Reviewerly/exHarmony huggingface dataset:
Fast and Accurate: Video Enhancement Using Sparse Depth

This paper presents a general framework to build fast and accurate algorithms for video enhancement tasks such as super-resolution, deblurring, and denoising. Essential to our framework is the realization that the accuracy, rather than the density, of pixel flows is what is required for high-quality video enhancement. Most of prior works take the opposite approach: they estimate dense (per-pixel)-but generally less robust-flows, mostly using computationally costly algorithms. Instead, we propose a lightweight flow estimation algorithm; it fuses the sparse point cloud data and (even sparser and less reliable) IMU data available in modern autonomous agents to estimate the flow information. Building on top of the flow estimation, we demonstrate a general framework that integrates the flows in a plug-and-play fashion with different task-specific layers. Algorithms built in our framework achieve 1.78x - 187.41x speedup while providing a 0.42 dB - 6.70 dB quality improvement over competing methods.
OneFlow: One-class flow for anomaly detection based on a minimal volume region

We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region. Contrary to density-based methods, OneFlow is constructed in such a way that its result typically does not depend on the structure of outliers. This is caused by the fact that during training the gradient of the cost function is propagated only over the points located near to the decision boundary (behavior similar to the support vectors in SVM). The combination of flow models and a Bernstein quantile estimator allows OneFlow to find a parametric form of bounding region, which can be useful in various applications including describing shapes from 3D point clouds. Experiments show that the proposed model outperforms related methods on real-world anomaly detection problems.
A Note on Occur-Check

Most known results on avoiding the occur-check are based on the notion of "not subject to occurcheck" (NSTO).It means that unification is performed only on such pairs of atoms for which the occur-check never succeeds in any run of a nondeterministic unification algorithm.Here we show that this requirement is too strong.We show how to weaken it, and present some related sufficient conditions under which the occur-check may be safely omitted.We show examples for which the proposed approach provides more general results than the approaches based on well-moded and nicely moded programs (this includes cases to which the latter approaches are inapplicable).
A study of ecocriticism on the representations of ecological conditions in Rawa Gambut

This study aims to determine the representation of the ecological crisis found in the Rawa Gambut drama script written by Conie Sema. This qualitative descriptive research used an ecocritical approach. The data are in the form of words, phrases, sentences, and paragraphs that contain information on ecological conditions. Textual analysis was used to examine the data. Data were collected through reading and note-taking techniques with heuristic and hermeneutic reading. The results showed the themes within the drama script as an ecological crisis in Indonesia due to unfriendly environmental development policies. The representation of the ecological crisis are: 1) ideal ecological conditions are maintained if the utilization pattern is wise and responsible, 2) the ecological crisis is closely related to human intervention, 3) humans are the main cause of ecological disasters, 4) there is a disregard for environmental wisdom principles in the use of sustainable living space, 5) the moral message is the importance of awareness to maintain environmental sustainability, and finally, 6) the author offers an ecocentric paradigm to oppose the ideology of anthropocentrism, which is still the dominant perspective in the natural resource management. This study has implications, along with the importance of educational efforts, to raise awareness of environmental sustainability through drama scripts.
Automated Essay Grading System using NLP Techniques

The purpose of the study is to develop an automated essay grading system (AES) which can grade students essays based on various factors. Our proposed system performs grading of essays based on two features. Simple features consist of finding syntactic errors such as spelling mistakes, grammatical errors, punctuations and sentence proportions. Complex features consist of finding semantic errors through discourse analysis, thematic analysis and detection of undesirable style of writing. Many existing AES systems fail to consider the semantic parts of the essay which is addressed in this study. Calculation of score would be done based on what is specified in rubrics. The proposed system is evaluated using datasets from kaggle. The accuracy of model and obtained results show an agreement with teachers’ grading. This gives us an indication that the model can be deployed for assessment of students’ essay, thereby leading to reduction in time, efforts and cost for evaluating an essay.
Recent Trends in Answer Script Evaluation – A Literature Survey

