Reviewerly/exHarmony huggingface dataset:
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

Abstract While significant research advances have been made in the field of deep reinforcement learning, there have been no concreteadversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms tomembership inference attacks. In such attacking systems, the adversary targets the set of collected input data on which thedeep reinforcement learning algorithm has been trained. To address this gap, we propose an adversarial attack frameworkdesigned for testing the vulnerability of a state-of-the-art deep reinforcement learning algorithm to a membership inferenceattack. In particular, we design a series of experiments to investigate the impact of temporal correlation, which naturally existsin reinforcement learning training data, on the probability of information leakage. Moreover, we compare the performance ofcollective and individual membership attacks against the deep reinforcement learning algorithm. Experimental results showthat the proposed adversarial attack framework is surprisingly effective at inferring data with an accuracy exceeding 84% inindividual and 97% in collective modes in three different continuous control Mujoco tasks, which raises serious privacy concernsin this regard. Finally, we show that the learning state of the reinforcement learning algorithm influences the level of privacybreaches significantly.
Streaming Punctuation for Long-Form Dictation with Transformers

While speech recognition Word Error Rate (WER) has reached human parity for English, longform dictation scenarios still suffer from segmentation and punctuation problems resulting from irregular pausing patterns or slow speakers. Transformer sequence tagging models are effective at capturing long bi-directional context, which is crucial for automatic punctuation. Automatic Speech Recognition (ASR) production systems, however, are constrained by real-time requirements, making it hard to incorporate the right context when making punctuation decisions. In this paper, we propose a streaming approach for punctuation or re-punctuation of ASR output using dynamic decoding windows and measure its impact on punctuation and segmentation accuracy across scenarios. The new system tackles over-segmentation issues, improving segmentation F0.5-score by 13.9%. Streaming punctuation achieves an average BLEU-score improvement of 0.66 for the downstream task of Machine Translation (MT).
Improving Robustness of Age and Gender Prediction based on Custom Speech Data

With the increased use of human-machine interaction via voice enabled smart devices over the years, there are growing demands for better accuracy of the speech analytics systems. Several studies show that speech analytics system exhibits bias towards speaker demographics, such age, gender, race, accent etc. To avoid such a bias, speaker demographic information can be used to prepare training dataset for the speech analytics model. Also, speaker demographic information can be used for targeted advertisement, recommendation, and forensic science. In this research we will demonstrate some algorithms for age and gender prediction from speech data with our custom dataset that covers speakers from around the world with varying accents. In order to extract speaker age and gender from speech data, we’ve also included a method for determining the appropriate length of audio file to be ingested into the system, which will reduce computational time. This study also identifies the most effective padding and cropping mechanism for obtaining the best results from the input audio file. We investigated the impact of various parameters on the performance and end-to-end implementation of a real-time speaker age and gender information extraction system. Our best model has a RMSE value of 4.1 for age prediction and 99.5% for gender prediction on custom test dataset.
Layer-Wise Relevance Propagation for Echo State Networks Applied to Earth System Variability

Artificial neural networks (ANNs) are powerful methods for many hard problems (e.g. image classification or time series prediction). However, these models are often difficult to interpret. Layer-wise relevance propagation (LRP) is a widely used technique to understand how ANN models come to their conclusion and to understand what a model has learned. Here, we focus on Echo State Networks (ESNs) as a certain type of recurrent neural networks. ESNs are easy to train and only require a small number of trainable parameters. We show how LRP can be applied to ESNs to open the black-box. We also show an efficient way of how ESNs can be used for image classification: Our ESN model serves as a detector for El Niño Southern Oscillation (ENSO) from sea surface temperature anomalies. ENSO is a well-known problem. Here, we use this problem to demonstrate how LRP can significantly enhance the explainablility of ESNs.
Self-Supervised Action Representation Learning Based on Asymmetric Skeleton Data Augmentation

