Reviewerly/exHarmony huggingface dataset:
Choosing the Decision Hyper-parameter for Some Cautious Classifiers

In some sensitive domains where data imperfections are present, standard classification techniques reach their limits. To avoid misclassification that has serious consequences, recent works propose cautious classification algorithms to handle the problem. Despite of the presence of uncertainty, a point prediction classifier is forced to decide which single class to associate to a sample. In such a case, a cautious classifier proposes the appropriate subset of candidate classes that can be assigned to the sample in the presence of imperfect information. On the other hand, cautiousness should not override relevance and a trade-off has to be made between these two criteria. Among the existing cautious classifiers, two classifiers propose to manage this trade-off in the decision step of the classifier algorithm by the mean of a parametrized objective function. The first one is the non-deterministic classifier (ndc) proposed within the framework of probability theory and the second one is eclair (evidential classifier based on imprecise relabelling) proposed within the framework of belief functions. The theoretical aim of the mentioned parameter is to control the size of predictions for both classifiers. This paper proposes to study this parameter in order to select the “best" value in a classification task. First the gain for each prediction candidate is studied related to the values of the hyper-parameter. In the illustration section, we propose a method to choose this hyper-parameter base on the training data and we show the classification results on randomly generated data and we present some comparisons with two other imprecise classifiers on 11 UCI datasets based on five measures of imprecise classification performances used in the state of the art.
PS$^2$F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing

We propose a compact snapshot monocular depth estimation technique that relies on an engineered point spread function (PSF). Traditional approaches used in microscopic super-resolution imaging such as the Double-Helix PSF (DHPSF) are ill-suited for scenes that are more complex than a sparse set of point light sources. We show, using the Cram\'er-Rao lower bound, that separating the two lobes of the DHPSF and thereby capturing two separate images leads to a dramatic increase in depth accuracy. A special property of the phase mask used for generating the DHPSF is that a separation of the phase mask into two halves leads to a spatial separation of the two lobes. We leverage this property to build a compact polarization-based optical setup, where we place two orthogonal linear polarizers on each half of the DHPSF phase mask and then capture the resulting image with a polarization-sensitive camera. Results from simulations and a lab prototype demonstrate that our technique achieves up to $50\%$ lower depth error compared to state-of-the-art designs including the DHPSF and the Tetrapod PSF, with little to no loss in spatial resolution.
Scope of generative adversarial networks (GANs) in image processing

Generative Adversarial Network is the topic of interest in today’s research in the field of image processing and computer vision. A basic GAN model was introduced by Ian Goodfellow et al. in 2014. After that advancement in the field of research in GAN models has been application specific. In computer vision and image to image translation GANs are playing very effective role either in the case of face detection and recognition or in image resolution enhancement and image augmentation. This paper represents a concise overview of various GAN models along with their features and applications. Pix2Pix and conditional GAN models work upon paired datasets while other models like cycle GAN, discover GAN, dual GAN, info GAN, deep convolutional GAN etc. work upon unpaired datasets. Various image datasets which are commonly used for training of generator and discriminator networks are also discussed in this paper. Since partial mode collapse is a common problem to occur during training process for all models, therefore various normalization techniques are also preferred during the training of generator and discriminator networks. As the advancements in GAN models are increasing at a very fast rate, soon these models will also be preferred in commercial applications.
DRESS: Dynamic REal-time Sparse Subnets

The limited and dynamically varied resources on edge devices motivate us to deploy an optimized deep neural network that can adapt its sub-networks to fit in different resource constraints. However, existing works often build sub-networks through searching different network architectures in a hand-crafted sampling space, which not only can result in a subpar performance but also may cause on-device re-configuration overhead. In this paper, we propose a novel training algorithm, Dynamic REal-time Sparse Subnets (DRESS). DRESS samples multiple sub-networks from the same backbone network through row-based unstructured sparsity, and jointly trains these sub-networks in parallel with weighted loss. DRESS also exploits strategies including parameter reusing and row-based fine-grained sampling for efficient storage consumption and efficient on-device adaptation. Extensive experiments on public vision datasets show that DRESS yields significantly higher accuracy than state-of-the-art sub-networks.
FL-Defender: Combating Targeted Attacks in Federated Learning

