Reviewerly/exHarmony huggingface dataset:
Pengembangan LKPD Berbasis Inkuiri Terbimbing pada Mata Pelajaran IPA Pokok Bahasan Klasifikasi Mahluk Hidup (Development of Guided Inquiry-based Student Worksheet on Science Subject in the Chapter of Classification of Living Things)

One effort that Teachers can do in order to encourage student engagement in class is by selecting media which enable students to practice their skills on doing experiments, critical thinking skills so that they could develop science concept. The purpose of this research is to produce Guided Inquiry-based Student Worksheet on Science Subject in the Chapter of Classification of Living Things, students and teacher perception on the worksheet are also sought. The developmental research uses ADDIE model. Worksheet as the product was put on trial prior to the validation processes. Media and material validation of the worksheet resulted in the percentage of 98.75% and 90.00% respectively, indicating both are categorised as “Very Good”. As for the small and large group testing, the percentage of 93.05% and 86.25% are obtained respectively, indicating both are categorised as “Very Good”. The percentage of 97.72 is obtained from the teachers’ perception, indicating it is categorised as “Very Good”. Based on the results of the study, a conclusion could be made that the Guided Inquiry-based Student Worksheet on Science Subject in the Chapter of Classification of Living Things is appropriate to be used in learning. Keywords: Student worksheet, Guided Inquiry, Classification of living things
Particle-Gibbs Sampling for Bayesian Feature Allocation Models

Bayesian feature allocation models are a popular tool for modelling data with a combinatorial latent structure. Exact inference in these models is generally intractable and so practitioners typically apply Markov Chain Monte Carlo (MCMC) methods for posterior inference. The most widely used MCMC strategies rely on an element wise Gibbs update of the feature allocation matrix. These element wise updates can be inefficient as features are typically strongly correlated. To overcome this problem we have developed a Gibbs sampler that can update an entire row of the feature allocation matrix in a single move. However, this sampler is impractical for models with a large number of features as the computational complexity scales exponentially in the number of features. We develop a Particle Gibbs sampler that targets the same distribution as the row wise Gibbs updates, but has computational complexity that only grows linearly in the number of features. We compare the performance of our proposed methods to the standard Gibbs sampler using synthetic data from a range of feature allocation models. Our results suggest that row wise updates using the PG methodology can significantly improve the performance of samplers for feature allocation models.
Prediction Models with Machine Learning Against Student Success in Online Learning

The learning system during the Covid-19 pandemic was carried out online, online learning had a negative impact and a positive impact. The impact given can affect the success of student learning. The success of learning is the main thing that must be achieved by students. From the success of learning, it can be seen that the online learning process is going well or not. To determine the success rate of online learning, testing is carried out by applying a neural network algorithm. Neural network algorithms are used because they can solve complex problems related to prediction. This research is expected to help lecturers or campus parties to create better online learning. In this study using student grade data for Academic Year 2018/2019 and Academic Year 2019/2020, data testing using Rapidminer software and operator cross validation. In testing the Academic Year 2018/2019 and Academic Year 2019/2020 value data using 700 training cycles, 0.4 momentum, 0.2 learning rate and hidden layer 2. The level of accuracy obtained in the 2018/2019 student grade data is 95, 55% and Academic Year 2019/2020 which is 93.17%. From the test results, it was found that the accuracy rate of Academic Year 2018/2019 is higher than Academic Year 2019/2020, so the success rate in Academic Year 2018/2019 before the pandemic is better than the success rate in Academic Year 2019/2020 after the pandemic.
Mining the Weights Knowledge for Optimizing Neural Network Structures

Knowledge embedded in the weights of the artificial neural network can be used to improve the network structure, such as in network compression. However, the knowledge is set up by hand, which may not be very accurate, and relevant information may be overlooked. Inspired by how learning works in the mammalian brain, we mine the knowledge contained in the weights of the neural network toward automatic architecture learning in this paper. We introduce a switcher neural network (SNN) that uses as inputs the weights of a task-specific neural network (called TNN for short). By mining the knowledge contained in the weights, the SNN outputs scaling factors for turning off and weighting neurons in the TNN. To optimize the structure and the parameters of TNN simultaneously, the SNN and TNN are learned alternately under the same performance evaluation of TNN using stochastic gradient descent. We test our method on widely used datasets and popular networks in classification applications. In terms of accuracy, we outperform baseline networks and other structure learning methods stably and significantly. At the same time, we compress the baseline networks without introducing any sparse induction mechanism, and our method, in particular, leads to a lower compression rate when dealing with simpler baselines or more difficult tasks. These results demonstrate that our method can produce a more reasonable structure.
On Some Cryptographic Protocol

