Reviewerly/exHarmony huggingface dataset:
Parallel bandit architecture based on laser chaos for reinforcement learning

Accelerating artificial intelligence by photonics is an active field of study aiming to exploit the unique properties of photons. Reinforcement learning is an important branch of machine learning, and photonic decision-making principles have been demonstrated with respect to the multi-armed bandit problems. However, reinforcement learning could involve a massive number of states, unlike previously demonstrated bandit problems where the number of states is only one. Q-learning is a well-known approach in reinforcement learning that can deal with many states. The architecture of Q-learning, however, does not fit well photonic implementations due to its separation of update rule and the action selection. In this study, we organize a new architecture for multi-state reinforcement learning as a parallel array of bandit problems in order to benefit from photonic decision-makers, which we call parallel bandit architecture for reinforcement learning or PBRL in short. Taking a cart-pole balancing problem as an instance, we demonstrate that PBRL adapts to the environment in fewer time steps than Q-learning. Furthermore, PBRL yields faster adaptation when operated with a chaotic laser time series than the case with uniformly distributed pseudorandom numbers where the autocorrelation inherent in the laser chaos provides a positive effect. We also find that the variety of states that the system undergoes during the learning phase exhibits completely different properties between PBRL and Q-learning. The insights obtained through the present study are also beneficial for existing computing platforms, not just photonic realizations, in accelerating performances by the PBRL algorithms and correlated random sequences.
RecNet: A Resource-Constraint Aware Neural Network for Used Car Recommendation

Abstract Resource constraints, e.g., limited product inventory or financial strength, may affect consumers’ choices or preferences in some recommendation tasks but are usually ignored in previous recommendation methods. In this paper, we aim to mine the cue of user preferences or intention in the case of resource-constraint recommendation tasks. For this purpose, we specifically build a largely used car transaction dataset possessing resource-constraint characteristics. Accordingly, we propose a resource-constraint-aware network to predict the user’s future interaction based on dynamic connections between users and items. To describe the user-item connection dynamically, mutually recursive recurrent neural networks (MRRNNs) are introduced to capture long-term interactive dependencies, and effective representations of users and items are obtained. To further consider the resource constraint, a resource-constraint branch is built to explore resource variation’s influence on user preferences. Finally, mutual information is introduced to measure the similarity between the future user action and fused historical behavior features to predict future interaction. The fused features come from both MRRNNs and resource-constraint branches. We test the performance on the built used car transaction dataset and the Tmall dataset, and the experimental results verify the effectiveness of our framework.
A Collaborative Filtering Recommendation Algorithm Based on Restricted Random Walk

Traditional collaborative filtering disregards the granularity of users’ preference drifting and item popularity bias in modeling, thus diminished the accuracy of recommendation. This paper proposes a new collaborative filtering algorithm based on constrained random walk. Two new trust network: user-based and item-based are proposed, with Restricted Random Walk (RW) to adaptively track the change of users’ preference drifting and item popularity bias. Experimental results on social data sets show that the proposed method is superior to existing recommendation algorithms.
Review of Parameters, Approaches and Challenges in Reading Comprehension Systems

Reading comprehension involves ability of reading the text, understanding the meaning of the given text passage and answering the questions based on it. It is a challenging task for machines, as it requires natural language understanding and knowledge about the world. In order to understand the meaning of the text, it is necessary to identify the context and organization of the given passage. It also involves ability to draw inference based on sentences in the paragraph. In this type of answering, reasoning is dependent on the type of comprehension dataset. The answering can be based on single sentence or set of multiple sentences. The complexity of the reasoning required in reading comprehension dataset depends on several factors such as source of paragraphs, the ordering of sentences in the paragraphs and type of questions. Answering the questions based on context requires identification of linguistic features, based on syntax, semantics, and different sentence patterns appearing in the paragraph text. This work presents, exhaustive study of the various approaches used by different authors for feature extraction in the domain of reading comprehension. Most of the recent work in reading comprehension is mainly focused on application of deep learning algorithms but it may not work well with low resource data. Low resource datasets having diverse linguistic features, need deep understanding of the text. At the end, we discuss some of the open challenges in modeling the comprehension systems.
Heuristic optimisation of multi-task dynamic architecture neural network (DAN2)

