a set of representative IoT use cases relevant for software developers. This was supported by gathering Competency Questions (CQs) through a series of workshops, resulting in 81 curated CQs. These CQs were then recorded as SPARQL queries, and the developed ontology was evaluated using the Common Pitfalls model with the help of the Protégé HermiT Reasoner and the Ontology Pitfall Scanner (OOPS!), as well as evaluation by external experts. The ontology was assessed within a user study that identified that the PARROT ontology can answer up to 58\% of privacy-related questions from software engineers.
Sampling Streaming Data with Parallel Vector Quantization -- PVQ

Accumulation of corporate data in the cloud has attracted more enterprise applications to the cloud creating data gravity. As a consequence, network traffic has become more cloud centric. This increase in cloud centric traffic poses new challenges in designing learning systems for streaming data due to class imbalance. The number of classes plays a vital role in the accuracy of the classifiers built from the data streams. In this paper, we present a vector quantization-based sampling method, which substantially reduces the class imbalance in data streams. We demonstrate its effectiveness by conducting experiments on network traffic and anomaly dataset with commonly used ML model building methods; Multilayered Perceptron on TensorFlow backend, Support Vector Machines, K-Nearest Neighbour, and Random Forests. We built models using parallel processing, batch processing, and randomly selecting samples. We show that the accuracy of classification models improves when the data streams are pre-processed with our method. We used out of the box hyper-parameters of these classifiers and auto sklearn for hyperparameter optimization.
New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.
Topological Complexities of Finite Digital Images

Digital topological methods are often used on computing the topological complexity of digital images. We give new results on the relation between reducibility and digital contractibility in order to determine the topological complexity of a digitally connected finite digital image. We present all possible cases of the topological complexity TC of a finite digital image in Z and Z^2$. Finally, we determine the higher topological complexity TC_{n} of finite irreducible digital images independently of the number of points for n > 1.
SVEva Fair: A Framework for Evaluating Fairness in Speaker Verification

Despite the success of deep neural networks (DNNs) in enabling on-device voice assistants, increasing evidence of bias and discrimination in machine learning is raising the urgency of investigating the fairness of these systems. Speaker verification is a form of biometric identification that gives access to voice assistants. Due to a lack of fairness metrics and evaluation frameworks that are appropriate for testing the fairness of speaker verification components, little is known about how model performance varies across subgroups, and what factors influence performance variation. To tackle this emerging challenge, we design and develop SVEva Fair, an accessible, actionable and model-agnostic framework for evaluating the fairness of speaker verification components. The framework provides evaluation measures and visualisations to interrogate model performance across speaker subgroups and compare fairness between models. We demonstrate SVEva Fair in a case study with end-to-end DNNs trained on the VoxCeleb datasets to reveal potential bias in existing embedded speech recognition systems based on the demographic attributes of speakers. Our evaluation shows that publicly accessible benchmark models are not fair and consistently produce worse predictions for some nationalities, and for female speakers of most nationalities. To pave the way for fair and reliable embedded speaker verification, SVEva Fair has been implemented as an open-source python library and can be integrated into the embedded ML development pipeline to facilitate developers and researchers in troubleshooting unreliable speaker verification performance, and selecting high impact approaches for mitigating fairness challenges
EFISIENSI LUASAN KUBAH PANEL GALVALUM UNTUK MASJID DI PEDESAAN

The application of dome on mosque buildings is rationally based on the need for recognizable identity (Fithri, Athaillah, and Karsono, 2016). In modern time, mosques in Indonesia look to emphasize the character of domes by variation of curves, details, motives and colors. One of the most popular technologies now to allow the variations is Galvalum Panel, the dome arranged by modular panels made of metal containing zinc and aluminum. It is also stainless and light weight. The modular panels arranged on pipes hollow structures. The challenge for applying the technology is its unaffordable price, especially for mosque developers in villages. This research is aimed to formulate efficiency for applying the technology related to its dimension and budget, especially for people in village. The research method is started from interviewing some suppliers about dimension and unit price standards; then analyzing efficiency of main room area as basis structure for the dome; then calculating surface area of domes in many variations of diameter and height; and the last is multiplying each number of areas by unit price. The result is the most efficient diameter of dome applied in village areas is not more than 5 meters based on analyzed minimum capacity in the main room to accommodate minimum Friday worshipers. Meanwhile the efficient surface area is ranging from 9.48m2 to 57.84m2.
PENGEMBANGAN PERMAINAN MONOPOLI SEBAGAI MEDIA PEMBELAJARAN SEJARAH PADA SISWA KELAS X SMA NEGERI 3 MUARO JAMBI