Assessment of answer scripts is an integral part of an examination and education system.A fair, consistent, unbiased, and correct valuation ensures the integrity of an examination system and is important for all education institutions.Since manual valuation is cumbersome and can be biased or influenced by the perception/mood of the evaluator, automatic grading of scripts has become very relevant.Automatic short answer grading (ASAG) techniques have been widely researched in the last decade and have assumed increased relevance because of online teaching and examinations during the Covid-19 pandemic.This review paper focuses on the recent works in the area of automatic answer grading and compares the techniques, methodologies employed, and the consequent results to evaluate their effectiveness.It discusses the advantages and limitations of the techniques by systematically categorizing the questions into both long/short as well as open-ended/close-ended questions and suggests a new model for improving the grading outcomes.
Correction: SERS-based nanostrategy for rapid anemia diagnosis

Correction for ‘SERS-based nanostrategy for rapid anemia diagnosis’ by Pir Muhammad et al. , Nanoscale , 2020, 12 , 1948–1957, DOI: 10.1039/C9NR09152A.
Context-Sensitive Control of Adaptation: Self-Modeling Networks for Human Mental Processes Using Mental Models

Within their mental and social processes, humans often learn, adapt and apply specific mental models of processes in the world or other persons, as a kind of blueprints. In this paper, it is discussed how analysis of this provides useful inspiration for the development of new computational approaches from a Machine Learning and Network-Oriented Modeling perspective. Three main elements are: applying the mental model by internal simulation, developing and revising a mental model by some form of adaptation, and exerting control over this adaptation in a context-sensitive manner. This concept of controlled adaptation relates to the Plasticity Versus Stability Conundrum from neuroscience. The presented analysis has led to a three-level computational architecture for controlled adaptation. It is discussed and illustrated by examples of applications how this three-level computational architecture can be specified based on a self-modeling network and used to model controlled learning and adaptation processes based on mental models in a context-sensitive manner.
Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness

Text style can reveal sensitive attributes of the author (e.g.race or age) to the reader, which can, in turn, lead to privacy violations and bias in both human and algorithmic decisions based on text.For example, the style of writing in job applications might reveal protected attributes of the candidate which could lead to bias in hiring decisions, regardless of whether hiring decisions are made algorithmically or by humans.We propose a VAE-based framework that obfuscates stylistic features of human-generated text through style transfer by automatically re-writing the text itself.Our framework operationalizes the notion of obfuscated style in a flexible way that enables two distinct notions of obfuscated style: (1) a minimal notion that effectively intersects the various styles seen in training, and (2) a maximal notion that seeks to obfuscate by adding stylistic features of all sensitive attributes to text, in effect, computing a union of styles.Our style-obfuscation framework can be used for multiple purposes, however, we demonstrate its effectiveness in improving the fairness of downstream classifiers.We also conduct a comprehensive study on style pooling's effect on fluency, semantic consistency, and attribute removal from text, in two and three domain style obfuscation. 1
DAAB: Deep Authorship Attribution in Bengali

Authorship attribution identifies the true author of an unknown document. Authorship attribution plays a crucial role in plagiarism detection and blackmailer identification, however, the existing studies on authorship attribution in Bengali are limited. In this paper, we propose an instance-based deep authorship attribution model, called DAAB, to identify authors in Bengali. Our DAAB model fuses features from convolutional neural networks and another set of features from an artificial neural network to learn the stylometry of an author for authorship attribution. Extensive experiments with three real benchmark datasets such as Bengali-Quora and two online Bengali Corpus demonstrate the superiority of our authorship attribution model.
Machine Learning for Cultural Heritage Classification

Cultural Heritage (CH) assets may be defined as integrated spatial systems composed of interconnected shapes.The classification and organization of geometries within a hierarchical system are functional to their correct interpretation, which is often performed using 3D point clouds.The recurring shapes recognition becomes a crucial activity, nowadays accelerated by Machine Learning (ML) procedures able to associate semantic meaning to geometric data.An interdisciplinary research team [1] has developed a ML supervised approach, tested on the Milan Cathedral and Pomposa Abbey datasets, which presents an innovative multi-level and multi-resolution classification (MLMR) process.The methodology improves the learning activity and optimizes the 3D classification by a hierarchical concept.
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning

We introduce EfficientCL, a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning.For data augmentation, we stack two types of operation sequentially: cutoff and PCA jittering.While pretraining steps proceed, we apply curriculum learning by incrementing the augmentation degree for each difficulty step.After data augmentation, we apply contrastive learning on projected embeddings of original and augmented examples.When fine-tuned on GLUE benchmark, our model outperforms baseline models, especially for sentence-level tasks.Additionally, this improvement is achieved with only 70% of computational memory compared to the baseline model. 1
Investigating the Emergence of Digital Platforms for Listening Learning Proficiency

This study investigates the use of any platforms in English language teaching and learning, especially in listening skills. This research design is qualitative. From the result, it shows that some technologies available both online or offline include applications or platforms that provide many choices for listening to English, they are. 1) Music platform. These platforms can be found in PlayStore, such as Joox and Spotify recommends songs for listening skills. 2) Youtube channel. Many YouTube channels for learning English listening skills include 1) Voice of America (VOA), BBC Learning English, Learn English with TV Series, English with Lucy, and Oxford Online English. 3) Podcasts. Both BBC (British Broadcasting Corporation) and VoA (Voice of America). These Podcasts offer listening skills. The other podcasts are “The English We Speak, Podcast in English, Better at English, Luke’s English Podcast, Espresso English Podcast, Anchor FM” etc. 4) Websites that are pretty representative in practicing the listening skills such as Sound English, ESL-Lab, English listening, Ello, learn English British Council, Daily ESL, Story Nory, Story Line, which can be accessed. Learning English through several applications above can be an alternative for students in practicing and improving their English listening skills. Listening exercises can be carried out by using interesting listening strategies when learning English. It depends on the teachers/lecturers who teach listening subjects and the students who learn English materials.
GUJARATI POETRY CLASSIFICATION BASED ON EMOTIONS USING DEEP LEARNING

Poetries are the way to express the word that represents emotions and thoughts with the use of any language in the world and the computational linguistic study to the emotion recognition from poetry is an overwhelming and complex task too. Ultimate goal of this study is to disclose emotions through Gujarati poetries with the use of variety of characteristic there within Gujarati poems. Study presents a novel perspective in sentiment capture as of Gujarati Poems. ‘Kavan’ Gujarati poems collection was by hand interpreted upon the Indian idea mentioned in ‘Navarasa’. In the collection, 300+ poems were categorized into nine feeling mentioned in ‘Navarasa’, respectively ‘?????’, ‘???????’, ‘??????’, ‘? ???????’, ‘???????’, ‘????????’, ‘????????’, ‘???????’, and ‘???????’. The Zipfian allotment is part a family of related discrete power law probability distributions, which is used to identify the probability of one ‘rasa’ from the given poem.. Deep Learning has colossal set of pattern recognition tools that can be used for natural language processing and have incredible potential to locate a solution for challenging machine learning problem. Result accuracy was found up to ~87.62% of emotion classification task from Gujarati poetry corpus.
Online Virtual Training in Soft Actor-Critic for Autonomous Driving

Deep Reinforcement Learning (RL) algorithms are widely being used in autonomous driving due to their ability to cope with unseen environments. However, in a complex domain like autonomous driving, these algorithms need to explore the environment enough to be able to converge. Therefore, these algorithms are faced with the problem of long training times and large amounts of data. In addition, using deep RL algorithms in areas that safety is an important factor such as autonomous driving can lead to a safety issue since we cannot leave the car driving in the street unattended. In this research, we tested two methods for the purpose of reducing the training time. First, we pre-trained Soft Actor-Critic (SAC) with Learning from Demonstrations (LfD) to find out if pre-training can reduce the training time of the SAC algorithm. Then, an online end-to-end combination method of SAC, LfD, and Learning from Interventions (LfI) is proposed to train an agent (dubbed Online Virtual Training). Both scenarios were implemented and tested in an inverted-pendulum task in OpenAI gym and autonomous driving in the Carla simulator. The results showed a dramatic reduction in the training time and a significant increase in gaining rewards for Online LfD (33%) and Online Virtual training (36 %) as compare to the baseline SAC. The proposed approach is expected to be effective in daily commute scenarios for autonomous driving.
One-dimensional quantum walks with a time and spin-dependent phase shift

