Reviewerly/exHarmony huggingface dataset:
Workshop of Improving Teachers’ Competency in Teaching Speaking by Using Buzz Group at SMA Kristen Kalam Kudus Medan.

Speaking ability is an essential tool for communication in a foreign language. Speaking cannot be separated from language learning because it is a productive skill that measures the improvement in learning the language. Speaking a foreign language is effective oral communication that requires the ability to use the language appropriately in social interaction. Principles in teaching speaking are important for improving speaking in the classroom. In order to keep on the intentional communicative class, the teacher should consider those principles. The principles also help the teacher design the appropriate materials. Thus, the teacher should concern with the teaching of speaking comprehension to gain the goals of the teaching and learning process. Buzz groups are teams of four to six students that are formed quickly and extemporaneously to respond to course-related questions, each group can respond to more questions; all groups can discuss the same or different questions. Discussion is informal, and students do not need to arrive at a consensus, but simply exchange ideas. Keywords: Teachers’ Competency, Teaching Speaking, Buzz Group
Designing Data Warehouse For Forecast and Data Visualization of Sales Nutrition Products

Sales data can be processed in such a way that it can become information that is used as material for analysis and consideration in making decisions. This study aims to visualize PT XYZ sales data for nutritious intake products and predict sales figures for 2018 and 2019. Data is obtained directly from PT XYZ by submitting a request for data withdrawal. Data on sales of nutritious beverage products for the last 5 years are processed using Pentaho tools with ETL method (extract, transform, load) then predicted sales figures for 2019 using R programming language with ARIMA and Holt-Winters methods after which data will be visualized using Powe BI so that the display of data presentation is more interesting and informative. To find out the compatibility in using the forecasting method, the writer will compare RSME numbers from both methods and use the method with the smallest RSME number.
Online field work practice management information system

Regulation of the Indonesia Minister of Education and Culture Number 3 of 2020 concerning the Freedom to Learn on an Independent Campus Policy, is a framework for preparing students to become tough graduates, relevant to the needs of the times. The implementation of vocational education, namely the existence of a training / apprenticeship period in the industrial world has similarities with the policy. Basically, both educational institutions and the industrial world need each other's. Information about the existence of a period of field work practice, both in the industrial world and in educational institutions today is very limited. This system was designed and built to bridge the information gap. This study uses the Rapid Application Development (RAD) method, which is a sequential and systematic software development method that emphasizes the development cycle in a short time. The RAD method consists of : Business Modeling, namely the process of modeling the flow of information on business functions; Data Modeling, namely the process of transforming a business model into a series of data object; Process Modeling, namely the process of translating data models into information flows in the implementation of business functions; Application Generation, namely the process of creating software using a programming language; and Testing and Turnover, which is a testing process to eliminate errors and return to the beginning of the development process. This information system provides 3 interfaces for 3 groups of users, namely: training administrators, prospective trainee and officers from industry with their own facilities.
A Review on Classification of Images with Convolutional Neural Networks

Abstract: Deep learning has recently been applied to scene labelling, object tracking, pose estimation, text detection and recognition, visual saliency detection, and image categorization. Deep learning typically uses models like Auto Encoder, Sparse Coding, Restricted Boltzmann Machine, Deep Belief Networks, and Convolutional Neural Networks. Convolutional neural networks have exhibited good performance in picture categorization when compared to other types of models. A straightforward Convolutional neural network for image categorization was built in this paper. The image classification was finished by this straightforward Convolutional neural network. On the foundation of the Convolutional neural network, we also examined several learning rate setting techniques and different optimisation algorithms for determining the ideal parameters that have the greatest influence on image categorization
Managing public integrity threats in infrastructure projects

Integrity risks can arise at every stage of the infrastructure life cycle, resulting in inappropriate use of resources or improper behaviour. During crises, when rapid responses are needed and some safeguards lifted, these risks may increase and require adequate firewalls. OECD recommendations on the governance of infrastructure and on public integrity (OECD, 2020, 2017) highlight the adoption of a risk-based approach to identify, mitigate, and address integrity risks such as fraud, collusion, corruption, undue influence or other unethical practices at each stage of the infrastructure life cycle and develop tailored control mechanisms.
Quantum Goemans-Williamson Algorithm with the Hadamard Test and Approximate Amplitude Constraints