Federated learning (FL) enables learning a global machine learning model from local data distributed among a set of participating workers. This makes it possible i) to train more accurate models due to learning from rich joint training data, and ii) to improve privacy by not sharing the workers' local private data with others. However, the distributed nature of FL makes it vulnerable to targeted poisoning attacks that negatively impact the integrity of the learned model while, unfortunately, being difficult to detect. Existing defenses against those attacks are limited by assumptions on the workers' data distribution, may degrade the global model performance on the main task and/or are ill-suited to high-dimensional models. In this paper, we analyze targeted attacks against FL and find that the neurons in the last layer of a deep learning (DL) model that are related to the attacks exhibit a different behavior from the unrelated neurons, making the last-layer gradients valuable features for attack detection. Accordingly, we propose \textit{FL-Defender} as a method to combat FL targeted attacks. It consists of i) engineering more robust discriminative features by calculating the worker-wise angle similarity for the workers' last-layer gradients, ii) compressing the resulting similarity vectors using PCA to reduce redundant information, and iii) re-weighting the workers' updates based on their deviation from the centroid of the compressed similarity vectors. Experiments on three data sets with different DL model sizes and data distributions show the effectiveness of our method at defending against label-flipping and backdoor attacks. Compared to several state-of-the-art defenses, FL-Defender achieves the lowest attack success rates, maintains the performance of the global model on the main task and causes minimal computational overhead on the server.
Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding

We evaluated 20+ Transformer models for ranking of long documents (including recent LongP models trained with FlashAttention) and compared them with simple FirstP baselines (applying the same model to input truncated to the first 512 tokens). We used MS MARCO Documents v1 as a primary training set and evaluated models in the zero-shot scenario as well as after fine-tuning on other collections. In our initial experiments with standard collections we found that long-document models underperformed FirstP or outperformed it by at most 5% on average in terms of MRR or NDCG. We then conjectured that this was not due to models inability to process long context but rather due to a positional bias of relevant passages, which tended to be among the first 512 document tokens. We found evidence that this bias was, indeed, present in at least two test sets, which motivated us to create a new collection MS MARCO FarRelevant where the relevant passages were not present among the first 512 tokens. Unlike standard collections where we observed both little benefit from incorporating longer contexts and limited variability in model performance (within a few %), experiments on MS MARCO FarRelevant uncovered dramatic differences among models. FirstP models performed roughly at the random-baseline level in both zero-shot and fine-tuning scenarios. Simple aggregation models (e.g., MaxP) had good zero-shot accuracy but benefited little from fine-tuning. Most other models had poor zero-shot performance (sometimes at a random baseline level) but outstripped MaxP by as much 13-28\% after finetuning. Thus, positional bias not only diminishes benefits of processing longer document contexts but also leads to model overfitting to this bias and performing poorly in a zero-shot setting when a distribution of relevant passages changes substantially. We make our software and MS MARCO FarRelevant available.
Perancangan Aplikasi Panduan Belajar Gerakan Tunarungu Menggunakan Adobe Flash

Deafness is one type of disability that is quite widely available in Indonesia, either experiencing it congenital or due to other factors where people with this disability experience a lack or loss of hearing ability so that they experience obstacles in language development. Deaf children still have the potential to learn to speak and speak. Therefore, deaf children need special services or media to develop language and speaking skills so as to minimize the impact of deafness experienced. Therefore, parents and teachers are obliged to guide children with hearing impairment to learn sign language. Currently, online education facilities are very attractive for children to learn. In fact, there are many developers who create modules, educational games, learning applications, tutorials, and more on Android. But not so for children with disabilities, especially deaf, Learning media is more likely to facilitate normal children. In fact, the learning materials and curricula of normal schools and special schools are the same, only the form of delivery is different. There are not many learning media for deaf children to learn, only the form of tutorials is limited to the introduction of the alphabet without any facilities to be able to deepen sign language itself. Deaf people need and deserve the same learning media as normal children.
Tensions and ambidexterity: a case study of an agile project at a government agency

