Reviewerly/exHarmony huggingface dataset:
Generating Streams of Random Key Based on Image Chaos and Genetic Algorithm

Today the Genetic Algorithm (GA) tops all the standard algorithms in solving complex nonlinear equations based on the laws of nature. However, permute convergence is considered one of the most significant drawbacks of GA, which is known as increasing the number of iterations needed to achieve a global optimum. To address this shortcoming, this paper proposes a new GA based on chaotic systems. In GA processes, we use the logistic map and the Linear Feedback Shift Register (LFSR) to generate chaotic values to use instead of each step requiring random values. The Chaos Genetic Algorithm (CGA) avoids local convergence more frequently than the traditional GA due to its diversity. The concept is using chaotic sequences with LFSR to generate seed values for genetic algorithms, which can generate keys with a high degree of randomness. The quality of key (generated sequence) was tested using known standard tests, then a comparison table is presented to show the increase in ratios in the test before and after applying GA, demonstrating that the proposed system generates sequence (key) with high randomness degree, The proposed system achieved an increase in the randomness rate by four degrees on average and thus it solves the problem of repetition and linearity, Finally, The system is built in the Java environment.
Self-supervised Enhancement of Latent Discovery in GANs

Several methods for discovering interpretable directions in the latent space of pre-trained GANs have been proposed. Latent semantics discovered by unsupervised methods are relatively less disentangled than supervised methods since they do not use pre-trained attribute classifiers. We propose Scale Ranking Estimator (SRE), which is trained using self-supervision. SRE enhances the disentanglement in directions obtained by existing unsupervised disentanglement techniques. These directions are updated to preserve the ordering of variation within each direction in latent space. Qualitative and quantitative evaluation of the discovered directions demonstrates that our proposed method significantly improves disentanglement in various datasets. We also show that the learned SRE can be used to perform Attribute-based image retrieval task without further training.
Parameter tuning and model selection in optimal transport with semi-dual Brenier formulation

Over the past few years, numerous computational models have been developed to solve Optimal Transport (OT) in a stochastic setting, where distributions are represented by samples and where the goal is to find the closest map to the ground truth OT map, unknown in practical settings. So far, no quantitative criterion has yet been put forward to tune the parameters of these models and select maps that best approximate the ground truth. To perform this task, we propose to leverage the Brenier formulation of OT.Theoretically, we show that this formulation guarantees that, up to sharp a distortion parameter depending on the smoothness/strong convexity and a statistical deviation term, the selected map achieves the lowest quadratic error to the ground truth. This criterion, estimated via convex optimization, enables parameter tuning and model selection among entropic regularization of OT, input convex neural networks and smooth and strongly convex nearest-Brenier (SSNB) models.We also use this criterion to question the use of OT in Domain-Adaptation (DA). In a standard DA experiment, it enables us to identify the potential that is closest to the true OT map between the source and the target. Yet, we observe that this selected potential is far from being the one that performs best for the downstream transfer classification task.
TCUDB: Accelerating Database with Tensor Processors

The emergence of novel hardware accelerators has powered the tremendous growth of machine learning in recent years. These accelerators deliver incomparable performance gains in processing high-volume matrix operators, particularly matrix multiplication, a core component of neural network training and inference. In this work, we explored opportunities of accelerating database systems using NVIDIA's Tensor Core Units (TCUs). We present TCUDB, a TCU-accelerated query engine processing a set of query operators including natural joins and group-by aggregates as matrix operators within TCUs. Matrix multiplication was considered inefficient in the past; however, this strategy has remained largely unexplored in conventional GPU-based databases, which primarily rely on vector or scalar processing. We demonstrate the significant performance gain of TCUDB in a range of real-world applications including entity matching, graph query processing, and matrix-based data analytics. TCUDB achieves up to 288x speedup compared to a baseline GPU-based query engine.
Ordinal Sums, Clockwise Hackenbush, and Domino Shave

We present two rulesets, Domino Shave and Clockwise Hackenbush. The first is somehow natural and, as special cases, includes Stirling Shave and Hetyei's Bernoulli game. Clockwise Hackenbush seems artificial yet it is equivalent to Domino Shave. From the pictorial form of the game, and a knowledge of Hackenbush, the decomposition into ordinal sums is immediate. The values of Clockwise Blue-Red Hackenbush are numbers and we provide an explicit formula for the ordinal sum of numbers where the literal form of the base is $\{x\,|\,\}$ or $\{\,|\,x\}$, and $x$ is a number. That formula generalizes van Roode's signed binary number method for Blue-Red Hackenbush.
Pure state tomography with parallel unentangled measurements