Semidefinite programs are optimization methods with a wide array of applications, such as approximating difficult combinatorial problems. One such semidefinite program is the Goemans-Williamson algorithm, a popular integer relaxation technique. We introduce a variational quantum algorithm for the Goemans-Williamson algorithm that uses only <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>+</mml:mo></mml:mrow><mml:mn>1</mml:mn></mml:math> qubits, a constant number of circuit preparations, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mtext>poly</mml:mtext><mml:mo stretchy="false">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> expectation values in order to approximately solve semidefinite programs with up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:msup></mml:math> variables and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>M</mml:mi><mml:mo>&amp;#x223C;</mml:mo><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> constraints. Efficient optimization is achieved by encoding the objective matrix as a properly parameterized unitary conditioned on an auxilary qubit, a technique known as the Hadamard Test. The Hadamard Test enables us to optimize the objective function by estimating only a single expectation value of the ancilla qubit, rather than separately estimating exponentially many expectation values. Similarly, we illustrate that the semidefinite programming constraints can be effectively enforced by implementing a second Hadamard Test, as well as imposing a polynomial number of Pauli string amplitude constraints. We demonstrate the effectiveness of our protocol by devising an efficient quantum implementation of the Goemans-Williamson algorithm for various NP-hard problems, including MaxCut. Our method exceeds the performance of analogous classical methods on a diverse subset of well-studied MaxCut problems from the GSet library.
Policy Implementation Performance in Developing Countries: The Study of Uganda among East African Countries

This policy paper examines the effectiveness of policy implementation in developing nations, with a focus on Uganda among the East African nations. The performance of Uganda's policy implementation in comparison to other East African nations is highlighted. The World Bank's "Country Policy and Institutional Assessment" (CPIA) for the year 2021 served as the foundation for this desk research. One-way analysis was used in the analysis. The results showed that the East African nations performed better than the global indicator. Kenya earned the highest mean policy implementation score among the three East African nations (3.7539683 on a scale of 1 = low to 6 = high). Kenya came in first, then Uganda (3.4484127), and Tanzania (3.5119048). However, the inconsequential F statistic showed that there is no difference in policy execution between the three states., i.e., the means are equal. There is a general consensus that Uganda has strong policies, but the execution is appalling. The country's policy implementation is better than the average index among developing countries, despite the claim that it might "hold some water." The three nations together with a few others make up the East African countries; while Uganda's performance is lower than Kenya's, it is higher than Tanzania's.
Prediction of Inefficiency in Health Insurance Administration Institutions in Indonesia using Light Gradient Boosting Machine

Everyone understands that health is something that must be considered and should not be ignored. Therefore, the government seeks to form institutions that can organize health social security for the community. However, in practice it is not often found that there are cases of waste in the services provided (inefficiency). So that research was carried out using the Light Gradient Boosting Machine (LightGBM) to provide solutions for solving cases of waste (inefficiency) in health social security administering institutions.
The Challenging Relationship between Contemporary Art and Intellectual Property

Nowadays, the contemporarily concept of intellectual property rights is a challenging problem, because it includes many ideas like innovation, invention, copyright, trademark and creativity and/or others of these kind. The legislation of intellectual property right is one of the most challenging ones not only in Albania but also in the most developed countries. A successful entrepreneur (Craig Venter) thinks that it is the key for the economic development and prosperity. The intellectual property plays a crucial role while being applied in the business, biotechnology and artificial intelligence. Its legislation is very recent and needs to be updated or modified so that countries should be able to anticipate any gaps of the legal framework that may be generated in future due to innovation and invention. Soon, Albania is going to join the European Union, so additional to the efforts to ratify the domestic legislation with the Acquis Communautaire, the government is facing with another huge challenge. According to international reports “Mapping the Real Routes of Trade in Fake Goods”, the country has become a main path for the international transit regarding counterfeit goods. The vigilance of domestic intuitions is underperforming that are incapable to stop this phenomenon.
The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach (Preprint)

