Reviewerly/exHarmony huggingface dataset:
The The Influence of Teaching Prefixes and Suffixes In Context On Developing Students’ English Vocabulary at Sman 2 Garut

Vocabulary through morphemes provides meaningful vocabulary acquisition skill. Students learn to guess the word meaning by looking for prefixes and suffixes in context. The students analyze prefixes and suffixes within the words. Then, the students analyze other context clues such as part of speech around the words. The purpose of this research is to uncover the significant difference between the influence of teaching prefixes and suffixes in context and without context toward students’ English vocabulary development. This research uses quantitative study in line with experimental method. This research was held in SMAN 2 Garut located at Jalan Guntur No. 3 Leles Garut. The population of this research is eight classes of students in the second grade of SMAN 2 Garut. The writer chooses two classes to be sample of the research, they are class XI IPA 1 as experimental group and class XI IPS 1 as the control group. The total numbers of the sample are 66 students. Pre-test and post-test are used to collect research data. Analisis butir soal pilihan ganda is used to test the validity of the data. To find out the reliability of the data, the writer uses split-half method as proposed by Suharsimi Arikunto ( 2009: 93). Because the data distribution is normal, t-test formula is used to analyze the data. Students’ diversity scores averages show that the experimental group gets 40 and the control group gets 30.15. It means that there is positive progress of the experimental group after having the treatment. Based on the data analysis, tcounted = 3.5 and ttable= 1.99. Because tcounted= 3.5 is out of Ho acceptation area, between ttable = -1.99 and 1.99, so that Ha is accepted. It means that Ha is accepted, there is significant difference of influence between teaching prefixes and suffixes in context and without context toward students’ English vocabulary development in the Second Grade Students of SMAN 2 Garut on ninety five percent (95%) significance level.
IMPLEMENTATION OF THE WEIGHT PRODUCT METHOD IN THE SYSTEM NEW STUDENT ADMISSION

The development of information technology is something that cannot be separated from life today. The development of information technology, especially the internet, is certainly welcomed by all circles, and has even penetrated the world of education since the last few years, thus creating competitive competition in every educational institution. Currently, there are not many schools in Indonesia that hold new student admissions (PPDB) by utilizing the online system. Of course, this will take a very long time, because after selecting the prospective students, the committee must recap the names and grades of the students accepted. We need a system that will support decisions in the selection of new student admissions so that the resulting output is more accurate. To solve this problem, it is necessary to have a decision support system for the selection process for new admissions using the Weight method Products . With this method the PPDB selection calculation will be more objective because the calculation is based on predetermined weights and assessment criteria. So that the creation of an optimal system that will facilitate the PPDB selection process.
Design of a Cricket Dryer Using PID Control

Cricket drying is an important process in the production of animal feed and the rearing of certain animals. Dried crickets have a longer shelf life, more stable nutritional content, and a lower risk of contamination. Therefore, the use of an efficient and controlled cricket dryer is important to achieve optimal results. The main objective of the design of a cricket dryer with PID control is to create a system that can adjust the drying temperature automatically. With this control system, it is expected to be able to overcome the problem of the drying process which is constrained by weather factors. The PID controller (from the English abbreviation: Proportional–Integral–Derivative controller) is a feedback mechanism controller that is usually used in industrial control systems. The control system is an inseparable part of processes in the field of industrial engineering, because with the help of control systems, desired results can be realized. The control system is needed to improve the response of the dynamic system so that the output signal is obtained as desired. A good control system has a good response to various input signals. Tools made with this system can dry raw materials with low moisture content, so that the dried raw materials can be directly processed into other forms, and the production process is not hampered by a shortage of raw materials. This tool is capable of producing temperatures up to 100 degrees Celsius. A timer relay is also included to regulate the length of the drying process so that quality raw materials are obtained.
Design of Horizontal Mixer Capacity of 15 Kilograms

Some home industries, such as the bakery industry, in their production still use manual methods or human labor to mix the bread dough until it is smooth. The disadvantages of using the manual method are that production takes a long time, requires a lot of energy, requires more than one person to mix all the ingredients for the bread dough. So research is needed on the design of a tool that can facilitate the production of bread, namely a mixer. The purpose of this study is to design a horizontal mixer machine and to design a horizontal dough mixer machine with a capacity of 15. This research is supported by literature studies in the form of books and internet sites. The design of a bread dough mixer machine with a capacity of 15 kg which describes in detail the machine elements used includes calculating the power of the driving motor, the diameter of the pulleys, the diameter and number of teeth on the gears, as well as determining the shaft material and shaft diameter used, the type of belt, and the dough mixing lever. This designed mixing machine has a capacity of 60 kg/hour which is capable of producing 1500 pieces of bread in 1 hour, while the components used consist of 1 motor with a power of 1.5 kW, 2 pulleys, 2 continuous rotation shafts, 1 rotating shaft. turning the mixing spoon, 2 belts, 2 gears, and 4 bearings that support the shaft. The bread dough reservoir is made of stainless plates to avoid rust which is able to maintain cleanliness and sterilize food dough, the shaft material uses ST 60 carbon steel. It is hoped that using a horizontal mixer machine as a result of this design can be one of the solutions in fulfilling production capacity both in quality as well as production quantity.
Production of Effective Microorganisms (Em) From Organic Waste from Traditional Markets in Tambelang District as Biocatalysts

