Reviewerly/exHarmony huggingface dataset:
An Evaluation of Preprocessing Steps and Tree-based Ensemble Machine Learning for Analysing Sentiment on Indonesian YouTube Comments

This study aims to find the best model for the sentiment analysis of Indonesian YouTube video comments.Datasets crawled from YouTube video comments about government services related to COVID-19 pandemic in Indonesia.There are two opinion datasets obtained from two different domains, different characteristics, and errors.The problem is that comments from YouTube videos are very unstructured, containing spelling, diction, and slang word errors.The scenario for the solution of the problem is to test several preprocessing techniques, including standard preprocessing such as stop word removal, slang word, emoticon conversion, and stemming.Feature extraction using count vectorizer and TF-IDF method.For the development of the model, five types of models were tested, namely Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Extra Tree classifier.The result is a model with a maximum accuracy of 89.68% using a combination of standard preprocessing (converting emoticons and handling unstructured words), the count vectorizer feature extraction, and Extra Tree model classifier.
New Physics Agnostic Selections For New Physics Searches

We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). Based on the loss assigned to each event, input data can be split into a background control sample and a signal enriched sample. Following this strategy, one can enhance the sensitivity to new physics with no assumption on the underlying new physics signature. Our results show that a typical BSM search on the signal enriched group is more sensitive than an equivalent search on the original dataset.
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models

We present a novel compression algorithm for reducing the storage of LiDAR sensor data streams. Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to reduce the bitrate of both geometry and intensity values. Towards this goal, we propose a novel conditional entropy model that models the probabilities of the octree symbols by considering both coarse level geometry and previous sweeps' geometric and intensity information. We then use the learned probability to encode the full data stream into a compact one. Our experiments demonstrate that our method significantly reduces the joint geometry and intensity bitrate over prior state-of-the-art LiDAR compression methods, with a reduction of 7-17% and 15-35% on the UrbanCity and SemanticKITTI datasets respectively.
PENERAPAN GERAKAN LITERASI SEKOLAH DI SEKOLAH DASAR

This study aims to obtain information about the application of GLS or the School Literacy Movement in Cluster 01 Elementary School, Singgahan District, Tuban Regency. This study uses qualitative research with a case study research design. Data collection techniques used are observation, interviews and documentation. The results showed that, the application of the School Literacy Movement in the Cluster 01 Elementary School in Singgahan District was still at the habituation stage, namely 1) through reading aloud for about 15 minutes before learning for the low class and heart reading for the high class, 2) making a reading corner in each class, and 3) provide school library facilities. In implementing the School Literacy Movement there are still some obstacles including: 1) the lack of reading books; 2) lack of interest in reading students; 3) the teacher has not fully carried out this activity; 4) lack of guidance from the local education office about this School Literacy Movement; 5) and parents of students who are somewhat indifferent to the needs of children in order to support the School Literacy Movement. Whereas efforts to overcome obstacles during the implementation of the School Literacy Movement are: 1) seeking the fulfillment of reading books and library facilities by allocating funds for School Operational Costs (BOS); 2) SDN Saringembat I frees students to choose whenever students want to read, it does not have to be 15 minutes pre-learning so that a carpet is provided in the reading corner for them to use as a base for seating when they want to read; 3) SDN Saringembat I in order to improve the quality of GLS implementation by discussing and collaborating with other schools on the occasion when gathering at the Teacher Working Group activities in group 01 and the Principal Working Group (KKKS) or to other schools that are also GLS reference schools.
Where to Submit? Helping Researchers to Choose the Right Venue

Whenever researchers write a paper, the same question occurs: "Where to submit?"In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper.We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue.We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset.WTS achieves an Accuracy@5 of approximately 83 % for AI papers and 95 % in the field of medicine.It is open source 1 and available for testing on https://wheretosubmit.ml.
ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding.