<sec> <title>BACKGROUND</title> Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures. </sec> <sec> <title>OBJECTIVE</title> Various tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond. </sec> <sec> <title>METHODS</title> The FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime. </sec> <sec> <title>RESULTS</title> FeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites. </sec> <sec> <title>CONCLUSIONS</title> FeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond. </sec>
MARBLER: An Open Platform for Standardized Evaluation of Multi-Robot Reinforcement Learning Algorithms

Multi-Agent Reinforcement Learning (MARL) has enjoyed significant recent progress thanks, in part, to the integration of deep learning techniques for modeling interactions in complex environments. This is naturally starting to benefit multi-robot systems (MRS) in the form of multi-robot RL (MRRL). However, existing infrastructure to train and evaluate policies predominantly focus on the challenges of coordinating virtual agents, and ignore characteristics important to robotic systems. Few platforms support realistic robot dynamics, and fewer still can evaluate Sim2Real performance of learned behavior. To address these issues, we contribute MARBLER: Multi-Agent RL Benchmark and Learning Environment for the Robotarium. MARBLER offers a robust and comprehensive evaluation platform for MRRL by marrying Georgia Tech's Robotarium (which enables rapid deployment on physical MRS) and OpenAI's Gym interface (which facilitates standardized use of modern learning algorithms). MARBLER offers a highly controllable environment with realistic dynamics, including barrier certificate-based obstacle avoidance. It allows anyone across the world to train and deploy MRRL algorithms on a physical testbed with reproducibility. Further, we introduce five novel scenarios inspired by common challenges in MRS and provide support for new custom scenarios. Finally, we use MARBLER to evaluate popular MARL algorithms and provide insights into their suitability for MRRL. In summary, MARBLER can be a valuable tool to the MRS research community by facilitating comprehensive and standardized evaluation of learning algorithms on realistic simulations and physical hardware. Links to our open-source framework and videos of real-world experiments can be found at https://shubhlohiya.github.io/MARBLER/.
Carbon-Efficient Neural Architecture Search

This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process. The proposed framework, called carbon-efficient NAS (CE-NAS), consists of NAS evaluation algorithms with different energy requirements, a multi-objective optimizer, and a heuristic GPU allocation strategy. CE-NAS dynamically balances energy-efficient sampling and energy-consuming evaluation tasks based on current carbon emissions. Using a recent NAS benchmark dataset and two carbon traces, our trace-driven simulations demonstrate that CE-NAS achieves better carbon and search efficiency than the three baselines.
The LDBC Social Network Benchmark Interactive workload v2: A transactional graph query benchmark with deep delete operations

The LDBC Social Network Benchmark's Interactive workload captures an OLTP scenario operating on a correlated social network graph. It consists of complex graph queries executed concurrently with a stream of updates operation. Since its initial release in 2015, the Interactive workload has become the de facto industry standard for benchmarking transactional graph data management systems. As graph systems have matured and the community's understanding of graph processing features has evolved, we initiated the renewal of this benchmark. This paper describes the draft Interactive v2 workload with several new features: delete operations, a cheapest path-finding query, support for larger data sets, and a novel temporal parameter curation algorithm that ensures stable runtimes for path queries.
CREPE: Learnable Prompting With CLIP Improves Visual Relationship Prediction

In this paper, we explore the potential of Vision-Language Models (VLMs), specifically CLIP, in predicting visual object relationships, which involves interpreting visual features from images into language-based relations. Current state-of-the-art methods use complex graphical models that utilize language cues and visual features to address this challenge. We hypothesize that the strong language priors in CLIP embeddings can simplify these graphical models paving for a simpler approach. We adopt the UVTransE relation prediction framework, which learns the relation as a translational embedding with subject, object, and union box embeddings from a scene. We systematically explore the design of CLIP-based subject, object, and union-box representations within the UVTransE framework and propose CREPE (CLIP Representation Enhanced Predicate Estimation). CREPE utilizes text-based representations for all three bounding boxes and introduces a novel contrastive training strategy to automatically infer the text prompt for union-box. Our approach achieves state-of-the-art performance in predicate estimation, mR@5 27.79, and mR@20 31.95 on the Visual Genome benchmark, achieving a 15.3\% gain in performance over recent state-of-the-art at mR@20. This work demonstrates CLIP's effectiveness in object relation prediction and encourages further research on VLMs in this challenging domain.
Coded Distributed Image Classification