Contrastive learning has received increasing attention in the field of skeleton-based action representations in recent years. Most contrastive learning methods use simple augmentation strategies to construct pairs of positive samples. When using such pairs of positive samples to learn action representations, deeper feature information cannot be learned, thus affecting the performance of downstream tasks. To solve the problem of insufficient learning ability, we propose an asymmetric data augmentation strategy and attempt to apply it to the training of 3D skeleton-based action representations. First, we carefully study the different characteristics presented by different skeleton views and choose a specific augmentation method for a certain view. Second, specific augmentation methods are incorporated into the left and right branches of the asymmetric data augmentation pipeline to increase the convergence difficulty of the contrastive learning task, thereby significantly improving the quality of the learned action representations. Finally, since many methods directly act on the joint view, the augmented samples are quite different from the original samples. We use random probability activation to transform the joint view to avoid extreme augmentation of the joint view. Extensive experiments on NTU RGB + D datasets show that our method is effective.
Robust Quantum Feature Selection Algorithm

&lt;p&gt;High dimensional data has been a notoriously challenging issue. Existing quantum dimension reduction technology mainly focuses on quantum principal component analysis. There are only a few works on the direction of quantum feature selection algorithm which they are not robust. Also, there are few quantum circuits designed for feature selection, in which some steps are not quantized yet. For example, existing quantum circuits cannot solve the objective function based on sparse learning. To deal with these issues, this paper proposes a robust quantum feature selection by designing a new quantum circuit. Specifically, the sparse regularization term and least squares loss are first applied to construct the proposed objective function. And then, six kinds of quantum registers and their initial states are prepared. In addition, quantum techniques, such as quantum phase estimation and controlled rotation, are employed to construct an alternating iterative quantum circuit to obtain the final quantum state of the feature selection variable. Finally, a series of experiments are conducted to verify that the proposed algorithm can accurately select important features and has good robustness. &lt;br&gt; &lt;/p&gt;
Questions Answered: Rob Mastrodomenico

Abstract The football-crazy sports statistician gained his PhD in statistics from the University of Reading, and is the founder of Alchemax Analytics as well as the vice chair of the Royal Statistical Society's sports section
Robust Deep Ensemble Method for Real-world Image Denoising

Recently, deep learning-based image denoising methods have achieved promising performance on test data with the same distribution as training set, where various denoising models based on synthetic or collected real-world training data have been learned. However, when handling real-world noisy images, the denoising performance is still limited. In this paper, we propose a simple yet effective Bayesian deep ensemble (BDE) method for real-world image denoising, where several representative deep denoisers pre-trained with various training data settings can be fused to improve robustness. The foundation of BDE is that real-world image noises are highly signal-dependent, and heterogeneous noises in a real-world noisy image can be separately handled by different denoisers. In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps to fuse these denoisers. Instead of solely learning pixel-wise weighting maps, Bayesian deep learning strategy is introduced to predict weighting uncertainty as well as weighting map, by which prediction variance can be modeled for improving robustness on real-world noisy images. Extensive experiments have shown that real-world noises can be better removed by fusing existing denoisers instead of training a big denoiser with expensive cost. On DND dataset, our BDE achieves +0.28~dB PSNR gain over the state-of-the-art denoising method. Moreover, we note that our BDE denoiser based on different Gaussian noise levels outperforms state-of-the-art CBDNet when applying to real-world noisy images. Furthermore, our BDE can be extended to other image restoration tasks, and achieves +0.30dB, +0.18dB and +0.12dB PSNR gains on benchmark datasets for image deblurring, image deraining and single image super-resolution, respectively.
Option Transfer and SMDP Abstraction with Successor Features

Abstraction plays an important role in the generalisation of knowledge and skills and is key to sample efficient learning. In this work, we study joint temporal and state abstraction in reinforcement learning, where temporally-extended actions in the form of options induce temporal abstractions, while aggregation of similar states with respect to abstract options induces state abstractions. Many existing abstraction schemes ignore the interplay of state and temporal abstraction. Consequently, the considered option policies often cannot be directly transferred to new environments due to changes in the state space and transition dynamics. To address this issue, we propose a novel abstraction scheme building on successor features. This includes an algorithm for transferring abstract options across different environments and a state abstraction mechanism that allows us to perform efficient planning with the transferred options.
Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images

Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. However, generating exhaustive and accurate annotations is labor-intensive, challenging, and costly. Drawing only coarse and approximate annotations is a much easier task, less costly, and it alleviates pathologists' workload. In this paper, we study the problem of refining these approximate annotations in digital pathology to obtain more accurate ones. Some previous works have explored obtaining machine learning models from these inaccurate annotations, but few of them tackle the refinement problem where the mislabeled regions should be explicitly identified and corrected, and all of them require a -- often very large -- number of training samples. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need of external training data. Patches cropped from a WSI with inaccurate labels are processed jointly within a multiple instance learning framework, mitigating their impact on the predictive model and refining the segmentation. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming state-of-the-art alternatives, even while learning from a single slide. Moreover, we demonstrate how real annotations drawn by pathologists can be efficiently refined and improved by the proposed approach. All these results demonstrate that LC-MIL is a promising, light-weight tool to provide fine-grained annotations from coarsely annotated pathology sets.
Effectiveness of Peer Mentoring Program on Academic Performance of Grade VII Bhutanese Students

The study explored the effectiveness of peer mentoring program on academic performance of class VII Bhutanese students. It employed mixed method approach with purposive sampling method. A total of 62 students from grade VII participated in the study. The study found out the mean of the differences (pre-test and post-test) was 5.14 (SD = 3.42) and the paired-samples t-test indicated that this mean of the differences is significantly greater than zero (t = 8.36, p = .00, 95% CI[6.39, 3.88]). This indicated that the peer mentoring program had positive impact on academic performance of students. The peer mentoring program also helped students to enhance their communication skills. However, some mentor students mentioned that it was quite hectic when mentees do not cooperate with them.&#x0D; It is recommended that peer mentoring program should be carried out in schools to improve students’ academic performance as well as their communication skills. Further, the present study focused on only some students of grade VII, a study may be carried out across schools to get a deep understanding of the subject.
CLASSIFYING WEATHER BASED VISUAL DATA USING NOVEL HYBRID APPROACH

Aspects such as the climate play a significant part in the expansion of businesses in the current period, in which companies from a variety of sectors are collaborating to fulfil the objectives of commercial enterprises. The climate not only has an impact on our day-to-day lives, but it also plays a significant role in a wide range of businesses, including retail, construction, and supply chain management, among others. Aside from that, it also has an effect on the functionality of a great deal of visual systems, such as those used in vehicles to assist drivers, outdoor video observation system, and so on. The presence of fog in the weather poses a significant risk for automobile accidents. The impact of the severe weather that has been affecting the region can also be observed in India. Although there is a significant amount of study that is now being conducted in the topic of "Weather Analytics," there is still a significant amount of research that needs to follow the role of A.I. in this particular field. This research work presents a specific study that has been done to fill the void. The aim of this research is to investigate potential applications of combining various deep learning models known as convolutional neural networks for solving weather detection issues. Expensive sensors are required for the more conventional methods of determining the current state of the weather. This article provides a strategy using computer vision to detect real weather conditions with precise accuracy in order to keep the costs to a minimum. KEYWORDS—Computer Vision, Weather Classification, Image processing, Deep Learning, Ensemble Learning, Accuracy
FIRST YEAR STUDENT ATTITUDES TOWARD DISTANCE LEARNING

Over the last two years, since the beginning of the COVID-19 pandemic, Higher Education Institutions (HEIs) in Serbia worked in the modality of distance learning – instructions and students` assignments were performed by distance, while colloquia and exams were realized in traditional conditions. Regardless of modality, HEIs have to ensure high-quality education in every single course. In this sense, the feedback from students is very important. The purpose of this study is to investigate students` attitudes about four dimensions of distance learning – fulfilment of preconditions, organization and realization of instructions, advantages/disadvantages of distance learning, communication and social interactions. Data was collected from 183 first-year students within three courses at The Academy of Applied Technical Studies Belgrade, and was processed in the SPSS software package. The research strategy included descriptive statistics, while the Likert scale was used to assess the satisfaction of the respondents. Analysis of variance and independent-sample t test were used to examine differences in opinions among different groups of respondents. The results of this study could be important for instructors, HEIs that operate in similar conditions, policymakers in the field of HE in Serbia, as well as for present and future research in this area.
Formulation of the Alpha Sliding Innovation Filter: A Robust Linear Estimation Strategy