In this paper we present a cryptographic protocol for a seller - buyer problem and in particular we prove that non-emptiness of a semi-commutative set defined by mappings involved in the protocol is a decidable problem.
Online Continual Learning in Image Classification: An Empirical Survey

Online continual learning for image classification studies the problem of learning to classify images from an online stream of data and tasks, where tasks may include new classes (class incremental) or data nonstationarity (domain incremental). One of the key challenges of continual learning is to avoid catastrophic forgetting (CF), i.e., forgetting old tasks in the presence of more recent tasks. Over the past few years, many methods and tricks have been introduced to address this problem, but many have not been fairly and systematically compared under a variety of realistic and practical settings. To better understand the relative advantages of various approaches and the settings where they work best, this survey aims to (1) compare state-of-the-art methods such as MIR, iCARL, and GDumb and determine which works best at different experimental settings; (2) determine if the best class incremental methods are also competitive in domain incremental setting; (3) evaluate the performance of 7 simple but effective trick such as "review" trick and nearest class mean (NCM) classifier to assess their relative impact. Regarding (1), we observe iCaRL remains competitive when the memory buffer is small; GDumb outperforms many recently proposed methods in medium-size datasets and MIR performs the best in larger-scale datasets. For (2), we note that GDumb performs quite poorly while MIR -- already competitive for (1) -- is also strongly competitive in this very different but important setting. Overall, this allows us to conclude that MIR is overall a strong and versatile method across a wide variety of settings. For (3), we find that all 7 tricks are beneficial, and when augmented with the "review" trick and NCM classifier, MIR produces performance levels that bring online continual learning much closer to its ultimate goal of matching offline training.
UCX Programming Interface for Remote Function Injection and Invocation

Network library APIs have historically been developed with the emphasis on data movement, placement, and communication semantics. Many communication semantics are available across a large variety of network libraries, such as send-receive, data streaming, put/get/atomic, RPC, active messages, collective communication, etc. In this work we introduce new compute and data movement APIs that overcome the constraints of the single-program, multiple-data (SPMD) programming model by allowing users to send binary executable code between processing elements. Our proof-of-concept implementation of the API is based on the UCX communication framework and leverages the RDMA network for fast compute migration. We envision the API being used to dispatch user functions from a host CPU to a SmartNIC (DPU), computational storage drive (CSD), or remote servers. In addition, the API can be used by large-scale irregular applications (such as semantic graph analysis), composed of many coordinating tasks operating on a data set so big that it has to be stored on many physical devices. In such cases, it may be more efficient to dynamically choose where code runs as the applications progresses.
The leftmost column of ordered Chinese restaurant process up-down chains: intertwining and convergence

Recently there has been significant interest in constructing ordered analogues of Petrov’s two-parameter extension of Ethier and Kurtz’s infinitely-many-neutral-alleles diffusion model. One method for constructing these processes goes through taking an appropriate diffusive limit of Markov chains on integer compositions called ordered Chinese Restaurant Process up-down chains. The resulting processes are diffusions whose state space is the set of open subsets of the open unit interval. In this paper we begin to study nontrivial aspects of the order structure of these diffusions. In particular, for a certain choice of parameters, we take the diffusive limit of the size of the first component of ordered Chinese Restaurant Process up-down chains and describe the generator of the limiting process. We then relate this to the size of the leftmost maximal open subset of the open-set valued diffusions. This is challenging because the function taking an open set to the size of its leftmost maximal open subset is discontinuous. Our methods are based on establishing intertwining relations between the processes we study.
Meta Navigator: Search for a Good Adaptation Policy for Few-shot Learning

Few-shot learning aims to adapt knowledge learned from previous tasks to novel tasks with only a limited amount of labeled data. Research literature on few-shot learning exhibits great diversity, while different algorithms often excel at different few-shot learning scenarios. It is therefore tricky to decide which learning strategies to use under different task conditions. Inspired by the recent success in Automated Machine Learning literature (AutoML), in this paper, we present Meta Navigator, a framework that attempts to solve the aforementioned limitation in few-shot learning by seeking a higher-level strategy and proffer to automate the selection from various few-shot learning designs. The goal of our work is to search for good parameter adaptation policies that are applied to different stages in the network for few-shot classification. We present a search space that covers many popular few-shot learning algorithms in the literature and develop a differentiable searching and decoding algorithm based on meta-learning that supports gradient-based optimization. We demonstrate the effectiveness of our searching-based method on multiple benchmark datasets. Extensive experiments show that our approach significantly outperforms baselines and demonstrates performance advantages over many state-of-the-art methods. Code and models will be made publicly available.
Extended Tree Search for Robot Task and Motion Planning