In this paper, we present a coded computation (CC) scheme for distributed computation of the inference phase of machine learning (ML) tasks, specifically, the task of image classification. Building upon Agrawal et al.~2022, the proposed scheme combines the strengths of deep learning and Lagrange interpolation technique to mitigate the effect of straggling workers, and recovers approximate results with reasonable accuracy using outputs from any $R$ out of $N$ workers, where $R\leq N$. Our proposed scheme guarantees a minimum recovery threshold $R$ for non-polynomial problems, which can be adjusted as a tunable parameter in the system. Moreover, unlike existing schemes, our scheme maintains flexibility with respect to worker availability and system design. We propose two system designs for our CC scheme that allows flexibility in distributing the computational load between the master and the workers based on the accessibility of input data. Our experimental results demonstrate the superiority of our scheme compared to the state-of-the-art CC schemes for image classification tasks, and pave the path for designing new schemes for distributed computation of any general ML classification tasks.
Compact Twice Fusion Network for Edge Detection

The significance of multi-scale features has been gradually recognized by the edge detection community. However, the fusion of multi-scale features increases the complexity of the model, which is not friendly to practical application. In this work, we propose a Compact Twice Fusion Network (CTFN) to fully integrate multi-scale features while maintaining the compactness of the model. CTFN includes two lightweight multi-scale feature fusion modules: a Semantic Enhancement Module (SEM) that can utilize the semantic information contained in coarse-scale features to guide the learning of fine-scale features, and a Pseudo Pixel-level Weighting (PPW) module that aggregate the complementary merits of multi-scale features by assigning weights to all features. Notwithstanding all this, the interference of texture noise makes the correct classification of some pixels still a challenge. For these hard samples, we propose a novel loss function, coined Dynamic Focal Loss, which reshapes the standard cross-entropy loss and dynamically adjusts the weights to correct the distribution of hard samples. We evaluate our method on three datasets, i.e., BSDS500, NYUDv2, and BIPEDv2. Compared with state-of-the-art methods, CTFN achieves competitive accuracy with less parameters and computational cost. Apart from the backbone, CTFN requires only 0.1M additional parameters, which reduces its computation cost to just 60% of other state-of-the-art methods. The codes are available at https://github.com/Li-yachuan/CTFN-pytorch-master.
Estimating label quality and errors in semantic segmentation data via any model

The labor-intensive annotation process of semantic segmentation datasets is often prone to errors, since humans struggle to label every pixel correctly. We study algorithms to automatically detect such annotation errors, in particular methods to score label quality, such that the images with the lowest scores are least likely to be correctly labeled. This helps prioritize what data to review in order to ensure a high-quality training/evaluation dataset, which is critical in sensitive applications such as medical imaging and autonomous vehicles. Widely applicable, our label quality scores rely on probabilistic predictions from a trained segmentation model -- any model architecture and training procedure can be utilized. Here we study 7 different label quality scoring methods used in conjunction with a DeepLabV3+ or a FPN segmentation model to detect annotation errors in a version of the SYNTHIA dataset. Precision-recall evaluations reveal a score -- the soft-minimum of the model-estimated likelihoods of each pixel's annotated class -- that is particularly effective to identify images that are mislabeled, across multiple types of annotation error.
A Modal Logic for Explaining some Graph Neural Networks

In this paper, we propose a modal logic in which counting modalities appear in linear inequalities. We show that each formula can be transformed into an equivalent graph neural network (GNN). We also show that each GNN can be transformed into a formula. We show that the satisfiability problem is decidable. We also discuss some variants that are in PSPACE.