Reviewerly/exHarmony huggingface dataset:
Data Augmentation Using Prosody and False Starts to Recognize Non-Native Children’s Speech

This paper describes AaltoASR's speech recognition system for the INTERSPEECH 2020 shared task on Automatic Speech Recognition (ASR) for non-native children's speech.The task is to recognize non-native speech from children of various age groups given a limited amount of speech.Moreover, the speech being spontaneous has false starts transcribed as partial words, which in the test transcriptions leads to unseen partial words.To cope with these two challenges, we investigate a data augmentation-based approach.Firstly, we apply the prosodybased data augmentation to supplement the audio data.Secondly, we simulate false starts by introducing partial-word noise in the language modeling corpora creating new words.Acoustic models trained on prosody-based augmented data outperform the models using the baseline recipe or the SpecAugment-based augmentation.The partial-word noise also helps to improve the baseline language model.Our ASR system, a combination of these schemes, is placed third in the evaluation period and achieves the word error rate of 18.71%.Post-evaluation period, we observe that increasing the amounts of prosody-based augmented data leads to better performance.Furthermore, removing low-confidence-score words from hypotheses can lead to further gains.These two improvements lower the ASR error rate to 17.99%.
Optical multiple-image compression-encryption via single-pixel Radon transform

Compression is a common operation in multiple-image encryption (MIE) schemes. For most of the existing MIE schemes, the compression process would lead to the waste of the detect data. In this paper, a novel MIE scheme based on the single-pixel Radon transform (RT) is proposed. In the encryption process, the single-pixel RT of each image from 0° to 180° is performed. This process can be realized by an optical setup. All Radon spectra are combined and encrypted by the asymmetric cryptosystem based on phase-truncated Fourier transforms (PTFT). To increase the security, the Arnold map and logistic map are used to scramble and diffuse the result of PTFT. Unlike conventional MIE schemes, there is no extra compression operation and data waste in the proposed method. The key sensitivity, the multiplexing capacity, and the security under various attacks are analyzed through simulation results. The optical experiment is also performed to demonstrate the feasibility of the proposed optical MIE scheme.
Nearly Minimax Optimal Reward-free Reinforcement Learning

We study the reward-free reinforcement learning framework, which is particularly suitable for batch reinforcement learning and scenarios where one needs policies for multiple reward functions. This framework has two phases. In the exploration phase, the agent collects trajectories by interacting with the environment without using any reward signal. In the planning phase, the agent needs to return a near-optimal policy for arbitrary reward functions. We give a new efficient algorithm, \textbf{S}taged \textbf{S}ampling + \textbf{T}runcated \textbf{P}lanning (\algoname), which interacts with the environment at most $O\left( \frac{S^2A}{ε^2}\text{poly}\log\left(\frac{SAH}ε\right) \right)$ episodes in the exploration phase, and guarantees to output a near-optimal policy for arbitrary reward functions in the planning phase. Here, $S$ is the size of state space, $A$ is the size of action space, $H$ is the planning horizon, and $ε$ is the target accuracy relative to the total reward. Notably, our sample complexity scales only \emph{logarithmically} with $H$, in contrast to all existing results which scale \emph{polynomially} with $H$. Furthermore, this bound matches the minimax lower bound $Ω\left(\frac{S^2A}{ε^2}\right)$ up to logarithmic factors. Our results rely on three new techniques : 1) A new sufficient condition for the dataset to plan for an $ε$-suboptimal policy; 2) A new way to plan efficiently under the proposed condition using soft-truncated planning; 3) Constructing extended MDP to maximize the truncated accumulative rewards efficiently.
Gauss–Jordan elimination-based image tampering detection and self-recovery

This paper proposes a novel Gauss–Jordan elimination-based image tampering detection and self-recovery scheme, aiming at dealing with the problem of malicious tampering on digital images. To deal with the copy–move tampering which is challenging because the tampered region may contain the watermark information, we propose the Improved Check Bits Generation algorithm during watermark generation, to generate the check bits for tampering detection. Meanwhile, the recovery bits are reconstructed according to the fundamental of Gauss–Jordan Elimination, for purpose of image contents self-recovery. To improve the accuracy of detection and the quality of recovered images, we propose the Morphological Processing-Based Enhancement method and the Edge Extension preprocessing respectively during and after the tampering detection Finally, the Gauss–JordanElimination-Based Self-Recovery method is proposed to recover the damaged content mathematically on basis of the detected results. By employing the unchanged recovery bits which are embedded in the non-tampered region, the failure in recovery caused by the damaged recovery bits can be completely avoided. A large number of experiments have been conducted to show the very good performance of the proposed scheme. The precision, recall, and F1 score are calculated for evaluation of tampering detection, while the PSNR values are calculated for evaluation of image recovery. The comparisons with the state-of-the-art methods show that the proposed scheme shows the superiorities in terms of imperceptibility, security and recovery capability. The experimental result indicates the average PSNR of recovered image is 44.415dB.
D2RL: Deep Dense Architectures in Reinforcement Learning