Quantum state tomography (QST) aims at estimating a quantum state from averaged quantum measurements made on copies of the state. Most quantum algorithms rely on QST at some point and it is a well explored topic in the literature, mostly for mixed states. In this paper we focus on the QST of a pure quantum state using parallel unentangled measurements. Pure states are a small but useful subset of all quantum states, their tomography requires fewer measurements and is essentially a phase recovery problem. Parallel unentangled measurements are easy to implement in practice because they allow the user to measure each qubit individually. We propose two sets of quantum measurements that one can make on a pure state as well as the algorithms that use the measurements outcomes in order to identify the state. We also discuss how those estimates can be fined tuned by finding the state that maximizes the likelihood of the measurements with different variants of the likelihood. The performances of the proposed three types of QST methods are validated by means of detailed numerical tests.
A Tale of a Probe and a Parser

Measuring what linguistic information is encoded in neural models of language has become popular in NLP. Researchers approach this enterprise by training "probes" - supervised models designed to extract linguistic structure from another model's output. One such probe is the structural probe (Hewitt and Manning, 2019), designed to quantify the extent to which syntactic information is encoded in contextualised word representations. The structural probe has a novel design, unattested in the parsing literature, the precise benefit of which is not immediately obvious. To explore whether syntactic probes would do better to make use of existing techniques, we compare the structural probe to a more traditional parser with an identical lightweight parameterisation. The parser outperforms structural probe on UUAS in seven of nine analysed languages, often by a substantial amount (e.g. by 11.1 points in English). Under a second less common metric, however, there is the opposite trend - the structural probe outperforms the parser. This begs the question: which metric should we prefer?
Upaya Meningkatkan Hasil Belajar pada Materi Berbahasa Krama Inggil Menggunakan Metode Bermain Peran dengan Model Pembelajaran Cooperative Learning

Class action research in general aims to improve the quality of learning for students of grade IV Purwoprajan II State Elementary School. Specifically aimed at: (1) Describe the activities of learners; (2) Describe the management of learning by the teacher; (3) Knowing the learning outcomes of learners who refer to role-playing methods with cooperative learning models in grade IV of Purwoprajan II State Elementary School even semester of the 2019/ 2020 school year for Javanese mulok subjects on krama inggil language material. Study subjects grade IV SDN Purwoprajan II Jebres Surakarta. Research data is obtained by observation and documentation. Then analyzed with descriptive qualitative. Research is conducted in two cycles in which each cycle consists of stages of planning, implementation, observation, and reflection. The results of the study concluded that the use of role-playing methods with cooperative learning models can increase the increase in the average value of learners in learning the language material in Javanese language content.
A cloud screening algorithm for ground-based sun photometry using all-sky images and deep transfer learning

Abstract. Aerosol optical depth (AOD) is used to characterize aerosol loadings within Earth’s atmosphere. Sun photometers measure AOD from the Earth’s surface based on direct-sunlight intensity readings by spectrally narrow light detectors. However, when the solar disk is partially obscured by cloud cover, sun photometer measurements can be biased due to the interaction of sunlight with cloud constituents. We present a novel deep transfer learning model on all-sky images to support more accurate AOD retrievals. We used three independent image datasets for training and testing: the novel Northern Colorado All-Sky Image (NCASI), the Whole Sky Image SEGmentation (WSISEG), and the METCRAX-II datasets from the National Center for Atmospheric Research (NCAR). We visually partitioned all-sky images into three categories: 1) clear sky around the solar disk, 2) thin cirrus obstructing the solar disk, and 3) thick, non-cirrus clouds obstructing the solar disk. Two-thirds of the images were allocated for training and one-third were allocated for testing. We trained models based on all possible combinations of the training sets. The best-performing model successfully classified 95.5 %, 96.9 %, and 89.1 % of testing images from NCASI, METCRAX-II and WSISEG datasets, respectively. Our results demonstrate that all-sky imaging with deep transfer learning can be applied toward cloud screening, which would aid ground-based AOD measurements.
ICT Adoption on Cultural Preservation Amongst Indian Youth in Selangor, Malaysia

This paper presents the significant role of Information and Communication Technology (ICT) on cultural preservation among Indian Youth in Selangor, Malaysia.A middle-class youth group aged 18 to 35 years old participated in the study in Subang Jaya, Selangor.In-depth interviews, which were used as a qualitative data collection method, helped the researchers in developing a comprehensive understanding of how ICT is used to preserve culture.Usage of multiple and variety of ICT devices in daily life gave the space and opportunity to know about culture.However, some informants feel that socialization and parents imparting knowledge about culture still play an important role.It is hoped that the findings of this study will contribute to the existing literature on how ICT can play a role in preserving culture in the future and among the coming younger generation who will be leaving life in a world surrounded by ICT.Despite being a minority in Malaysia, the Indian community should be given equal rights and access to ICT to carry out their daily functions, including developing their cultural capital and cultural preservation
Time Based Prediction of Breast Cancer Tumor Recurrence Using Machine Learning