This research and development was taken from the problems that occurred in SMA Negeri 3 Muaro Jambi where the learning was monotonous and less varied so that students felt bored quickly and their attention was quickly distracted by other things. Learning media can be an alternative in solving problems that occur in schools. This study aims to produce a learning media product, namely Monopoly Game which applies the elements of playing and learning into one in class X history lessons at SMA Negeri 3 Muaro Jambi. The research model used in this study is ADDIE developed by Lee and Owen which consists of Analysis, Design, Development, Implementation, and Evaluation. This research was conducted at SMA Negeri 3 Muaro Jambi with the research subjects of Class X students, totaling 4 people for individual trials, 14 people for small group trials, and 30 for large group trials. From the results of the field trials, the results of individual trials were obtained with an average score of 72% in the "good" category, Small Group trial results with an average score of 75% in the good category, Field Group trial results with an average score the percentage of 81 with the "very good" category for learning this monopoly game can be applied in history learning.
MOVE: Effective and Harmless Ownership Verification via Embedded External Features

Currently, deep neural networks (DNNs) are widely adopted in different applications. Despite its commercial values, training a well-performed DNN is resource-consuming. Accordingly, the well-trained model is valuable intellectual property for its owner. However, recent studies revealed the threats of model stealing, where the adversaries can obtain a function-similar copy of the victim model, even when they can only query the model. In this paper, we propose an effective and harmless model ownership verification (MOVE) to defend against different types of model stealing simultaneously, without introducing new security risks. In general, we conduct the ownership verification by verifying whether a suspicious model contains the knowledge of defender-specified external features. Specifically, we embed the external features by tempering a few training samples with style transfer. We then train a meta-classifier to determine whether a model is stolen from the victim. This approach is inspired by the understanding that the stolen models should contain the knowledge of features learned by the victim model. In particular, we develop our MOVE method under both white-box and black-box settings to provide comprehensive model protection. Extensive experiments on benchmark datasets verify the effectiveness of our method and its resistance to potential adaptive attacks. The codes for reproducing the main experiments of our method are available at \url{https://github.com/THUYimingLi/MOVE}.
Understanding complex predictive models with Ghost Variables

We propose a procedure for assigning a relevance measure to each explanatory variable in a complex predictive model. We assume that we have a training set to fit the model and a test set to check the out of sample performance. First, the individual relevance of each variable is computed by comparing the predictions in the test set, given by the model that includes all the variables with those of another model in which the variable of interest is substituted by its ghost variable, defined as the prediction of this variable by using the rest of explanatory variables. Second, we check the joint effects among the variables by using the eigenvalues of a relevance matrix that is the covariance matrix of the vectors of individual effects. It is shown that in simple models, as linear or additive models, the proposed measures are related to standard measures of significance of the variables and in neural networks models (and in other algorithmic prediction models) the procedure provides information about the joint and individual effects of the variables that is not usually available by other methods. The procedure is illustrated with simulated examples and the analysis of a large real data set.
Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language

We present a new pre-trained language model (PLM) for Rabbinic Hebrew, termed Berel (BERT Embeddings for Rabbinic-Encoded Language). Whilst other PLMs exist for processing Hebrew texts (e.g., HeBERT, AlephBert), they are all trained on modern Hebrew texts, which diverges substantially from Rabbinic Hebrew in terms of its lexicographical, morphological, syntactic and orthographic norms. We demonstrate the superiority of Berel on Rabbinic texts via a challenge set of Hebrew homographs. We release the new model and homograph challenge set for unrestricted use.
GRIP: A Graph Neural Network Accelerator Architecture