Abstract This article proposes a novel method to optimise the Dynamic Architecture Neural Network (DAN2) adapted for a multi-task learning problem. The multi-task learning neural network adopts a multi-head and serial architecture with DAN2 layers acting as the basic subroutine. Adopting a dynamic architecture, the layers are added consecutively starting from a minimal initial structure. The optimisation method adopts an iterative heuristic scheme that sequentially optimises the shared layers and the task-specific layers until the solver converges to a small tolerance. Application of the method has demonstrated the applicability of the algorithm to simulated datasets. Comparable results to Artificial Neural Networks (ANNs) have been obtained in terms of accuracy and speed.
Blockchain technology in quantum chemistry: A tutorial review for running simulations on a blockchain

Abstract Simulations of molecules have recently been performed directly on a blockchain virtual computer at atomic resolution. This tutorial review covers the current applications of blockchain technology for molecular modeling in physics, chemistry, and biology, and provides a step‐by‐step tutorial for computational scientists looking to use blockchain computers to simulate physical and scientific processes in general. Simulations of carbon monoxide have been carried out using molecular dynamics software on the Ethereum blockchain in order to facilitate the tutorial.
Практическая народная астрономия – об исчислении времени в архивных текстах

Time can be determined by observation of astronomical objects. For determination of time, various phenological signs as well as the position of the heavenly bodies were used. To tell the time of the day (or night), the most useful constellations are Big and Little Dipper, the Orion and the Sieve. The North Star is the center of the clock and due to its immobility an important landmark. The best known time teller is the Wain, also used for fortune telling and meteorological forecasts. The Orion was the winter-time time-telling constellation and provider of omens. We know, that earlier time we did not had the clocks... At the morning, if host wanted to wake up his workers, he went outside, looked, where is Big Dipper, where is Sieve and where is Orion, and over his head he looked every important star … and where they were, he knew, what the time was, must he wake up the workers or let them sleep more, because he knew the stars and could say the time as from clock. E 20980/1 (3) &lt; Tõstamaa khk. The majority of Estonian folk calendar holidays are related to solar or lunisolar system, but there are some constellation names, directly connected with folk calendar. The article is based on materials from the manuscripts of the Estonian Foklore Archives, digitized and analyzed by the Department of Folkloristics.
Kemampuan Mahasiswa dalam Menganalisis Homofoni pada Video Animasi 《中秋节》 Zhōngqiū Jié di Kanal YouTube 三淼儿童官方频道 Sān Miǎo Értóng Guānfāng Píndào

Homophone are the similarity of sounds between two speech units regardless of spelling.In Mandarin, homophones are related to Chinese pronunciations that have the same tone but different Chinese forms and meanings.The purpose of this research was to find out how the ability of 1 st year students of Chinese Literature Gunadarma University in distinguishing homophones in Chinese.This research is a qualitative descriptive study.The results of this research showed that the ability of 1 st year students of Chinese Literature Gunadarma University in distinguishing homophones in Chinese was still low.Of the 10 test questions tested, most students answered correctly on the homophones dào, bǎi, péng, dan tú.
The Effect of Transitioning to Online Learning on the Academic Performance of Upper Basic Stage Female Students in English Language in Amman District Directorate from their Point of View

The study aimed at identifying the effect of transitioning to online learning on the academic performance of upper basic stage female students in Amman district directorate. The population was (3953) of upper basic stage female students in Amman District directorate. The sample consisted of (375) female students. The questionnaire consisted of (4) dimensions and (32) statements. It was found that the status quo of the academic performance of upper basic stage female students in English language in Amman district directorate before transitioning to online learning was high. The status quo of the academic performance of upper basic stage female students in English language in Amman district directorate after transitioning to online learning was moderate .There were significant differences in the means to identify the status quo of the effect of transitioning to online learning on the academic performance of upper basic stage female students in Amman district directorate, which came in favour of before transitioning to online learning, there were significant differences in the status quo of the academic performance of upper basic stage female students in English language in Amman district directorate after transitioning to online learning, attributed to Grade and English Language Average Score. Several recommendations were presented.
Automate the Deployment of Any Custom Web Applications Across Several Clouds using Kubernetes Clusters

During COVID period, most the businesses are worked in online mode. For deploying their businesses need to keep their web apps up all throughout the clock. Every successful small-scale business has its own website or [3 ]web application. To deploy the website with every modification needed, a team of DevOps Engineers is needed. The role of those engineers is to take the website, run it on a server and make it available to the end-user. This entire process can therefore be automated in a single click with this system. In this system, we are integrating the DevOps tools such as Ansible, Terraform, Kubernetes, Docker and Cloud Servers. This system when used will save more time and would be much more efficient. It will help businesses cost-effectively achieve zero downtime. Along with these benefits, their website or web app would be secured as it would be deployed and monitored on Cloud. This system will help businesses to grow more.
Analisis Motivasi dan Kesulitan Belajar Bahasa Mandarin Mahasiswa Indonesia non-Keturunan Tionghoa di Universitas Sebelas Maret Indonesia