An in-depth study using the database from GLOBOCAN, CDC, and WHO health repository highlights the lethality of breast cancer, taking thousands of lives each year.Therefore, timely prediction of cancer can help patients to consult the doctor on time.In the past various studies have successfully predicted the nature of the tumor to be benign or malignant and if the breast cancer tumor will reoccur or not.However, no time-based models have been studied previously.With the help of Machine Learning, this study has shown various prediction models that can predict the time as accurately as 1 year for the tumor to reappear in malignant patients.In this prediction analysis performed on the 198 patients, 40% of the total patients were predicted to have breast cancer tumors reoccurring within 1st year of the diagnosis.The proposed machine learning techniques use various classification models such as Spectral clustering, DBSCAN, and k-means along with prediction models like Support Vector Machines (SVM), Decision trees, and Random forest.The results demonstrate the ability of the model to predict the time taken by the tumor to reoccur or the time taken by the patient for full recovery with the best accuracy of 78.7% using SVM.This population-based study performed on multivariate real attributed characteristics data can therefore provide the patients a reasonable estimate about their recovery time or the time before which they should consult the doctor.
New primitives for bounded degradation in network service

Certain new ascendant data center workloads can absorb some degradation in network service, not needing fully reliable data transport and/or their fair-share of network bandwidth. This opens up opportunities for superior network and infrastructure multiplexing by having this flexible traffic cede capacity under congestion to regular traffic with stricter needs. We posit there is opportunity in network service primitives which permit degradation within certain bounds, such that flexible traffic still receives an acceptable level of service, while benefiting from its weaker requirements. We propose two primitives, namely guaranteed partial delivery and bounded deprioritization. We design a budgeting algorithm to provide guarantees relative to their fair share, which is measured via probing. The requirement of budgeting and probing limits the algorithm's applicability to large flexible flows. We evaluate our algorithm with large flexible flows and for three workloads of regular flows of small size, large size and a distribution of sizes. Across the workloads, our algorithm achieves less speed-up of regular flows than fixed prioritization, especially for the small flows workload (1.25x vs. 6.82 in the 99th %-tile). Our algorithm provides better guarantees in the workload with large regular flows (with 14.5% vs. 32.5% of flexible flows being slowed down beyond their guarantee). However, it provides not much better or even slightly worse guarantees for the other two workloads. The ability to enforce guarantees is influenced by flow fair share interdependence, measurement inaccuracies and dependency on convergence. We observe that priority changes to probe or to deprioritize causes queue shifts which deteriorate guarantees and limit possible speed-up, especially of small flows. We find that mechanisms to both prioritize traffic and track guarantees should be as non-disruptive as possible.
An Empirical Study on Ethereum Private Transactions and the Security Implications

Recently, Decentralized Finance (DeFi) platforms on Ethereum are booming, and numerous traders are trying to capitalize on the opportunity for maximizing their benefits by launching front-running attacks and extracting Miner Extractable Values (MEVs) based on information in the public mempool. To protect end users from being harmed and hide transactions from the mempool, private transactions, a special type of transactions that are sent directly to miners, were invented. Private transactions have a high probability of being packed to the front positions of a block and being added to the blockchain by the target miner, without going through the public mempool, thus reducing the risk of being attacked by malicious entities. Despite the good intention of inventing private transactions, due to their stealthy nature, private transactions have also been used by attackers to launch attacks, which has a negative impact on the Ethereum ecosystem. However, existing works only touch upon private transactions as by-products when studying MEV, while a systematic study on private transactions is still missing. To fill this gap and paint a complete picture of private transactions, we take the first step towards investigating the private transactions on Ethereum. In particular, we collect large-scale private transaction datasets and perform analysis on their characteristics, transaction costs and miner profits, as well as security impacts. This work provides deep insights on different aspects of private transactions.
High-Accuracy RGB-D Face Recognition via Segmentation-Aware Face Depth Estimation and Mask-Guided Attention Network

Deep learning approaches have achieved highly accurate face recognition by training the models with very large face image datasets. Unlike the availability of large 2D face image datasets, there is a lack of large 3D face datasets available to the public. Existing public 3D face datasets were usually collected with few subjects, leading to the over-fitting problem. This paper proposes two CNN models to improve the RGB-D face recognition task. The first is a segmentation-aware depth estimation network, called DepthNet, which estimates depth maps from RGB face images by including semantic segmentation information for more accurate face region localization. The other is a novel mask-guided RGB-D face recognition model that contains an RGB recognition branch, a depth map recognition branch, and an auxiliary segmentation mask branch with a spatial attention module. Our DepthNet is used to augment a large 2D face image dataset to a large RGB-D face dataset, which is used for training an accurate RGB-D face recognition model. Furthermore, the proposed mask-guided RGB-D face recognition model can fully exploit the depth map and segmentation mask information and is more robust against pose variation than previous methods. Our experimental results show that DepthNet can produce more reliable depth maps from face images with the segmentation mask. Our mask-guided face recognition model outperforms state-of-the-art methods on several public 3D face datasets.
OUTDOOR EXPERIENTIAL METHOD FOR ENHANCING STUDENTS' VOCABULARY