Neural architecture search (NAS) aims to produce the optimal sparse solution from a high-dimensional space spanned by all candidate connections. Current gradient-based NAS methods commonly ignore the constraint of sparsity in the search phase, but project the optimized solution onto a sparse one by post-processing. As a result, the dense super-net for search is inefficient to train and has a gap with the projected architecture for evaluation. In this paper, we formulate neural architecture search as a sparse coding problem. We perform the differentiable search on a compressed lower-dimensional space that has the same validation loss as the original sparse solution space, and recover an architecture by solving the sparse coding problem. The differentiable search and architecture recovery are optimized in an alternate manner. By doing so, our network for search at each update satisfies the sparsity constraint and is efficient to train. In order to also eliminate the depth and width gap between the network in search and the target-net in evaluation, we further propose a method to search and evaluate in one stage under the target-net settings. When training finishes, architecture variables are absorbed into network weights. Thus we get the searched architecture and optimized parameters in a single run. In experiments, our two-stage method on CIFAR-10 requires only 0.05 GPU-day for search. Our one-stage method produces state-of-the-art performances on both CIFAR-10 and ImageNet at the cost of only evaluation time.
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph

Previous works on knowledge-to-text generation take as input a few RDF triples or keyvalue pairs conveying the knowledge of some entities to generate a natural language description.Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text.However, in practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge.In this paper, we introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text.Our dataset involves retrieving abundant knowledge of various types of main entities from a large knowledge graph (KG), which makes the current graph-to-sequence models severely suffer from the problems of information loss and parameter explosion while generating the descriptions.We address these challenges by proposing a multi-graph structure that is able to represent the original graph information more comprehensively.Furthermore, we also incorporate aggregation methods that learn to extract the rich graph information.Extensive experiments demonstrate the effectiveness of our model architecture.
Sparse learning with CART

Decision trees with binary splits are popularly constructed using Classification and Regression Trees (CART) methodology. For regression models, this approach recursively divides the data into two near-homogenous daughter nodes according to a split point that maximizes the reduction in sum of squares error (the impurity) along a particular variable. This paper aims to study the statistical properties of regression trees constructed with CART methodology. In doing so, we find that the training error is governed by the Pearson correlation between the optimal decision stump and response data in each node, which we bound by constructing a prior distribution on the split points and solving a nonlinear optimization problem. We leverage this connection between the training error and Pearson correlation to show that CART with cost-complexity pruning achieves an optimal complexity/goodness-of-fit tradeoff when the depth scales with the logarithm of the sample size. Data dependent quantities, which adapt to the dimensionality and latent structure of the regression model, are seen to govern the rates of convergence of the prediction error.
Teaching a GAN What Not to Learn

Generative adversarial networks (GANs) were originally envisioned as unsupervised generative models that learn to follow a target distribution. Variants such as conditional GANs, auxiliary-classifier GANs (ACGANs) project GANs on to supervised and semi-supervised learning frameworks by providing labelled data and using multi-class discriminators. In this paper, we approach the supervised GAN problem from a different perspective, one that is motivated by the philosophy of the famous Persian poet Rumi who said, art of knowing is knowing what to ignore. In the GAN framework, we not only provide the GAN positive data that it must learn to model, but also present it with so-called negative samples that it must learn to avoid - we call this Rumi Framework. This formulation allows the discriminator to represent the underlying target distribution better by learning to penalize generated samples that are undesirable - we show that this capability accelerates the learning process of the generator. We present a reformulation of the standard GAN (SGAN) and least-squares GAN (LSGAN) within the Rumi setting. The advantage of the reformulation is demonstrated by means of experiments conducted on MNIST, Fashion MNIST, CelebA, and CIFAR-10 datasets. Finally, we consider an application of the proposed formulation to address the important problem of learning an under-represented class in an unbalanced dataset. The Rumi approach results in substantially lower FID scores than the standard GAN frameworks while possessing better generalization capability.
Quantum search for unknown number of target items by hybridizing fixed-point method with trail-and-error method*