Today’s dynamic business environment must continuously adapt its software development methods to changing technologies and new requirements on the part of customers. Therefore, Agile methods are being used more and more used because they emphasize both flexibility and the ability to change. However, at the same time, the business-driven need for predictability and control remains. The purpose of this case study is to explore and theorize on paradoxical tensions and ambidexterity during an Agile software development project at a government agency. The study empirically examines how tensions and the ambidextrous responses to these tensions are related to Agile values. Data was collected by conducting interviews and studying internal project documents. Four categories of tensions (learning, organizing, performing, and belonging) were used for analytical purposes. The findings suggest that most of the tensions perceived were in the categories of learning and performing. There are, furthermore, several connectionsbetween the ambidextrous responses to these tensions and Agile principles. A deeper understanding of Agile values and principles is required in order to make projects successful. The contribution made by the study, therefore, is of great importance because Agile methods are for leading projects, not only in Agile software development, but also in other industries and sectors.
Virtual linear map algorithm for classical boost in near-term quantum computing

The rapid progress in quantum computing witnessed in recent years has sparked widespread interest in developing scalable quantum information theoretic methods to work with large quantum systems. For instance, several approaches have been proposed to bypass tomographic state reconstruction, and yet retain to a certain extent the capability to estimate multiple physical properties of a given state previously measured. In this paper, we introduce the Virtual Linear Map Algorithm (VILMA), a new method that enables not only to estimate multiple operator averages using classical post-processing of informationally complete measurement outcomes, but also to do so for the image of the measured reference state under low-depth circuits of arbitrary, not necessarily physical, $k$-local maps. We also show that VILMA allows for the variational optimisation of the virtual circuit through sequences of efficient linear programs. Finally, we explore the purely classical version of the algorithm, in which the input state is a state with a classically efficient representation, and show that the method can prepare ground states of many-body Hamiltonians.
Geometric Learning of Hidden Markov Models via a Method of Moments Algorithm

We present a novel algorithm for learning the parameters of hidden Markov models (HMMs) in a geometric setting where the observations take values in Riemannian manifolds. In particular, we elevate a recent second-order method of moments algorithm that incorporates non-consecutive correlations to a more general setting where observations take place in a Riemannian symmetric space of non-positive curvature and the observation likelihoods are Riemannian Gaussians. The resulting algorithm decouples into a Riemannian Gaussian mixture model estimation algorithm followed by a sequence of convex optimization procedures. We demonstrate through examples that the learner can result in significantly improved speed and numerical accuracy compared to existing learners.
Crop Yield Prediction Using Naïve Bayes Algorithm

Agriculture is that the backbone of India and it plays important role in economy.It Is source of production for about 58 per cent of India’s population in step with government of the day’s estimates, States’s food production was about 291.95 MT in 2019-20; for 2020-21, the govt. had set the target up to 298.3 MT, which was two per cent more from the previous year’s output.Food production must double by 2050 to match the country’s population and income growth. the small and marginal farmers, therefore, have a big role within the country’s food security and meeting the SDG goals.Nearly 14 per cent of the population (189.2 million) continues to be undernourished in India, in line with State of Food Security and Nutrition within the planet, 2020 report. the planet Hunger Index 2020 placed India at the 94th position among 107 countries. Achieving ‘zero hunger’ by 2030 is also a humungous challenge, and needs an integrated and multi-dimensional approach for overall sustainable agriculture and food systems within the country.
Prediksi Harga Saham Indonesia pada Masa Covid-19 Menggunakan Regresi Pohon Keputusan

Stock price prediction is an interesting area of data mining. Many variables affect stock prices. Stock prices fluctuate, especially in the Covid-19 era which has an impact on the economy. This study aims to predict the stock price of Indonesian telecommunications, especially in the covid19 era. The object of this research is the financial statements of companies listed on the Indonesia Stock Exchange, where data related to stock prices of telecommunications companies are used as samples in the data processing of this study. In this study, the regression techniques that will be used are Multiple Linear Regression, Support Vector Regression, Decision Tree Regression, and K-Nearest Regression. The results showed that the fundamental data and stock prices have a relationship. High correlation coefficient resulted from Decision Tree Regression and K-Nearest Regression. Decision Tree Regression produces good results on the Train Test Split and KFold Cross Validation data, 2.99% and 2.98% repeatedly. It can conclude that d espite the fact that the pandemic scenario has had a significant impact on the stock market, the study's findings suggest that fundamental data and stock prices have a relationship. Decision Tree Regression and K-Nearest Regression both produced high correlation coefficients. Keywords: Stock Price, Prediction, Decision Tree, Regression, COVID-19
PVDD: A Practical Video Denoising Dataset with Real-World Dynamic Scenes