We present GRIP, a graph neural network accelerator architecture designed for low-latency inference. AcceleratingGNNs is challenging because they combine two distinct types of computation: arithmetic-intensive vertex-centric operations and memory-intensive edge-centric operations. GRIP splits GNN inference into a fixed set of edge- and vertex-centric execution phases that can be implemented in hardware. We then specialize each unit for the unique computational structure found in each phase.For vertex-centric phases, GRIP uses a high performance matrix multiply engine coupled with a dedicated memory subsystem for weights to improve reuse. For edge-centric phases, GRIP use multiple parallel prefetch and reduction engines to alleviate the irregularity in memory accesses. Finally, GRIP supports severalGNN optimizations, including a novel optimization called vertex-tiling which increases the reuse of weight data.We evaluate GRIP by performing synthesis and place and route for a 28nm implementation capable of executing inference for several widely-used GNN models (GCN, GraphSAGE, G-GCN, and GIN). Across several benchmark graphs, it reduces 99th percentile latency by a geometric mean of 17x and 23x compared to a CPU and GPU baseline, respectively, while drawing only 5W.
On the Limitations of Computational Fuzzy Extractors

We present a negative result of fuzzy extractors with computational security. Specifically, we show that, under a computational condition, a computational fuzzy extractor implies the existence of an information-theoretic fuzzy extractor with slightly weaker parameters. Our result implies that to circumvent the limitations of information-theoretic fuzzy extractors, we need to employ computational fuzzy extractors that are not invertible by non-lossy functions.
Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks

&lt;p&gt;Image analysis tools for cancer, such as automatic nuclei segmentation, are impacted by the inherent variation contained in pathology image data. Convolutional neural networks (CNN), demonstrate success in generalizing to variable data, illustrating great potential as a solution to the problem of data variability. In some CNN-based segmentation works for digital pathology, authors apply color normalization (CN) to reduce color variability of data as a preprocessing step prior to prediction, while others do not. Both approaches achieve reasonable performance and yet, the reasoning for utilizing this step has not been justified. It is therefore important to evaluate the necessity and impact of CN for deep learning frameworks, and its effect on downstream processes. In this paper, we evaluate the effect of popular CN methods on CNN-based nuclei segmentation frameworks. &lt;/p&gt;
IHC Color Histograms for Unsupervised Ki67 Proliferation Index Calculation

&lt;p&gt;Automated image analysis tools for Ki67 breast cancer digital pathology images would have signiﬁcant value if integrated into diagnostic pathology workﬂows. Such tools would reduce the workload of pathologists, while improving efﬁciency, and accuracy. Developing tools that are robust and reliable to multicentre data is challenging, however, differences in staining protocols, digitization equipment, staining compounds, and slide preparation can create variabilities in image quality and color across digital pathology datasets. In this work, a novel unsupervised color separation framework based on the IHC color histogram (IHCCH) is proposed for the robust analysis of Ki67 and hematoxylin stained images in multicentre datasets. An “overstaining” threshold is implemented to adjustforbackgroundoverstaining,andanautomatednucleiradiusestimatorisdesigned to improve nuclei detection. Proliferation index and F1 scores were compared between the proposed method and manually labeled ground truth data for 30 TMA cores that have ground truths for Ki67+ and Ki67− nuclei. The method accurately quantiﬁed the PI over the dataset, with an average proliferation index difference of 3.25%. To ensure the method generalizes to new, diverse datasets, 50 Ki67 TMAs from the Protein Atlas were used to test the validated approach. As the ground truth for this dataset is PI ranges, the automated result was compared to the PI range. The proposed method correctly classiﬁed 74 out of 80 TMA images, resulting in a 92.5% accuracy. In addition to these validations experiments, performance was compared to two color-deconvolution based methods, and to six machine learning classiﬁers. In all cases, the proposed work maintained more consistent (reproducible) results, and higher PI quantiﬁcation accuracy.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;
Consensus on Demand

