Reviewerly/exHarmony huggingface dataset:
Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations

Reinforcement Learning (RL) has achieved significant success in application domains such as robotics, games and health care. However, training RL agents is very time consuming. Current implementations exhibit poor performance due to challenges such as irregular memory accesses and thread-level synchronization overheads on CPU. In this work, we propose a framework for generating scalable reinforcement learning implementations on multi-core systems. Replay Buffer is a key component of RL algorithms which facilitates storage of samples obtained from environmental interactions and data sampling for the learning process. We define a new data structure for Prioritized Replay Buffer based on $K$-ary sum tree that supports asynchronous parallel insertions, sampling, and priority updates. To address the challenge of irregular memory accesses, we propose a novel data layout to store the nodes of the sum tree that reduces the number of cache misses. Additionally, we propose $\textit{lazy writing}$ mechanism to reduce thread-level synchronization overheads of the Replay Buffer operations. Our framework employs parallel actors to concurrently collect data via environmental interactions, and parallel learners to perform stochastic gradient descent using the collected data. Our framework supports a wide range of reinforcement learning algorithms including DQN, DDPG, etc. We demonstrate the effectiveness of our framework in accelerating RL algorithms by performing experiments on CPU + GPU platform using OpenAI benchmarks.
Optimization-based Causal Estimation from Heterogenous Environments

This paper presents a new optimization approach to causal estimation. Given data that contains covariates and an outcome, which covariates are causes of the outcome, and what is the strength of the causality? In classical machine learning (ML), the goal of optimization is to maximize predictive accuracy. However, some covariates might exhibit a non-causal association to the outcome. Such spurious associations provide predictive power for classical ML, but they prevent us from causally interpreting the result. This paper proposes CoCo, an optimization algorithm that bridges the gap between pure prediction and causal inference. CoCo leverages the recently-proposed idea of environments, datasets of covariates/response where the causal relationships remain invariant but where the distribution of the covariates changes from environment to environment. Given datasets from multiple environments -- and ones that exhibit sufficient heterogeneity -- CoCo maximizes an objective for which the only solution is the causal solution. We describe the theoretical foundations of this approach and demonstrate its effectiveness on simulated and real datasets. Compared to classical ML and existing methods, CoCo provides more accurate estimates of the causal model.
Towards Book Cover Design via Layout Graphs

Book covers are intentionally designed and provide an introduction to a book. However, they typically require professional skills to design and produce the cover images. Thus, we propose a generative neural network that can produce book covers based on an easy-to-use layout graph. The layout graph contains objects such as text, natural scene objects, and solid color spaces. This layout graph is embedded using a graph convolutional neural network and then used with a mask proposal generator and a bounding-box generator and filled using an object proposal generator. Next, the objects are compiled into a single image and the entire network is trained using a combination of adversarial training, perceptual training, and reconstruction. Finally, a Style Retention Network (SRNet) is used to transfer the learned font style onto the desired text. Using the proposed method allows for easily controlled and unique book covers.
Who Was that Masked Voter? The Tally Won’t Tell!

We consider elections that publish anonymised voted ballots or anonymised cast-vote records for transparency or verification purposes, investigating the implications for privacy, coercion, and vote selling and exploring how partially masking the ballots can alleviate these issues.Risk Limiting Tallies (RLT), which reveal only a random sample of ballots, were previously proposed to mitigate some coercion threats. Masking some ballots provides coerced voters with plausible deniability, while risk-limiting techniques ensure that the required confidence level in the election result is achieved. Risk-Limiting Verification (RLV) extended this approach to masking a random subset of receipts or trackers.Here we show how these ideas can be generalised and made more flexible and effective by masking at a finer level of granularity: at the level of the components of ballots. In particular, we consider elections involving complex ballots, where RLT may be vulnerable to pattern-based vote buying. We propose various measures of verifiability and coercion-resistance and investigate how several sampling/masking strategies perform against these measures. Using methods from coding theory, we analyse signature attacks, bounding the number of voters who can be coerced. We also define new quantitative measures for the level of coercion-resistance without plausible deniability and the level of vote-buying-resistance without “free lunch” vote sellers.These results and the different strategies for masking ballots are of general interest for elections that publish ballots for auditing, verification, or transparency purposes.
Efficient Attribute-based Proxy Re-Encryption with Constant Size Ciphertexts