For the unsorted database quantum search with the unknown fraction $\lambda$ of target items, there are mainly two kinds of methods, i.e., fixed-point or trail-and-error. (i) In terms of the fixed-point method, Yoder et al. [Phys. Rev. Lett. 113, 210501 (2014)] claimed that the quadratic speedup over classical algorithms has been achieved. However, in this paper, we point out that this is not the case, because the query complexity of Yoder's algorithm is actually in $O(1/\sqrt{\lambda_0})$ rather than $O(1/\sqrt{\lambda})$, where $\lambda_0$ is a known lower bound of $\lambda$. (ii) In terms of the trail-and-error method, currently the algorithm without randomness has to take more than 1 times queries or iterations than the algorithm with randomly selected parameters. For the above problems, we provide the first hybrid quantum search algorithm based on the fixed-point and trail-and-error methods, where the matched multiphase Grover operations are trialed multiple times and the number of iterations increases exponentially along with the number of trials. The upper bound of expected queries as well as the optimal parameters are derived. Compared with Yoder's algorithm, the query complexity of our algorithm indeed achieves the optimal scaling in $\lambda$ for quantum search, which reconfirms the practicality of the fixed-point method. In addition, our algorithm also does not contain randomness, and compared with the existing deterministic algorithm, the query complexity can be reduced by about 1/3. Our work provides an new idea for the research on fixed-point and trial-and-error quantum search.
Chaotic Image Encryption Algorithm Based on Fractional Order Scrambling Wavelet Transform and 3D Cyclic Displacement Operation

In order to deal with the security of image information, this paper proposes a chaotic image encryption method based on fractional wavelet transform.First, fractional wavelet transform is applied to preprocess the image as a signal.After three-level fractional wavelet decomposition, high-frequency components are obtained with low frequency components.Second, the hash function is used to generate the initial value of the chaotic system for the plaintext operation, and the initial value is brought into the Lorenz system to obtain the chaotic sequence, the chaotic sequence is scrambled for the high frequency components, and the 3D cyclic displacement scrambling is performed for the low frequency components.The high-frequency components and low-frequency components are reconstructed by fractional wavelet transform and restored to the final scrambled image.Last, in order to further deepen the encryption effect, cyclic displacement diffusion is performed on the scrambled image to obtain the final encrypted image.Experimental and simulation results show that the algorithm proposed in this paper has excellent encryption effect and can resist common attack methods.
Laguerre–Volterra Feed-Forward Neural Network for Modeling PAM-4 High-Speed Links

In this article, we present a PAM-4 IBIS-AMI model derived from machine learning for time-domain simulation. More specifically, we report a Laguerre-Volterra-expanded feed-forward neural network (LVFFN) approach with one hidden layer and ten neurons to model the 28-Gb/s PAM-4 high-speed link buffer. The proposed LVFFN model reduces the model size and improves the computational efficiency dramatically compared with the Volterra series model and other transitional artificial neural network models. The LVFFN model is implemented in IBIS-AMI, an industrial standard, and is simulated in existing software platforms for eye-diagram analysis. This work has two innovations: 1) we propose a method that dramatically reduces the neural network model complexity through a Laguerre-Volterra expansion when modeling weakly nonlinear systems with memory and 2) we implement an LVFFN model into IBIS-AMI to enhance the model interoperability and transportability.
Image Blocking to Hide Secret Message

A huge number of messages circulate through social media, and many of these messages are confidential or carry personal information, which requires protection from intruders. The least significant bit method of data steganography is effective in masking messages, but it is not secure. In this research paper we will present a way to raise the security of the method of hiding messages without affecting the effectiveness of the method by keeping the MSE values low and PSNR values high.The proposed method will base on extracting a secret block from the image, this block will be used as a covering media to hold the secret message, after inserting the secret message the block will be returned back to the covering image.
Robust True Random Number Generator using Stochastic Short-Term Recovery of Charge Trapping FinFET for Advanced Hardware Security

In this work, we demonstrated a novel true random number generator (TRNG) utilizing stochastic short-term recovery of Charge- Trapping (CT) FinFET devices. The true random bits were generated by measuring the recovery time of CT-FinFET with a digital counter by a time-to-digital count converter (TDCC) unit. The resulting CT - TRNG circuit shows great immunity against a power noise of up to 600m V in amplitude and up to 1.5G Hz in frequency across a wide range of temperatures (-20 to 85°C). It passed all NIST 800-22 and NIST 800-90B randomness tests. We have shown this novel CT - TRNG to be the most promising high-reliability hardware security solution to date.
Advanced Antlion Optimizer with Discrete Ant Behavior for Feature Selection