The purpose of this study was to examine the relationship between students' interest in learning through outdoor activity and their vocabulary mastery, as well as whether or not the use of outdoor experiential methods can improve students' vocabulary mastery and whether or not the students are interested in learning vocabulary in this way. This study used quantitative methods, specifically in the form of a quasi-experimental design, to address the aims. Thirty second-graders from Class VIII A served as the experimental group in this study, which was conducted at SMP Darul Aman in March 2020. Thirty second-graders from Class VIII B served as the control group. The researcher administered questionnaires to students to gauge their interest in the outdoor experiential technique and conducted vocabulary tests for the pretest and posttest to get information about the students' progress in vocabulary mastering. Three conclusions come from this study: 1) The students were interested in using the outdoor experiential method to learn vocabulary, 2) the students' interest in the method was positively correlated with their vocabulary mastery, and 3) the outdoor experiential method proved effective in improving students' vocabulary mastery.
Zero-Shot Semantic Segmentation via Spatial and Multi-Scale Aware Visual Class Embedding

Fully supervised semantic segmentation technologies bring a paradigm shift in scene understanding. However, the burden of expensive labeling cost remains as a challenge. To solve the cost problem, recent studies proposed language model based zero-shot semantic segmentation (L-ZSSS) approaches. In this paper, we address L-ZSSS has a limitation in generalization which is a virtue of zero-shot learning. Tackling the limitation, we propose a language-model-free zero-shot semantic segmentation framework, Spatial and Multi-scale aware Visual Class Embedding Network (SM-VCENet). Furthermore, leveraging vision-oriented class embedding SM-VCENet enriches visual information of the class embedding by multi-scale attention and spatial attention. We also propose a novel benchmark (PASCAL2COCO) for zero-shot semantic segmentation, which provides generalization evaluation by domain adaptation and contains visually challenging samples. In experiments, our SM-VCENet outperforms zero-shot semantic segmentation state-of-the-art by a relative margin in PASCAL-5i benchmark and shows generalization-robustness in PASCAL2COCO benchmark.
Practice-Dependent Differences in the Automatization of Literary Arabic (LA) Speech Production

Native speakers of Arabic acquire language in a diglossic context that requires them to use different varieties for different purposes: spoken Arabic (SA) is the dialect they use informally in daily oral communications; literary Arabic (LA) is the variety they use mainly for reading, writing, and formal communications. In general, Arabic native speakers perform differently across different tasks and modalities­­—performance tends to be better when the task requires SA or LA in the same way it is normally used. In this study, an LA speech production task was performed by two groups of Arabic native speakers who varied significantly in their amount of practice with LA. Although both groups acquired LA under the same conditions, the group with more practice was more fluent. Practice-dependent differences are interpreted within a memory-based automaticity framework. Such a framework, it is argued, is able to account for differences both in general performance patterns among the Arab population as well as specific, practice-dependent patterns such as those observed in the present study.
Information chaos and creativity

Creativity is one of the key features that help distinguish humans and other species. Even within human society, creativity is also a crucial factor contributing to the competitive advantage of an individual or an organization. As creativity is an outcome of an individual’s mental process, information inputs are required for making creativity. The question is:Which kind of information input is better for creativity: structured or unstructured information?
SOSIALISASI SOFTWARE ACCURATE ACCOUNTING KEPADA SISWA SMAN 1 RANCAH CIAMIS-JAWA BARAT

Accounting is one of the financial procedures that should be prepared by every business that is carried out. However, accounting is also useful for learning for the academic world. The world of technology is developing, so that accounting is currently being updated with an accounting system, which will make it easier for users to compile accounting reports more precisely. In this community service, the participants are students of SMAN 1 Rancah, Ciamis, West Java. The material given is the socialization of the Accurate Accounting system. Accurate Accounting is a software that is able to help its users to compile a complete, informative and easy to use accounting financial report. So that the material provided will be easily absorbed by the participants. Constraints faced such as the lack of knowledge about basic accounting and ignorance of the Accurate Accounting software became the basis for community service to be carried out. The limited implementation time can be an important concern, because the scope of the accounting world is quite broad, starting from the basics of accounting and developing accounting as a system within the company and becoming an excellent learning medium for educational development. Keywords: financial statement, accounting, accurate, system