Attribute-based proxy re-encryption (ABPRE) allows a semi-trusted proxy to transform an encryption under an access-policy into an encryption under a new access policy, without revealing any information about the underlying message. Such a primitive facilitates fine-grained secure sharing of encrypted data in the cloud. In its key-policy flavor, the re-encryption key is associated with an access structure that specifies which type of ciphertexts can be re-encrypted. Only two attempts have been made towards realising key-policy ABPRE (KP-ABPRE), one satisfying replayable chosen ciphertext security (RCCA security) and the other claiming to be chosen ciphertext secure (CCA secure). We show that both the systems are vulnerable to RCCA and CCA attacks respectively. We further propose a selective CCA secure KP-ABPRE scheme in this work. Since we demonstrate attacks on the only two existing schemes in the literature, our scheme becomes the first KP-ABPRE scheme satisfying selective CCA security. Moreover, our scheme has an additional attractive property, namely collusion resistance. A proxy re-encryption scheme typically consists of three parties: a delegator who delegates his decryption rights, a proxy who performs re-encryption, and a delegatee to whom the decryption power is delegated to. When a delegator wishes to share his data with a delegatee satisfying an access-policy, the proxy can collude with the malicious delegatee to attempt to obtain the private keys of the delegator during delegation period. If the private keys are exposed, security of the delegator’s data is completely compromised. Hence, achieving collusion resistance is indispensable to real-world applications. In this paper, we show that our construction satisfies collusion resistance. Our scheme is proven collusion resistant and selective CCA secure in the random oracle model, based on Bilinear Diffie-Hellman exponent assumption.
DenDrift: A Drift-Aware Algorithm for Host Profiling.

Detecting and reacting to unauthorized actions is an essential task in security monitoring. What make this task challenging are the large number and various categories of hosts and processes to monitor. To these we should add the lack of an exact definition of normal behavior for each category. Host profiling using stream clustering algorithms is an effective means of analyzing hosts' behaviors, categorizing them, and identifying atypical ones. However, unforeseen changes in behavioral data (i.e. concept drift) make the obtained profiles unreliable. DenStream is a well-known stream clustering algorithm, which can be effectively used for host profiling. This algorithm is an incremental extension of DBSCAN which is a non-parametric algorithm widely used in real-world clustering applications. Recent experimental studies indicate that DenStream is not robust against concept drift. In this paper, we present DenDrift as a drift-aware host profiling algorithm based on DenStream. DenDrift relies on non-negative matrix factorization for dimensionality reduction and Page-Hinckley test for drift detection. We have done experiments on both synthetic and industrial datasets and the results affirm the robustness of DenDrift against abrupt, gradual and incremental drifts.
IntentVizor: Towards Generic Query Guided Interactive Video Summarization Using Slow-Fast Graph Convolutional Networks

The target of automatic Video summarization is to create a short skim of the original long video while preserving the major content/events. There is a growing interest in the integration of user's queries into video summarization, or query-driven video summarization. This video summarization method predicts a concise synopsis of the original video based on the user query, which is commonly represented by the input text. However, two inherent problems exist in this query-driven way. First, the query text might not be enough to describe the exact and diverse needs of the user. Second, the user cannot edit once the summaries are produced, limiting this summarization technique's practical value. We assume the needs of the user should be subtle and need to be adjusted interactively. To solve these two problems, we propose a novel IntentVizor framework, which is an interactive video summarization framework guided by genric multi-modality queries. The input query that describes the user's needs is not limited to text but also the video snippets. We further conclude these multi-modality finer-grained queries as user `intent', which is a newly proposed concept in this paper. This intent is interpretable, interactable, and better quantifies/describes the user's needs. To be more specific, We use a set of intents to represent the inputs of users to design our new interactive visual analytic interface. Users can interactively control and adjust these mixed-initiative intents to obtain a more satisfying summary of this newly proposed interface. Also, as algorithms help users achieve their summarization goal via video understanding, we propose two novel intent/scoring networks based on the slow-fast feature for our algorithm part. We conduct our experiments on two benchmark datasets. The comparison with the state-of-the-art methods verifies the effectiveness of the proposed framework.
Script Identification for Devanagari and Gurumukhi using OCR

The study of character research is an active area for research as it pertains a lot of challenges. Various pattern recognition techniques are being used every day. As there are so many writing styles available, development of OCR (Optical Character Recognition) for handwritten text is difficult. Therefore, several measures have to be taken to improve the recognition process so that the burden of computation can be decreased and the accuracy for pattern recognition can be increased. The main objective of this review was to recognize and analyze handwritten document images. In this paper, we present a scheme to identify different Indian scripts like Devanagari and Gurumukhi.
Instance Segmentation Challenge Track Technical Report, VIPriors Workshop at ICCV 2021: Task-Specific Copy-Paste Data Augmentation Method for Instance Segmentation