Digital money can be implemented efficiently by avoiding consensus. However, no-consensus implementations have drawbacks, as they cannot support smart contracts, and (even more fundamentally) they cannot deal with conflicting transactions. We present a novel protocol that combines the benefits of an asynchronous, broadcast-based digital currency, with the capacity to perform consensus. This is achieved by selectively performing consensus a posteriori, i.e., only when absolutely necessary. Our on-demand consensus comes at the price of restricting the Byzantine participants to be less than a one-fifth minority in the system, which is the optimal threshold. We formally prove the correctness of our system and present an open-source implementation, which inherits many features from the Ethereum ecosystem.
Internship as a practice of developing professional competencies of students

The article is devoted to developing professional competencies among students studying 07.03.02 "Reconstruction and restoration of architectural heritage" during internships based at restoration work-shops. Presently, the problems of reconstruction and restoration of stone and wooden architectural constructions are highly relevant, and specialists in this area are in demand in the labour market. Ir-kutsk is a historical city where monuments of architecture have survived. However, the historic build-ings typical of the capital of Eastern Siberia are on the verge of extinction; therefore, it is necessary to preserve cultural heritage objects. The article discusses the concepts of competence and "skills ap-proach", their role in training and implementing this approach. It is noted that the RF state educational standards are developed based on introducing a "skills approach" in assessing the quality of education. The article considers the internship for obtaining professional skills and professional experience that contribute to the professional competencies of future graduates of the "Reconstruction and restoration of architectural heritage" department. We analysed the stages of the internship and restoration project and described the methodology and application of restoration standards for preserving a cultural herit-age object. The cultural and historical context of historical and bibliographic research was revealed; restoration methods necessary for reconstructing the original appearance of an architectural monu-ment are considered. Particular attention is paid to the professional experience obtained as a result of the internship.
Towards the simulation of transition-metal oxides of the cathode battery materials using VQE methods

Variational quantum eigensolver (VQE) is a hybrid quantum-classical technique that leverages noisy intermediate scale quantum (NISQ) hardware to obtain the minimum eigenvalue of a model Hamiltonian. VQE has so far been used to simulate condensed matter systems as well as quantum chemistry of small molecules. In this work, we employ VQE methods to obtain the ground-state energy of LiCoO$_2$, a candidate transition metal oxide used for battery cathodes. We simulate Li$_2$Co$_2$O$_4$ and Co$_2$O$_4$ gas-phase models, which represent the lithiated and delithiated states during the discharge and the charge of the Li-ion battery, respectively. Computations are performed using a statevector simulator with a single reference state for three different trial wavefunctions: unitary coupled-cluster singles and doubles (UCCSD), unitary coupled-cluster generalized singles and doubles (UCCGSD) and k-unitary pair coupled-cluster generalized singles and doubles (k-UpCCGSD). The resources in terms of circuit depth, two-qubit entangling gates and wavefunction parameters are analyzed. We find that the k-UpCCGSD with k=5 produces results similar to UCCSD but at a lower cost. Finally, the performance of VQE methods is benchmarked against the classical wavefunction-based methods, such as coupled-cluster singles and doubles (CCSD) and complete active space configuration interaction (CASCI). Our results show that VQE methods quantitatively agree with the results obtained from CCSD. However, the comparison against the CASCI results clearly suggests that advanced trial wavefunctions are likely necessary to capture the multi-reference characteristics as well as the correlations emerging from high-level electronic excitations.
Cache-Efficient Memory Representation of Markov Decision Processes

Research in automated planning typically focuses on the development of new or improved algorithms.Yet, an equally important but often overlooked topic is that of how to actually implement these algorithms eciently.In this study, we are making an attempt to close this gap in the context of optimal Markov Decision Process (MDP) planning.Precisely, we present a novel cache-ecient memory representation of MDPs, which we call CSR-MDP, that takes advantage of low-level hardware features such as memory hierarchy.We evaluate the speed improvement provided by our memory representation by comparing the performance of CSR-MDP with the performance obtained by traditional MDP representation.We show that by using our CSR-MDP memory representation, existing MDP solvers, including VI, LRTDP and TVI, are able to nd an optimal policy an order of magnitude faster.