While improvements in deep learning architectures have played a crucial role in improving the state of supervised and unsupervised learning in computer vision and natural language processing, neural network architecture choices for reinforcement learning remain relatively under-explored. We take inspiration from successful architectural choices in computer vision and generative modeling, and investigate the use of deeper networks and dense connections for reinforcement learning on a variety of simulated robotic learning benchmark environments. Our findings reveal that current methods benefit significantly from dense connections and deeper networks, across a suite of manipulation and locomotion tasks, for both proprioceptive and image-based observations. We hope that our results can serve as a strong baseline and further motivate future research into neural network architectures for reinforcement learning. The project website is at this link https://sites.google.com/view/d2rl-anonymous/home
Is Retriever Merely an Approximator of Reader?

The state of the art in open-domain question answering (QA) relies on an efficient retriever that drastically reduces the search space for the expensive reader. A rather overlooked question in the community is the relationship between the retriever and the reader, and in particular, if the whole purpose of the retriever is just a fast approximation for the reader. Our empirical evidence indicates that the answer is no, and that the reader and the retriever are complementary to each other even in terms of accuracy only. We make a careful conjecture that the architectural constraint of the retriever, which has been originally intended for enabling approximate search, seems to also make the model more robust in large-scale search. We then propose to distill the reader into the retriever so that the retriever absorbs the strength of the reader while keeping its own benefit. Experimental results show that our method can enhance the document recall rate as well as the end-to-end QA accuracy of off-the-shelf retrievers in open-domain QA tasks.
Robust Multi-Linear Fuzzy SVR Designed With the Aid of Fuzzy C-Means Clustering Based on Insensitive Data Information

Multiple SVR based on ensemble learning could be enhanced from the viewpoint of the performance, but the performance of modeling closely depends on the initial condition of the partitioning method and they are easily affected by noise and outliers. In this study, a multi-linear fuzzy support vector regression (MFSVR) robust to noise is proposed with the aid of the composite kernel function and $\varepsilon $ -fuzzy c-means (FCM) clustering based on insensitive data information. Here insensitive data information stands for the interval data information of “$\varepsilon $ ” which stands for insensitive loss parameter used in the $\varepsilon $ - insensitive loss function. The objective of this study is to reduce the effect of noise and to alleviate the overfitting problem through the synergistic effect of the following methods: First, $\varepsilon $ -FCM clustering based on insensitive data information is used for considering more impact on decision boundary and reducing the effect of noise. Second, the composite kernel based on multiple linear kernel expression is proposed for implementing multi-linear decision boundary to alleviate overfitting problem. In more detail, each training data point is assigned with corresponding membership degrees in the $\varepsilon $ -FCM clustering. Some data which are potentially to be noise or outlier are assigned with lower membership degrees and given small contribution (compensation) considered in composite kernel function. Then, the composite kernel function for multiple local SVRs is constructed according to the distribution characteristics of $\varepsilon $ -FCM clustering. The proposed MFSVR is tested with both synthetic and UCI data sets in order to verify the effectiveness as well as efficient performance improvement. Experimental results demonstrate that the proposed method shows the better performance when compared to other some methods studied so far.
Under-Sampling and Feature Selection Algorithms for S2SMLP

Imbalance learning is a hot topic in the data mining and machine learning domains. Data-level, algorithm-level and ensemble solutions are the three main methods proposed thus far to address imbalance learning. To alleviate the issues of data explosion and feature selection in multilayer perceptron based on simultaneous two-sample representation(S2SMLP), in this paper, firstly, spectral clustering is exploited to select majority samples so as to construct a smaller training dataset for the classifier. We divide all majority samples into many clusters through spectral clustering, extract different numbers of representative samples from a cluster according to the size of each cluster, the average distance between the minority class and all samples of the cluster, then construct the training dataset of the classifier by combining these extracted samples from the majority class and all minority samples. Secondly, we propose a novel feature selection method based on the pairwise samples distance constraint, which considers the class labels of paired samples, select the features which push two similar samples closer together and pull two different samples farther apart. Finally, we conduct extensive experiments on 44 two-class imbalanced datasets and four high-dimensional DNA microarray datasets. The experimental results demonstrate that our proposed algorithms outperform some state-of-the-art algorithms in terms of $F\textrm {-measure},G\textrm {-mean}$ and AUC.
On Explaining Decision Trees

Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models. This is informally motivated by paths in DTs being often much smaller than the total number of features. This paper shows that in some settings DTs can hardly be deemed interpretable, with paths in a DT being arbitrarily larger than a PI-explanation, i.e. a subset-minimal set of feature values that entails the prediction. As a result, the paper proposes a novel model for computing PI-explanations of DTs, which enables computing one PI-explanation in polynomial time. Moreover, it is shown that enumeration of PI-explanations can be reduced to the enumeration of minimal hitting sets. Experimental results were obtained on a wide range of publicly available datasets with well-known DT-learning tools, and confirm that in most cases DTs have paths that are proper supersets of PI-explanations.
Treatment of produced water from Niger Delta oil fields using simultaneous mixture of local materials

Abstract Produced water (PW) from petroleum reservoirs often contains heavy metals and other contaminants that are harmful to the environment. Most of the commonly used treatment techniques have been reported to be ineffective in reducing some of the contaminants’ concentrations to recommended disposal levels. This study evaluated the effectiveness of four selected bio-adsorbents combined for treating PW from Niger Delta oil fields. In this study, orange peels (I), banana peels (II), sponge gourd ( Luffa cylindrica ) (III) and palm kernel fibers (IV) were washed with distilled water, sun-dried (24 h) and dried in the oven at 105 ± 5 °C (3 h, I and II), 150 °C (30 min, III) and 80 °C (3 h, IV). They were ground into powder, sieved (150 μ, Group A) and (300 μ, Group B), washed with 0.4 mol/L HNO 3 , filtered and rinsed with distilled water. Samples of PW were obtained from fields R, X, and Y in the Niger Delta and analysed for heavy metals using an atomic absorption spectrophotometer (AAS). Samples were treated in adsorption column over 6 h using the adsorbents simultaneously. Treated samples were analysed with AAS and characterised. Adsorption of heavy metals were assessed using Langmuir and Freundlich models. Data were analysed using regression and other statistical methods. For the 150 μ size of sample R, the percentage reductions for the metal concentrations (Pb, Ni, Cd, Cu, Fe, Mg, Cr, Zn, Mn, Ca, Ar, B, Sn and Ba) were found to be 100%, 52.7%, 100%, 100%, 85.87%, 19.48%, 100%, 92.8%, 17.74%, 98.86%, 22.32%, 29.56%, 78.06% and 44.74%, respectively, while the reduction in 300 μ size were 1.52%, 97.2%, 71.4%, 17.1%, 43.8%, 45.6%, 7.04%, 89.6%, 35.4%, 99.6%, 0.0001%, 1.19%, 14.19% and 0.002%, respectively. The finer adsorbents were more effective. Similar results were obtained for PW samples from the other fields. Produced water from Niger Delta oil fields was effectively treated of contaminants using four selected bio-adsorbents mixed simultaneously.
An Automatic Mass Screening System for Cervical Cancer Detection Based on Convolutional Neural Network

Cervical cancer is the fourth most common type of cancer and is also a leading cause of mortality among women across the world. Various types of screening tests are used for its diagnosis, but the most popular one is the Papanicolaou smear test, in which cell cytology is carried out. It is a reliable tool for early identification of cervical cancer, but there is always a chance of misdiagnosis because of possible errors in human observations. In this paper, an auto-assisted cervical cancer screening system is proposed that uses a convolutional neural network trained on Cervical Cells database. The training of the network is accomplished through transfer learning, whereby initializing weights are obtained from the training on ImageNet dataset. After fine-tuning the network on the Cervical Cells database, the feature vector is extracted from the last fully connected layer of convolutional neural network. For final classification/screening of the cell samples, three different classifiers are proposed including Softmax regression (SR), Support vector machine (SVM), and GentleBoost ensemble of decision trees (GEDT). The performance of the proposed screening system is evaluated for two different testing protocols, namely, 2-class problem and 7-class problem, on the Herlev database. Classification accuracies of SR, SVM, and GEDT for the 2-class problem are found to be 98.8%, 99.5%, and 99.6%, respectively, while for the 7-class problem, they are 97.21%, 98.12%, and 98.85%, respectively. These results show that the proposed system provides better performance than its previous counterparts under various testing conditions.
Exploring DeshuffleGANs in Self-Supervised Generative Adversarial Networks