Copy-Paste has proven to be a very effective data augmentation for instance segmentation which can improve the generalization of the model. We used a task-specific Copy-Paste data augmentation method to achieve good performance on the instance segmentation track of the 2nd VIPriors workshop challenge. We also applied additional data augmentation techniques including RandAugment and GridMask. Our segmentation model is the HTC detector on the CBSwin-B with CBFPN with some tweaks. This model was trained at the multi-scale mode by a random sampler on the 6x schedule and tested at the single-scale mode. By combining these techniques, we achieved 0.398 AP@0.50:0.95 with the validation set and 0.433 AP@0.50:0.95 with the test set. Finally, we reached 0.477 AP@0.50:0.95 with the test set by adding the validation set to the training data. Source code is available at https://github.com/jahongir7174/VIP2021.
Unblind the charts: Towards Making Interactive Charts Accessible in Android Applications

Smartphones are a crucial aspect of routine life in the modern world, and viewing information graphics such as charts becomes common practice for many unassuming tasks. However, for the vision impaired, accessing graphical material presents many difficulties. Android smartphones usually come preinstalled with Google Talkback as a default screen-reader, which attempts to cater for the visually impaired by providing supplementary auditory information when interfacing with supported applications. Still, the crux of this situation is that screen-readers rely on developers correctly implementing the required accessibility guidelines for UI elements, such as charts. Unfortunately, according to the empirical study, more than 88% of the charts found in real-world Android applications are inaccessible to a vision-impaired user, contributing to the wider accessibility issues faced by vision impaired users of smartphones. These accessibility issues can be attributed to a knowledge gap in considering possible disabilities for users, and time costs for developers. To overcome these challenges, this study proposes CAM (Chart Accessibility Module), which aims to reduce time and bridge the knowledge gap required to implement chart accessibility. CAM has two steps, generating chart summary using raw data and feeding it to the screen-reader using the Android Accessibility API for MPAndroidChart library. The user study results show that CAM significantly reduces difficulty and time taken to implement accessibility for application developers.
The Ethics of Investing in Cryptocurrencies

Much has been written about the sudden rise of cryptocurrencies, but there remains a glaring gap in the literature as to whether investing in cryptocurrencies is ethical. This first-of-its-kind Article aims to fill that gap by providing an in-depth ethical analysis based on a classical utilitarian framework comparing arguments for and against cryptocurrency investing. Topics covered include traditional views of ethical investing, environmental considerations, the support of illegal transactions, diverting funds from traditional investments which provide more societal benefit, unique U.S. considerations, efficacy as currency, and corporate social responsibility. Such analysis invites future research into this emerging, multi-trillion-dollar industry.
Binocular Mutual Learning for Improving Few-shot Classification

Most of the few-shot learning methods learn to transfer knowledge from datasets with abundant labeled data (i.e., the base set). From the perspective of class space on base set, existing methods either focus on utilizing all classes under a global view by normal pretraining, or pay more attention to adopt an episodic manner to train meta-tasks within few classes in a local view. However, the interaction of the two views is rarely explored. As the two views capture complementary information, we naturally think of the compatibility of them for achieving further performance gains. Inspired by the mutual learning paradigm and binocular parallax, we propose a unified framework, namely Binocular Mutual Learning (BML), which achieves the compatibility of the global view and the local view through both intra-view and cross-view modeling. Concretely, the global view learns in the whole class space to capture rich inter-class relationships. Meanwhile, the local view learns in the local class space within each episode, focusing on matching positive pairs correctly. In addition, cross-view mutual interaction further promotes the collaborative learning and the implicit exploration of useful knowledge from each other. During meta-test, binocular embeddings are aggregated together to support decision-making, which greatly improve the accuracy of classification. Extensive experiments conducted on multiple benchmarks including cross-domain validation confirm the effectiveness of our method.
Sanctuary Novel Mechanism for Data Verification Scheme of Email Services