Non-Chinese ancestry students studying Mandarin at Sebelas Maret University, Indonesia have increased every year. This paper discusses three aspects, learning motivation, learning attitude, and learning difficulties of non-Chinese ancestry students in the Mandarin Language Department at Universitas Sebelas Maret. Non-Chinese ancestry students have several characteristics in learning, have learning goals, and have a positive learning attitude. In general, the difficulties faced by non-Chinese ancestry students in learning Chinese are because they rarely communicate in Chinese, which caused mistakes in listening and pronunciation, even though there is a lot of pressure in learning, they still maintain a strong motivation to learn Chinese.
The Current Trends of Deep Learning in Autonomous Vehicles: A Review

Autonomous vehicles are the future of road traffic.In addition to improving safety and efficiency from reduced errors compared to conventional vehicles, autonomous vehicles can also be implemented in applications that may be inconvenient or dangerous to a human driver.To realize this vision, seven essential technologies need to be evolved and refined including path planning, computer vision, sensor fusion, data security, fault diagnosis, control, and lastly, communication and networking.The contributions and the novelty of this paper are: 1) provide a comprehensive review of the recent advances in using deep learning for autonomous vehicle research, 2) offer insights into several important aspects of this emerging area, and 3) identify five directions for future research.To the best of our knowledge, there is no previous work that provides similar reviews for autonomous vehicle design.
Children Story Books as a Means of Social and Intellectual Development

The proposed study has explored the interpersonal meta-function in two children story books entitled “Four Feet Two Sandals” and “The Spirit of Cheetah”. It was aimed to explore what type of social and intellectual messages were being conveyed in these stories. The study was based on qualitative method and Halliday &amp; Mathiessen (2004) modal of interpersonal meta-function was applied as a theoretical framework. Meanwhile, a sample of 10 passages was drawn from the selected story books. Data was analyzed through the lens of content analysis technique. In both aspects of mood and modality, the findings revealed the author’s intention to convey several useful values like sacrifice, friendship, patience, determination to boost the social and intellectual level of the children.
PROSEDUR PENERJEMAHAN TEKS BERITA BAHASA INDONESIA KE DALAM BAHASA MANDARIN

This study aims to analyze the procedure for translating Indonesian news texts into Mandarin.To analyze the translation procedure, Newmark's theory of translation procedures was used.The methods and techniques used are qualitative research methods based on the translation of news texts.Where the translation procedure is not only used to translate various texts, but also has a more important function, namely in translating news texts.With this research, it is hoped that it can help readers to use good and appropriate translation procedures.
Linear regression with partially mismatched data: local search with theoretical guarantees

Linear regression is a fundamental modeling tool in statistics and related fields. In this paper, we study an important variant of linear regression in which the predictor-response pairs are partially mismatched. We use an optimization formulation to simultaneously learn the underlying regression coefficients and the permutation corresponding to the mismatches. The combinatorial structure of the problem leads to computational challenges. We propose and study a simple greedy local search algorithm for this optimization problem that enjoys strong theoretical guarantees and appealing computational performance. We prove that under a suitable scaling of the number of mismatched pairs compared to the number of samples and features, and certain assumptions on problem data; our local search algorithm converges to a nearly-optimal solution at a linear rate. In particular, in the noiseless case, our algorithm converges to the global optimal solution with a linear convergence rate. Based on this result, we prove an upper bound for the estimation error of the parameter. We also propose an approximate local search step that allows us to scale our approach to much larger instances. We conduct numerical experiments to gather further insights into our theoretical results, and show promising performance gains compared to existing approaches.
Spatiotemporal Deformable Scene Graphs for Complex Activity Detection

Long-term complex activity recognition and localisation can be crucial for decision making in autonomous systems such as smart cars and surgical robots. Here we address the problem via a novel deformable, spatiotemporal scene graph approach, consisting of three main building blocks: (i) action tube detection, (ii) the modelling of the deformable geometry of parts, and (iii) a graph convolutional network. Firstly, action tubes are detected in a series of snippets. Next, a new 3D deformable RoI pooling layer is designed for learning the flexible, deformable geometry of the constituent action tubes. Finally, a scene graph is constructed by considering all parts as nodes and connecting them based on different semantics such as order of appearance, sharing the same action label and feature similarity. We also contribute fresh temporal complex activity annotation for the recently released ROAD autonomous driving and SARAS-ESAD surgical action datasets and show the adaptability of our framework to different domains. Our method is shown to significantly outperform graph-based competitors on both augmented datasets.
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer

Self-supervised representation learning (SSL) methods provide an effective label-free initial condition for fine-tuning downstream tasks. However, in numerous realistic scenarios, the downstream task might be biased with respect to the target label distribution. This in turn moves the learned fine-tuned model posterior away from the initial (label) bias-free self-supervised model posterior. In this work, we re-interpret SSL fine-tuning under the lens of Bayesian continual learning and consider regularization through the Elastic Weight Consolidation (EWC) framework. We demonstrate that self-regularization against an initial SSL backbone improves worst sub-group performance in Waterbirds by 5% and Celeb-A by 2% when using the ViT-B/16 architecture. Furthermore, to help simplify the use of EWC with SSL, we pre-compute and publicly release the Fisher Information Matrix (FIM), evaluated with 10,000 ImageNet-1K variates evaluated on large modern SSL architectures including ViT-B/16 and ResNet50 trained with DINO.
WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy

Accurate segmentation of organelle instances, e.g., mitochondria, is essential for electron microscopy analysis. Despite the outstanding performance of fully supervised methods, they highly rely on sufficient per-pixel annotated data and are sensitive to domain shift. Aiming to develop a highly annotation-efficient approach with competitive performance, we focus on weakly-supervised domain adaptation (WDA) with a type of extremely sparse and weak annotation demanding minimal annotation efforts, i.e., sparse point annotations on only a small subset of object instances. To reduce performance degradation arising from domain shift, we explore multi-level transferable knowledge through conducting three complementary tasks, i.e., counting, detection, and segmentation, constituting a task pyramid with different levels of domain invariance. The intuition behind this is that after investigating a related source domain, it is much easier to spot similar objects in the target domain than to delineate their fine boundaries. Specifically, we enforce counting estimation as a global constraint to the detection with sparse supervision, which further guides the segmentation. A cross-position cut-and-paste augmentation is introduced to further compensate for the annotation sparsity. Extensive validations show that our model with only 15% point annotations can achieve comparable performance as supervised models and shows robustness to annotation selection.
System Demo: Tool and Infrastructure for Offensive Language Error Analysis (OLEA) in English

The automatic detection of offensive language is a pressing societal need. Many systems perform well on explicit offensive language but struggle to detect more complex, nuanced, or implicit cases of offensive and hateful language. OLEA is an open-source Python library that provides easy-to-use tools for error analysis in the context of detecting offensive language in English. OLEA also provides an infrastructure for re-distribution of new datasets and analysis methods requiring very little coding.
When does mixup promote local linearity in learned representations?

Mixup is a regularization technique that artificially produces new samples using convex combinations of original training points. This simple technique has shown strong empirical performance, and has been heavily used as part of semi-supervised learning techniques such as mixmatch~\citep{berthelot2019mixmatch} and interpolation consistent training (ICT)~\citep{verma2019interpolation}. In this paper, we look at Mixup through a \emph{representation learning} lens in a semi-supervised learning setup. In particular, we study the role of Mixup in promoting linearity in the learned network representations. Towards this, we study two questions: (1) how does the Mixup loss that enforces linearity in the \emph{last} network layer propagate the linearity to the \emph{earlier} layers?; and (2) how does the enforcement of stronger Mixup loss on more than two data points affect the convergence of training? We empirically investigate these properties of Mixup on vision datasets such as CIFAR-10, CIFAR-100 and SVHN. Our results show that supervised Mixup training does not make \emph{all} the network layers linear; in fact the \emph{intermediate layers} become more non-linear during Mixup training compared to a network that is trained \emph{without} Mixup. However, when Mixup is used as an unsupervised loss, we observe that all the network layers become more linear resulting in faster training convergence.
Socio-economic influences on the ease of doing business in the Niger Delta: Evidence from small business owners and managers

Doing business with ease depends on many factors. For Nigeria’s oil-rich Niger Delta region, the ease of starting and running a small-scale business depends on so many factors. This paper aims to leverage the experiences of 103 small business owners and managers to examine the socio-economic factors influencing the ease of doing business in the Niger Delta region. Data collection was via the structured questionnaire administered via Google Form. Data analysis involved simple percentages, mean and ranking. The findings reveal that lack of power supply, getting credit and resolving insolvency are the major hindrances to the ease of doing business in the region. The study recommends setting up effective policies and programmes in the areas of attracting investment to the energy sector. The government should enlighten the masses about National Collateral Registry and its importance to business owners. Keywords: Business, National Collateral Registry, Niger Delta region, World Bank indices;