In this paper, a new filter referred to as the alpha sliding innovation filter (ASIF) is presented. The sliding innovation filter (SIF) is a newly developed estimation strategy that uses innovation or measurement error as a switching hyperplane. It is a sub-optimal filter that provides a robust and stable estimate. In this paper, the SIF is reformulated by including a forgetting factor, which significantly improves estimation performance. The proposed ASIF is applied to several systems including a first-order thermometer, a second-order spring-mass-damper, and a third-order electrohydrostatic actuator (EHA) that was built for experimentation. The proposed ASIF provides an improvement in estimation accuracy while maintaining robustness to modeling uncertainties and disturbances.
A Review of Blockchain-based Internet of Things

The use of smart network applications based on the Internet of Things is increasing, which increases the attractiveness of malicious activities, leading to the need to increase the adequate security of these networks. In this paper, the latest recent breakthroughs in blockchain for the Internet of Things are examined in the context of electronic health (e-health), smart cities, smart transportation, and other applications in this article. Research gaps and possible solutions are discussed, such as security, connection, transparency, privacy, and the IoT’s blockchain regulatory challenges. In addition, the most important consensus algorithms used in the blockchain have been discussed, including Proof of Work, Proof of Stake, and Proof of Authority, each of which operates within certain rules.
The Power of Language Learning Beyond the Classroom to English Mastery of an EFL Learner

Language learners have come to an era in which studying outside the classroom is well-facilitated by technology development. Along with this advancement, there is a wider chance to learn things outside the classroom, including language. In the Indonesian context, many students have done wider exploration to improve their English exposure through various activities. This study aims to reveal the learning strategies done by a successful EFL learner through case study research. The result shows that online games, e-books, and movies are some of the prominent learning tools of Language Learning Beyond the Classroom (LLBC) to enhance language exposure and learners’ autonomy.
DEVELOPMENT OF A WEBSITE FOR AGRICULTURAL MARKETING

India places a high importance on agriculture, but today's farmers are of a lower social class and deal with a lot of difficulties in their daily lives as a result of extreme poverty. Although the employers in this artwork represent 50% of our working population, agriculture only contributes to roughly 15% of India's GDP (properly domestic Product). One of the main factors contributing to farm suicide in India is the income era. Farm poverty is caused by a lack of understanding of the modern generation or new techniques. Even after all the laborious effort and output produced with the help of the farmers, the farmers are still pressured by the sellers in the modern marketplace, which causes poverty. To create direct income between farmers and consumers, it is essential to reduce middlemen's roles in marketing farm products. By using direct farmer-to-patron and language communication with farmers, this website enables farmers to ensure the most profitability. Farmers would be able to understand how entrepreneurs are deceiving them about the quality of their products with the help of this program. The farmer's need is made apparent by this utility. Farmers can learn all there is to know about eliminating inflation and black marketing by using this service. It enables accurate data and information remodeling. The device's clearly defined interfaces allowed one to easily explore the vast amount of information. Higher communication between the farmer and the consumer is made possible by this optimization website online
The Effect of Corporate Income Tax on FDI Inflow in the Mining Sector in Tanzania

Purpose: Tanzania is one of the mineral-rich countries giving corporate income tax (CIT) concessions to encourage foreign direct investment (FDI). Tanzania's mining industry remains enticing to investors, owing to its expanding diversity. This paper assesses the impact of corporate income tax in the mining sector for the period of 1990 – 2020. The paper also sought to analyse the impact of other determinants of FDI namely, Political stability, Human capital and infrastructure on FDI inflow in the mining sector.&#x0D; Methodology: The study utilized secondary annual time series data collected from the World Bank (WB), Bank of Tanzania (BOT), and Foundation for Studies and Research on International Development (FERDI). The Autoregressive Distribution of Lags (Ardl) estimating approach was used to analyse the short-run and long-run relationship between FDI and corporate income tax as well as the aforementioned variables.&#x0D; Findings: A negative relationship between corporate income Tax and FDI inflow in the mining sector was confirmed. Thus, portraying both short-run and long-run effects of corporate income tax on FDI inflow in the mining sector.&#x0D; Unique Contribution to Theory, Practice and Policy: To encourage and increase FDI inflow in the mining sector, the government of Tanzania requires concerted efforts with relevant authorities that will ensure a fair moderation of corporate income Tax which will in turn increase tax compliance. The concerted efforts carter for factors linked with FDI inflow such as corporate income tax, political stability, human capital quality, and infrastructure. These factors align concurrently with the eclectic paradigm which is the anchoring theory of the study.&#x0D; &#x0D;