The paper is all about data verification scheme of E-mail services for a company by developing an intranet mail service for the company through the generation of password. Project is based on sending and delivering the mail. This sequence is faced in a company when a manager has to deliver an authentified mail to a client, which sometime delivered to unknown recipients. To maintain confidential document or record of a company, it is must the mails are authentified by correct user credentials. If any confidential mails are sent by clients, it should not be viewed by the personal assistant. The confidential mails will be blocked which can be viewed only by the permission of the admin. The intranet mail service application will contain User Registration, User Login and Admin Login for providing a better mailing service to the company with more security. The possible outcomes of the project are creating an intranet mail service application for maintaining confidential documents, creating required authentication to send or receive restricted mails
InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring

Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging. In this paper, we propose a new model, named InstanceRefer, to achieve a superior 3D visual grounding through the grounding-by-matching strategy. In practice, our model first predicts the target category from the language descriptions using a simple language classification model. Then, based on the category, our model sifts out a small number of instance candidates (usually less than 20) from the panoptic segmentation of point clouds. Thus, the non-trivial 3D visual grounding task has been effectively re-formulated as a simplified instance-matching problem, considering that instance-level candidates are more rational than the redundant 3D object proposals. Subsequently, for each candidate, we perform the multi-level contextual inference, i.e., referring from instance attribute perception, instance-to-instance relation perception, and instance-to-background global localization perception, respectively. Eventually, the most relevant candidate is selected and localized by ranking confidence scores, which are obtained by the cooperative holistic visual-language feature matching. Experiments confirm that our method outperforms previous state-of-the-arts on ScanRefer online benchmark and Nr3D/Sr3D datasets.
Implementation of Learning Principles in Distance Learning through the WhatsApp Application

This study aims to determine the implementation of learning principles in distance learning through the WhatsApp chat application. The method used in this research is the ex post facto method. This research was conducted on students of the Economic Education Study Program, Faculty of Education and Social Sciences, Indraprasta University, PGRI with a sample of 282 students. The results of this study were as many as 81.9% of students stated that they could pay attention to learning well, 52.5% of students felt motivated when participating in learning, 69.5% of students stated that they were able to actively participate in learning, 62.4% of students stated that they felt involved when learning takes place, 84.8% stated that they could re-access the material that had been studied, 49.3% said the given task could accommodate their understanding, and 54.3% stated that the complexity of learning could be solved through learning via WhatsApp. Based on this research, it can be concluded that learning principles such as attention and motivation, activeness, direct involvement, repetition, challenges, and individual differences have been implemented well even though learning is carried out remotely through the WhatsApp chat application. However, teachers seem to need to pay attention to the challenges and individual differences in the implementation of distance learning through WhatsApp. This can be seen from the results of the study that the task given with the aim of accommodating understanding turned out to be the lowest number, not even reaching 50%. This shows that there is a need for other innovations in an effort to accommodate understanding in order to facilitate each individual who has differences in understanding each material given.
The Power of Contrast for Feature Learning: A Theoretical Analysis

Contrastive learning has achieved state-of-the-art performance in various self-supervised learning tasks and even outperforms its supervised counterpart. Despite its empirical success, theoretical understanding of the superiority of contrastive learning is still limited. In this paper, under linear representation settings, (i) we provably show that contrastive learning outperforms the standard autoencoders and generative adversarial networks, two classical generative unsupervised learning methods, for both feature recovery and in-domain downstream tasks; (ii) we also illustrate the impact of labeled data in supervised contrastive learning. This provides theoretical support for recent findings that contrastive learning with labels improves the performance of learned representations in the in-domain downstream task, but it can harm the performance in transfer learning. We verify our theory with numerical experiments.
Changing of Schools: How it affects the School Life Experiences of a Student

The Pivot point of research carried out is to study the impact of changing of school in a student’s life and various effects it can have on school life experiences. Researcher has worked upon feed backs gathered from individuals of different age and their experience accumulated from their own school lives. Analyst got reactions from 140 participants. The information was gathered using Google forms and it was investigated utilizing Google's analytical tools. It was found that changing of school had both merits and demerits given that how one perceives it and adapts according to the changes. People have different reasons for changing their schools. Factors like new region, culture and language of new place also acts as deciding factors for the adaptability and performance of newly admitted student.
Boosting RANSAC via Dual Principal Component Pursuit

In this paper, we revisit the problem of local optimization in RANSAC. Once a so-far-the-best model has been found, we refine it via Dual Principal Component Pursuit (DPCP), a robust subspace learning method with strong theoretical support and efficient algorithms. The proposed DPCP-RANSAC has far fewer parameters than existing methods and is scalable. Experiments on estimating two-view homographies, fundamental and essential matrices, and three-view homographic tensors using large-scale datasets show that our approach consistently has higher accuracy than state-of-the-art alternatives.