&#x0D; &#x0D; &#x0D; &#x0D; Municipal solid waste largely consists of market waste which has become a critical problem for city environment. Indiscriminate dumping of waste around city poses environmental hazards causing ecological imbalances with respect to land, water and air pollution. This waste in the form of vegetable left over, rotten vegetables can be successfully being converted into effective micro-organism i.e. In the present work, the influence of hydraulic retention time on dark fermentation metabolism was evaluated through the operation and analysis of a series of three tank reactors ranging from 7 dayto 21 day.In depth analysis of microbial communities showed growth increases up to 8,0 x 108cpu/ml.&#x0D; &#x0D; &#x0D; &#x0D;
Consideration of Liver Function test with Erythropoietin Hormone Levels in Iraqi Patients Diagnosed with Lymphoid and Myeloid Leukemia

The study was carried out to measure the erythropoietin hormone level and a number of the hematological variables (redblood cells, platelets, monocytes and lymphocytes) and biochemical variables (erythropoietin, alkaline phosphatase, alaninetransaminase and aspartate transaminase) for two types of leukemia (lymphoid leukemia and myeloid leukemia). EnzymeLinked Immune Sorbent Assay (ELISA) was used to detect level of erythropoietin in patients with lymphoid and myeloid,leukemia and control group , liver enzymes activity were measured including alkaline phosphatase (ALP) alanine transaminase(ALT) and aspartate transaminase (AST). Also estimated some hematological variables were estimated: red blood cells,platelets, lymphocytes and monocytes, as well as, ABO blood groups in all studied groups. The statistical analysis showedsignificant (P&lt; 0.05) increase in the level of erythropoietin, liver enzymes, lymphocytes and monocytes number in patientswith lymphoid and myeloid leukemia when compared with the control group. While, there was a decrease (P&lt; 0.05) in redblood cells count and platelets count in the patients groups compared with the healthy group. Also, the results from statisticalanalysis revealed that there was a strong correlation between ABO blood groups and leukemia.
A review paper on simulated intellect

John mccarthy is the father of artificial intelligence. Artificial intelligence is a way of making computers and computer controlled machines and software to be intelligent enough to learn, decide, and execute in a manner similar to that of the way a human brain thinks and acts. Artificial intelligence is a knowledge and machinery based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering. It is defined as “the science and engineering of making intelligent machines, especially intelligent computer programs
Image Deblurring using Deep Learning

Abstract: Image deblurring is a most considered problem in low-level computer vision with an objective to recover a high resolution image from a blurred input image. It is a mandatory task in image processing and has been studied for several years which is used in reconstruction of images which are blurred due to various reasons. Particularly in these years, deep learning approaches have shown great promises in various image restoration tasks, which includes image deblurring. Among these, DBSRCNN is a powerful deep learning approach that has been used for super-resolution and image deblurring. In this model developed we are going to implement De Blurring Super Resolution Convolutional Neural Network (DBSRCNN) for blurred reconstruction. The proposed method achieves superior results in both quantitative and qualitative evaluations, depicting its effectiveness in image deblurring tasks.
Stable Diffusion is Unstable

Recently, text-to-image models have been thriving. Despite their powerful generative capacity, our research has uncovered a lack of robustness in this generation process. Specifically, the introduction of small perturbations to the text prompts can result in the blending of primary subjects with other categories or their complete disappearance in the generated images. In this paper, we propose Auto-attack on Text-to-image Models (ATM), a gradient-based approach, to effectively and efficiently generate such perturbations. By learning a Gumbel Softmax distribution, we can make the discrete process of word replacement or extension continuous, thus ensuring the differentiability of the perturbation generation. Once the distribution is learned, ATM can sample multiple attack samples simultaneously. These attack samples can prevent the generative model from generating the desired subjects without compromising image quality. ATM has achieved a 91.1% success rate in short-text attacks and an 81.2% success rate in long-text attacks. Further empirical analysis revealed four attack patterns based on: 1) the variability in generation speed, 2) the similarity of coarse-grained characteristics, 3) the polysemy of words, and 4) the positioning of words.
Quantum multiparameter estimation with graph states