To facilitate video denoising research, we construct a compelling dataset, namely, "Practical Video Denoising Dataset" (PVDD), containing 200 noisy-clean dynamic video pairs in both sRGB and RAW format. Compared with existing datasets consisting of limited motion information, PVDD covers dynamic scenes with varying and natural motion. Different from datasets using primary Gaussian or Poisson distributions to synthesize noise in the sRGB domain, PVDD synthesizes realistic noise from the RAW domain with a physically meaningful sensor noise model followed by ISP processing. Moreover, based on this dataset, we propose a shuffle-based practical degradation model to enhance the performance of video denoising networks on real-world sRGB videos. Extensive experiments demonstrate that models trained on PVDD achieve superior denoising performance on many challenging real-world videos than on models trained on other existing datasets.
Deep Learning Models Based on Subword Tokenization for Vulnerability Detection of Source Code

The study of vulnerability detection in source code has been attracting attention in practice and academia because web applications can be vulnerable to attacks from the outside due to the open access characteristics. This study aims to build deep learning models and evaluate their performances for the field of source code vulnerability detection. The proposed deep learning models tackle class imbalance problem, long-term dependency problem, and out-of-vocabulary problem which are challenging problems in detecting source code vulnerabilities. As an experiment result, the precision of the subword tokenization-based one-dimensional convolution model showed 39%, which is about 20 times higher than the expected precision of 1.92% of the model predicted by chance. Although Conv1d+BT model using the BERT tokenizer showed the highest AUC value of 0.9116, the precision and recall of this model were 0.39 and 0.35, so it is judged that further improvement is needed for practical application.
Control of mathematical modeling process of dynamics of harmful substances concentrations on the basis of ontological approach

The problem of building a mathematical model of the dynamics of nitrogen dioxide concentrations at different parts of the city is considered in the paper. The peculiarities of the construction of such models on the basis of periodic measurement of concentrations of harmful substances and identification on the basis of the measurements obtained are considered. This paper also proposes an ontological approach as a control tool that greatly simplifies the systematic standardized methods of the models storage, the process of their construction and appropriate usage in practice. The use of the ontological model allows formalizing the process of obtaining, storing and using relevant knowledge and is suitable for more intelligent systems, such as identification of obviously erroneous solutions based on the model, predictive control of the model, optimization of the decision-making process based on knowledge and modeling of an appropriate technological flow chart. This paper also describes the features of the construction of the corresponding ontological model, the pattern of choice of a nonlinear model with "switching" to different conditions. Relevant experimental studies have also been conducted to confirm the effectiveness of the proposed approach.
Sentiment Analysis of Tourism Objects on Trip Advisor Using LSTM Method

This study developed a sentiment analysis application for comments on tourist sites. It is used to help people who want to know about tourist attractions information to get positive or negative information. The method used to analyze the sentiment was LSTM. The determination of LSTM architecture consists of scraping data, manual labelling, preprocessing (case folding, removing punctuation, removing stopwords, tokenization, and lemmatization), word2index, word embedding, and LSTM layer. In order to achieve optimal accuracy, it is necessary to determine the right embedded method, the total number of layers for the dropout layer, and LSTM. The performance of this study showed that the accuracy and loss from sentiment analysis using the LSTM method were 96.71% and 14.22%.
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition

Transferring knowledge from task-agnostic pre-trained deep models for downstream tasks is an important topic in computer vision research. Along with the growth of computational capacity, we now have open-source vision-language pre-trained models in large scales of the model architecture and amount of data. In this study, we focus on transferring knowledge for video classification tasks. Conventional methods randomly initialize the linear classifier head for vision classification, but they leave the usage of the text encoder for downstream visual recognition tasks undiscovered. In this paper, we revise the role of the linear classifier and replace the classifier with the different knowledge from pre-trained model. We utilize the well-pretrained language model to generate good semantic target for efficient transferring learning. The empirical study shows that our method improves both the performance and the training speed of video classification, with a negligible change in the model. Our simple yet effective tuning paradigm achieves state-of-the-art performance and efficient training on various video recognition scenarios, i.e., zero-shot, few-shot, general recognition. In particular, our paradigm achieves the state-of-the-art accuracy of 87.8% on Kinetics-400, and also surpasses previous methods by 20~50% absolute top-1 accuracy under zero-shot, few-shot settings on five popular video datasets. Code and models can be found at https://github.com/whwu95/Text4Vis .