Integrated task and motion planning (TAMP) is desirable for generalized autonomy robots but it is challenging at the same time. TAMP requires the planner to not only search in both the large symbolic task space and the high-dimension motion space but also deal with the infeasible task actions due to its intrinsic hierarchical process. We propose a novel decision-making framework for TAMP by constructing an extended decision tree for both symbolic task planning and high-dimension motion variable binding. We integrate top-k planning for generating explicitly a skeleton space where a variety of candidate skeleton plans are at disposal. Moreover, we effectively combine this skeleton space with the resultant motion variable spaces into a single extended decision space. Accordingly, we use Monte-Carlo Tree Search (MCTS) to ensure an exploration-exploitation balance at each decision node and optimize globally to produce optimal solutions. The proposed seamless combination of symbolic top-k planning with streams, with the proved optimality of MCTS, leads to a powerful planning algorithm that can handle the combinatorial complexity of long-horizon manipulation tasks. We empirically evaluate our proposed algorithm in challenging robot tasks with different domains that require multi-stage decisions and show how our method can overcome the large task space and motion space through its effective tree search compared to its most competitive baseline method.
One Time Password Generation based on Random Permutation using User Identity with Timestamp

A password is a string of alphanumeric elements that is used mainly to authenticate user identity in order to give permission gaining access to the computer resources that should be secured from any unauthorized access. For this reason, password need to be kept secure among different types of attack. One way to increase the security of gaining access to any computer resources is the using of one-time password as a two-factor authentication which is generated for using it only one time. In this paper, we introduced a new method to generate a one-time password that depends on the user identity such as user account name or password and the timestamp. This information is gathered to make a string that will be used to generate a random permutation of a given size. The process of generating random permutation is a one-way hashing method. From the generated random permutation, the one-time password is constructed with a flexibility of having different size as needed.
The Patch: An Artful Syn(aes)thetic Mapping of Linguistic Data through Collaborative Digital / Analogue Literacy Processes

The Patch workshop explores creative / critical analyses that can map the collectively relevant topoi of semiosis in linguistic texts according to the three ecologies as articulated by Félix Guattari. As creative pedagogues both in service and critical of creative economics, we valourize a generative practice, one that results in successive creative readings, writings, visualizations, sonifications and audiovisual artifacts. The Patch is a human-computer procedural algorithm, engaging a series of recursive and recombinant processes that utilize several software programs, collaborative writing and performance practices to bridge analogue and digital literacies. A total of 80 teacher education students, graduate students and faculty, working with a single input text, provided the data reported in this paper.
Normalized Datasets of Harnack’s Reconstruction of Marcion’s &lt;i&gt;Gospel&lt;/i&gt;

These two datasets are the first born-digital, normalized, peer-reviewed datasets of Harnack’s classic reconstruction of Marcion’s <em>Gospel</em>. The first consists of human-readable postclassical Greek, the second of lemmatized and morphologically tagged text following the openly licensed BibleWorks Greek Morphology schema. The recent deluge of critical editions of Marcion’s <em>Gospel</em> makes Harnack’s public domain work even more relevant as scholars turn from theology- to text-based approaches to restore Marcion’s <em>Gospel</em> and account for its place in the editorial history of early canonical and non-canonical Gospels. These datasets resource Marcion’s <em>Gospel</em> becoming a major topic of interest in Computational Linguistics research.
Keefektifan Pembelajaran Menulis Cerita Pendek dengan Metode Pemodelan

This research aims to illustrate the effectiveness of short story writing learning with modeling methods in private high school students of The Langkat Bahorok Regional Government compared to lecture methods. The population in this study was all students of class VIII of the Private High School Langkat Bahorok With a sample number of 20 people for each treatment. A research method is an experiment that compares between two groups given different treatment, in which case the experimental group is given short story writing learning with modeling methods and the control group is given learning by lecture method. From the distribution results obtained: (1) The experimental group that got learning to write short stories with modeling methods had an average value (Mean) of 74.75 and standard deviation (SD) of 15.66; (2) The control group that learned to write short stories with lecture methods had an average score (mean) of 62.5 and Standard Deviation (SD) of 10.97. Furthermore, from the calculation of the hypothesis test obtained greatly t hitung = 2,37 at a significance level of 5% price t tabel = 2,02 and to a degree 1% price t tabel = 2,71. Because of the price t hitung Bigger than t tabel (2,02 &lt; 2,37 &lt; 2,71), then the nil hypothesis (Ho) can be rejected and the alternative hypothesis (Ha) is accepted, which means there is a significant influence between the learning outcomes of writing short stories with modeling methods and learning outcomes of writing short stories with lecture methods on students Private High School Langkat Bahorok.
On Coordinate Decoding for Keypoint Estimation Tasks.