In the SU(2) dynamics, it is especially significant to achieve a simultaneous optimal multiparameter estimation but it is very difficult. Evolution on SU(N) dynamics is a research method to explore simultaneous multiparameter estimation with the quantum network. As the highly entangled states, graph state, is an intrinsical quantum resource for quantum metrology. For n-qubit graph state, we propose a simultaneous multiparameter estimation scheme that investigates evolution in SU(N) dynamics. For single-parameter estimation, the precision limit beyond the Heisenberg limit in the higher dimension spin of SU(2). We consider two scenarios where the Hamiltonian operator is commutation and non-commutation respectively and verify that the global estimation precision is higher than the local estimation precision. In the parameter limit condition, the precision of parameter estimation for the simultaneous estimation of each parameter is equal to the precision of the singleparameter estimation. In addition, we find a precision-enhancement scheme that depends on the dynamics SU(N). The smaller the N for the dynamics evolution, the higher the precision of the parameter estimation. Finally, we prove that the graph state is the optimal state of quantum metrology, a set of optimal measurement basic can be found, and the precision limit of multiparameter estimation can attain the quantum Cram\'er-Rao bound.
Structural Re-weighting Improves Graph Domain Adaptation

In many real-world applications, graph-structured data used for training and testing have differences in distribution, such as in high energy physics (HEP) where simulation data used for training may not match real experiments. Graph domain adaptation (GDA) is a method used to address these differences. However, current GDA primarily works by aligning the distributions of node representations output by a single graph neural network encoder shared across the training and testing domains, which may often yield sub-optimal solutions. This work examines different impacts of distribution shifts caused by either graph structure or node attributes and identifies a new type of shift, named conditional structure shift (CSS), which current GDA approaches are provably sub-optimal to deal with. A novel approach, called structural reweighting (StruRW), is proposed to address this issue and is tested on synthetic graphs, four benchmark datasets, and a new application in HEP. StruRW has shown significant performance improvement over the baselines in the settings with large graph structure shifts, and reasonable performance improvement when node attribute shift dominates.
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models

Latent Gaussian models have a rich history in statistics and machine learning, with applications ranging from factor analysis to compressed sensing to time series analysis. The classical method for maximizing the likelihood of these models is the expectation-maximization (EM) algorithm. For problems with high-dimensional latent variables and large datasets, EM scales poorly because it needs to invert as many large covariance matrices as the number of data points. We introduce probabilistic unrolling, a method that combines Monte Carlo sampling with iterative linear solvers to circumvent matrix inversion. Our theoretical analyses reveal that unrolling and backpropagation through the iterations of the solver can accelerate gradient estimation for maximum likelihood estimation. In experiments on simulated and real data, we demonstrate that probabilistic unrolling learns latent Gaussian models up to an order of magnitude faster than gradient EM, with minimal losses in model performance.
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs

Exploration in environments which differ across episodes has received increasing attention in recent years. Current methods use some combination of global novelty bonuses, computed using the agent's entire training experience, and \textit{episodic novelty bonuses}, computed using only experience from the current episode. However, the use of these two types of bonuses has been ad-hoc and poorly understood. In this work, we shed light on the behavior of these two types of bonuses through controlled experiments on easily interpretable tasks as well as challenging pixel-based settings. We find that the two types of bonuses succeed in different settings, with episodic bonuses being most effective when there is little shared structure across episodes and global bonuses being effective when more structure is shared. We develop a conceptual framework which makes this notion of shared structure precise by considering the variance of the value function across contexts, and which provides a unifying explanation of our empirical results. We furthermore find that combining the two bonuses can lead to more robust performance across different degrees of shared structure, and investigate different algorithmic choices for defining and combining global and episodic bonuses based on function approximation. This results in an algorithm which sets a new state of the art across 16 tasks from the MiniHack suite used in prior work, and also performs robustly on Habitat and Montezuma's Revenge.
Early Weight Averaging meets High Learning Rates for LLM Pre-training

Training Large Language Models (LLMs) incurs significant cost; hence, any strategy that accelerates model convergence is helpful. In this paper, we investigate the ability of a simple idea checkpoint averaging along the trajectory of a training run to improve both convergence and generalization quite early on during training. Here we show that models trained with high learning rates observe higher gains due to checkpoint averaging. Furthermore, these gains are amplified when checkpoints are sampled with considerable spacing in training steps. Our training recipe outperforms conventional training and popular checkpoint averaging baselines such as exponential moving average (EMA) and stochastic moving average (SWA). We evaluate our training recipe by pre-training LLMs, where high learning rates are inherently preferred due to extremely large batch sizes. Specifically, we pre-trained nanoGPT-2 models of varying sizes, small (125M), medium (335M), and large (770M)on the OpenWebText dataset, comprised of 9B tokens. Additionally, we present results for publicly available Pythia LLMs, ranging from 1B to 12B, which were trained on the PILE-deduped dataset containing 207B tokens.
Generating Private Synthetic Data with Genetic Algorithms