Feature selection is important for learning algorithms, and it is still an open problem. Antlion optimizer is an excellent nature inspired method, but it doesn't work well for feature selection. This paper proposes a hybrid approach called Ant-Antlion Optimizer which combines advantages of antlion's smart behavior of antlion optimizer and ant's powerful searching movement of ant colony optimization. A mutation operator is also adopted to strengthen exploration ability. Comprehensive experiments by binary classification problems show that the proposed algorithm is superiority to other state-of-art methods on four performance indicators.
MANAGEMENT OF VIRTUAL LABORATORY EXPERIMENTS IN THE GEOSCIENCES FIELD IN THE TIME OF COVID-19 PANDEMIC

MANAGEMENT OF VIRTUAL LABORATORY EXPERIMENTS IN THE GEOSCIENCES FIELD IN THE TIME OF COVID-19 PANDEMIC
Characterizing Erasable Accounts in Ethereum

Being the most popular permissionless blockchain that supports smart contracts, Ethereum allows any user to create accounts on it. However, not all accounts matter. For example, the accounts due to attacks can be removed. In this paper, we conduct the first investigation on erasable accounts that can be removed to save system resources and even users’ money (i.e., ETH or gas). In particular, we propose and develop a novel tool named Glaser, which analyzes the State DataBase of Ethereum to discover five kinds of erasable accounts. The experimental results show that Glaser can accurately reveal 508,482 erasable accounts and these accounts lead to users wasting more than 106 million dollars. Glaser can help stop further economic loss caused by these detected accounts. Moreover, Glaser characterizes the attacks/behaviors related to detected erasable accounts through graph analysis.
Dense-Scale Feature Learning in Person Re-identification

For mass pedestrians re-identification (Re-ID), models must be capable of representing extremely complex and diverse multi-scale features. However, existing models only learn limited multi-scale features in a multi-branches manner, and directly expanding the number of scale branches for more scales will confuse the discrimination and affect performance. Because for a specific input image, there are a few scale features that are critical. In order to fulfill vast scale representation for person Re-ID and solve the contradiction of excessive scale declining performance, we proposed a novel Dense-Scale Feature Learning Network (DSLNet) which consist of two core components: Dense Connection Group (DCG) for providing abundant scale features, and Channel-Wise Scale Selection (CSS) module for dynamic select the most discriminative scale features to each input image. DCG is composed of a densely connected convolutional stream. The receptive field gradually increases as the feature flows along the convolution stream. Dense shortcut connections provide much more fused multi-scale features than existing methods. CSS is a novel attention module different from any existing model which calculates attention along the branch direction. By enhancing or suppressing specific scale branches, truly channel-wised multi-scale selection is realized. To the best of our knowledge, DSLNet is most lightweight and achieves state-of-the-art performance among lightweight models on four commonly used Re-ID datasets, surpassing most large-scale models.
A Two-Tier Service Filtering Model for Web Service QoS Prediction

Service recommendation technology is the key to realize the personalization of intelligent services. The recommended services need to meet functional requirements as well as non-functional requirements. Therefore, QoS-based service recommendation came into being. To perform intelligent service recommendations, matching users with convenient services based on QoS becomes an inevitable task. However, most of the service recommendation models are based on user interaction records to predict and recommend, ignoring the service-user correlation and unstable QoS values. In this article, we propose a new service recommendation model. We have performed two-tier filtering calculation on a large number of Web Services, filtering the contextual information of users and services and the instability of services. In the first filtering layer, we take the instability of QoS as an indicator to eliminate invalid services, which significantly reduces the service scale and eliminates the interference of invalid services on the recommendation to a certain extent. Further, we process the contextual information of both users and services in the second filtering layer. Considering the impact of the correlation between the service and the user, we use the geographic location information of the user and the service, and solve the combined features generated by the similarity between the user and the service to filter. Considering the sparsity of the service recommendation environment and the influence of noise generated by useless features, we use a model of factorization machine combined with the attention mechanism for computational processing. It effectively distinguishes the interactive importance of different features. We have conducted many experiments on real dataset, and the results show that our model is better than most baseline model in terms of recommendation performance.