Generative Adversarial Networks (GANs) have become the most used networks towards solving the problem of image generation. Self-supervised GANs are later proposed to avoid the catastrophic forgetting of the discriminator and to improve the image generation quality without needing the class labels. However, the generalizability of the self-supervision tasks on different GAN architectures is not studied before. To that end, we extensively analyze the contribution of a previously proposed self-supervision task, deshuffling of the DeshuffleGANs in the generalizability context. We assign the deshuffling task to two different GAN discriminators and study the effects of the task on both architectures. We extend the evaluations compared to the previously proposed DeshuffleGANs on various datasets. We show that the DeshuffleGAN obtains the best FID results for several datasets compared to the other self-supervised GANs. Furthermore, we compare the deshuffling with the rotation prediction that is firstly deployed to the GAN training and demonstrate that its contribution exceeds the rotation prediction. We design the conditional DeshuffleGAN called cDeshuffleGAN to evaluate the quality of the learnt representations. Lastly, we show the contribution of the self-supervision tasks to the GAN training on the loss landscape and present that the effects of these tasks may not be cooperative to the adversarial training in some settings. Our code can be found at https://github.com/gulcinbaykal/DeshuffleGAN.
A Fast Encryption Scheme Suitable for Video Surveillance Applications Using SHA-256 Hash Function and 1D Sine–Sine Chaotic Map

This paper proposes an image encryption technique which is fast and secure. The encryption scheme is designed for secure transmission of video surveillance data (keyframes) over insecure network. The image encryption technique employs 1D Sine–Sine system with better chaotic properties than its seed map and faster than higher-dimensional chaotic systems. Further, design of encryption scheme is based on two permutation rounds, which employs pixel swapping operation and diffusion operation which is simple and provides required security against plaintext, differential and various other attacks. Three separate chaotic sequences are generated using 1D Sine–Sine system which enhances the key space of the encryption scheme. Secret keys are updated dynamically with SHA-256 hash value obtained from plain image. Hash values of plain image are efficiently used without loss of any hash value information. This makes the encryption scheme plaintext sensitive and secure against plaintext attacks. Performance and security aspects of encryption scheme is analyzed both quantitatively using predefined security metrics and qualitatively by scrutinizing the internal working of encryption scheme. Computational complexity of encrypting a plain image of size [Formula: see text] is [Formula: see text] and is suitable for encrypting keyframes of video for secure surveillance applications.
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation algorithms continually increase in the research community. In the light of this challenge, we propose a unified, comprehensive and efficient recommender system library called RecBole, which provides a unified framework to develop and reproduce recommendation algorithms for research purpose. In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. We implement the RecBole library based on PyTorch, which is one of the most popular deep learning frameworks. Our library is featured in many aspects, including general and extensible data structures, comprehensive benchmark models and datasets, efficient GPU-accelerated execution, and extensive and standard evaluation protocols. We provide a series of auxiliary functions, tools, and scripts to facilitate the use of this library, such as automatic parameter tuning and break-point resume. Such a framework is useful to standardize the implementation and evaluation of recommender systems. The project and documents are released at https://recbole.io/.
StatAid: An R package with a graphical user interface for data analysis

Data analysis is a crucial step in every research project in life science.Every clinician or researcher is one day faced with the need to perform statistical analysis.However, few free accessible solutions exist to date and most of the relevant software programs need a paid license.R is a free language which allows one to perform statistical analysis (R Core Team, 2017).While the R environment is very powerful, its learning curve can be very steep in the beginning, especially for people with no previous coding skills or those with less time to learn them.A graphical user interface has already been provided as an independent package, but its features are limited for medical and applied life science studies and its usage remains difficult and unintuitive for new users (Fox, 2005).Other free software programs exist, such as iNZight or Jamovi.However, while providing solutions with multiple features such as variable recoding, they do not guide the user through the analysis and can lack some key features such as time-dependent outcome analysis.
A 2.1 KHz Zero-Knowledge Processor with BubbleRAM

Zero-Knowledge (ZK) proofs (ZKP) are foundational in cryptography. Most recent ZK research focuses on non-interactive proofs (NIZK) of small statements, useful in blockchain scenarios. Another line, and our focus, instead targets proofs of large statements that are useful, e.g., in proving properties of programs in ZK. We specify a zero-knowledge processor that executes arbitrary programs written in a simple instruction set, and proves in ZK the correctness of the execution. Such an approach is well-suited for constructing ZK proofs of large statements as it efficiently supports complex programming constructs, such as loops and RAM access. Critically, we propose several novel ZK improvements that make our approach concretely efficient: (1) an efficient arithmetic representation with conversions to/from Boolean, (2) an efficient read-only memory that uses $2łog n$ OTs per access, and (3) an efficient read-write memory, øurram, which uses $\frac1 2 łog^2 n$ OTs per access. øurram beats linear scan for RAM of size $>3$ elements! Prior ZK systems used generic ORAM costing orders of magnitude more. We cast our system as a garbling scheme that can be plugged into the ZK protocol of [Jawurek et al, CCS'13]. Put together, our system is concretely efficient: for a processor instantiated with $512$KB of main memory, each processor cycle costs $24$KB of communication. We implemented our approach in \textttC++. On a 1Gbps LAN our implementation realizes a $2.1$KHz processor.