We study the problem of efficiently generating differentially private synthetic data that approximate the statistical properties of an underlying sensitive dataset. In recent years, there has been a growing line of work that approaches this problem using first-order optimization techniques. However, such techniques are restricted to optimizing differentiable objectives only, severely limiting the types of analyses that can be conducted. For example, first-order mechanisms have been primarily successful in approximating statistical queries only in the form of marginals for discrete data domains. In some cases, one can circumvent such issues by relaxing the task's objective to maintain differentiability. However, even when possible, these approaches impose a fundamental limitation in which modifications to the minimization problem become additional sources of error. Therefore, we propose Private-GSD, a private genetic algorithm based on zeroth-order optimization heuristics that do not require modifying the original objective. As a result, it avoids the aforementioned limitations of first-order optimization. We empirically evaluate Private-GSD against baseline algorithms on data derived from the American Community Survey across a variety of statistics--otherwise known as statistical queries--both for discrete and real-valued attributes. We show that Private-GSD outperforms the state-of-the-art methods on non-differential queries while matching accuracy in approximating differentiable ones.
Dual self-distillation of U-shaped networks for 3D medical image segmentation

U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (DSD) framework for U-shaped networks for 3D medical image segmentation. DSD distills knowledge from the ground-truth segmentation labels to the decoder layers and also between the encoder and decoder layers of a single U-shaped network. DSD is a generalized training strategy that could be attached to the backbone architecture of any U-shaped network to further improve its segmentation performance. We attached DSD on two state-of-the-art U-shaped backbones, and extensive experiments on two public 3D medical image segmentation datasets (cardiac substructure and brain tumor) demonstrated significant improvement over those backbones. On average, after attaching DSD to the U-shaped backbones, we observed an improvement of 4.25% and 3.15% in Dice similarity score for cardiac substructure and brain tumor segmentation respectively.
Stack Over-Flowing with Results: The Case for Domain-Specific Pre-Training Over One-Size-Fits-All Models

Large pre-trained neural language models have brought immense progress to both NLP and software engineering. Models in OpenAI's GPT series now dwarf Google's BERT and Meta's RoBERTa, which previously set new benchmarks on a wide range of NLP applications. These models are trained on massive corpora of heterogeneous data from web crawls, which enables them to learn general language patterns and semantic relationships. However, the largest models are both expensive to train and deploy and are often closed-source, so we lack access to their data and design decisions. We argue that this trend towards large, general-purpose models should be complemented with single-purpose, more modestly sized pre-trained models. In this work, we take StackOverflow (SO) as a domain example in which large volumes of rich aligned code and text data is available. We adopt standard practices for pre-training large language models, including using a very large context size (2,048 tokens), batch size (0.5M tokens) and training set (27B tokens), coupled with a powerful toolkit (Megatron-LM), to train two models: SOBertBase, with 109M parameters, and SOBertLarge with 762M parameters, at a budget of just $\$187$ and $\$800$ each. We compare the performance of our models with both the previous SOTA model trained on SO data exclusively as well general-purpose BERT models and OpenAI's ChatGPT on four SO-specific downstream tasks - question quality prediction, closed question prediction, named entity recognition and obsoletion prediction (a new task we introduce). Not only do our models consistently outperform all baselines, the smaller model is often sufficient for strong results. Both models are released to the public. These results demonstrate that pre-training both extensively and properly on in-domain data can yield a powerful and affordable alternative to leveraging closed-source general-purpose models.
Entanglement distribution in two-dimensional square grid network

We study entanglement generation in a quantum network where repeater nodes can perform $n$-qubit Greenberger-Horne-Zeilinger(GHZ) swaps, i.e., projective measurements, to fuse $n$ imperfect-Fidelity entangled-state fragments. We show that the distance-independent entanglement distribution rate found previously for this protocol, assuming perfectly-entangled states at the link level, does not survive. This is true also in two modified protocols we study: one that incorporates $l \to 1$ link-level distillation and another that spatially constrains the repeater nodes involved in the swaps. We obtain analytical formulas for a GHZ swap of multiple Werner states, which might be of independent interest. Whether the distance-independent entanglement rate might re-emerge with a spatio-temporally-optimized scheduling of GHZ swaps and multi-site block-distillation codes remains open.