A series of 2D (and 3D) keypoint estimation tasks are built upon heatmap coordinate representation, i.e. a probability map that allows for learnable and spatially aware encoding and decoding of keypoint coordinates on grids, even allowing for sub-pixel coordinate accuracy. In this report, we aim to reproduce the findings of DARK that investigated the 2D heatmap representation by highlighting the importance of the encoding of the ground truth heatmap and the decoding of the predicted heatmap to keypoint coordinates. The authors claim that a) a more principled distribution-aware coordinate decoding method overcomes the limitations of the standard techniques widely used in the literature, and b), that the reconstruction of heatmaps from ground-truth coordinates by generating accurate and continuous heatmap distributions lead to unbiased model training, contrary to the standard coordinate encoding process that quantizes the keypoint coordinates on the resolution of the input image grid.
Secure skyline query processing in mobile edge computing over location-based data

Aiming at the efficiency and privacy of queries under mobile edge computing, secure skyline query processing in mobile edge computing over location-based data is developed. A secure skyline query framework in the mobile edge computing scenario is first proposed and a security model is also defined. To improve the efficiency of secure queries in the edge server, we use the Paillier homomorphic encryption and order-revealing encryption to devise a novel and unified secure index structure. It can effectively protect the query unlinkability in accordance with the semi-blind structure proposed by us. Furthermore, based on the secure index, we propose a secure skyline query protocol in mobile edge computing to realize the edge-cloud collaborative skyline query considering the privacy leakage. Finally, we analyze the complexity and security of the proposed approach. Findings from the experimental evaluation show that our proposed approach significantly increases the efficiency under the semi-honest model, in comparison to the state-of-art.
Employing a View Field in Autonomous Robots and Optimization of Checkpoint Placement in Area Surveillance

In area surveillance problems, planning an effective route is important for both of cost and safety. One solution is to transform a surveillance problem to another route planning problem such as a traveling salesman problem or a vehicle routing problem. However, conventional problems are limited in their settings (e.g., route expression), which cause difference between the models and the real world situation. Another solution is to evaluate routes by modeling and simulating the problem environment, for approaching the real world. In this paper, we develop a new simulation environment for surveillance, then propose a route optimization algorithm in the environment.
An improved phase measuring deflectometry method for defect detection of specular reflection surface

For specular reflection surface detection, high reflectivity is a large challenge to effectively extract the depth information of surface. Phase Measuring Deflectometry (PMD) based three-dimensional shape measurement is proposed for solving this problem. In this study, PMD method based on the characteristic of high specular reflectivity is used to perform structured light imaging on the glass surface to obtain depth information on the surface of the glass panel. In this paper, we propose a new image reconstruction method suitable for imaging specular reflection surface defects. According to the characteristic of the glass panel, the proposed method has a phase pre-unwrapping process and improves the least square method of unfolding and folding the phase algorithm. The experimental results show that the proposed method is more robust for imaging and detection of high-reflective plane than the traditional least squares method.
Bridging Educational Equity Through ICT Learning: Lessons from Learning Behaviour and Challenges in Welcoming ICT of Children at a Rural Area

This study aims to explore challenges and learning behavior of children at Desa Balerejo, Kecamatan Pangungrejo, Blitar, in welcoming ICT introduction program. Education is the heart of community development. The creation of learning community and its’ development is sustained by community education, including its’ young generation education. Children are the future of the community. They will be the next leaders and their community developers. Thus, providing sufficient education is vital for assuring certain community existence and development sustainability. However, in reality, not all children can pursue equal education. Education in some remote areas is still left behind. Consequently, children in rural areas, are frequently suffering from educational disparities. The data are collected through direct observation during the community service program, through our roles as community service program providers as well as the instructors. The study is the result of our reflection, what happens in the field and what can be improved for future empowerment programs. The study indicates that there are several substantial factors determine and pose challenges for the effectiveness of ICT education for children at rural area. The first factor is culture and social context. Submissive and collectivism cultural orientation may affect the children’s behavior. The second aspect is the teaching approaches and learning psychology of children. Teaching method and materials should be adapted to the children, what they like and how they may learn best. The last factor is relating the nature of the empowerment program, which should be executed collaboratively and simultaneously with other programs.