We investigate the role of a time and spin-dependent phase shift on the evolution of one-dimensional discrete-time quantum walks. By employing Floquet engineering a time and spin-dependent phase shift (ϕ) is stroboscopically imprinted onto the walker's wave function at the end of each step of the walk. For a quantum walk driven by the standard protocol and with rational values of the phase factor, i.e., ϕ/2π=p/q we show with our numerical simulations that revivals with equal periods occur in the probability distribution of the walk. In the split-step quantum walk we employ two different coin operators which are parametrized by a parameter δθ. By making use of δθ as a control parameter we demonstrate that for ϕ/2π=p/q the walk exhibits three major types of dynamics: ballistic spreading, revivals, and localization. For an irrational value of ϕ/2π our results show revivals with unpredictable periods, and the walker remains localized in a small region of the lattice. Furthermore, in view of an experimental realization we investigate the robustness of revivals against noise in the phase shift. Our results show that signatures of revivals persist for smaller values of the noise parameter while these vanish for larger values. Our work is important in the context of quantum computation and simulation with quantum walks where different types of the walk dynamics can be engineered by making use of the time and spin-dependent phases shift, and the coin parameter as control knobs.
The Integration of Internet and Picture Book: Using Online Picture Book Reading Project to Promote Primary School Students’ Reading Literacy

This study aims to explore the effect of the combination of English picture books and the Internet on pupils’ English reading literacy. 12 primary school students participated in the online picture book reading project. The results are as follows: first, through pre-test and post-test, students’ reading comprehension ability has been improved; Second, the questionnaire shows the online project can help them increase reading interests, develop good reading habits and reading strategies; Last but not least, children give positive feedback to the project, especially about reading-guide videos and teachers’ comments in pronunciation. In general, the online project can promote pupils’ reading literacy.
Online sales system and organization outcome

The aim of this study is to identify the link between online sales systems, infrastructure, ease of use &amp; information accuracy in improving Jordanian restaurants’ call centers' performance effectiveness (JRCCPE). In order to achieve the study's objectives, a questionnaire was conducted for measuring the link between online sales systems (OSS), infrastructure, ease of use &amp; information accuracy in improving JRCCPE. The study sample was selected by distributing 220 questionnaires to all employees of Jordanian restaurants’ call centers (JRCC) from the set of employees working in an online sales system where 173 were retrieved. Aiming to answer the study questions and test hypotheses, the researcher extracted the means and standard deviations to apply the multiple regression equation. Accordingly, the study reached many results, showing a statistically significant effect for using the OSS, infrastructure, ease of use &amp; information accuracy in improving JRCCPE. The study suggested that JRCC seeks adding various characteristics of editing, deleting, copying, and setting the time on the basis of text messaging through such networks as well as the need to conduct marketing studies in order to enable companies to achieve the customers' wishes in a method matching their expectations.
Static Analysis at GitHub

The Semantic Code team at GitHub builds and operates a suite of technologies that power symbolic code navigation on github.com. We learned that scale is about adoption, user behavior, incremental improvement, and utility. Static analysis in particular is difficult to scale with respect to human behavior; we often think of complex analysis tools working to find potentially problematic patterns in code and then trying to convince the humans to fix them. Our approach took a different tack: use basic analysis techniques to quickly put information that augments our ability to understand programs in front of everyone reading code on GitHub with zero configuration required and almost immediate availability after code changes.
Obtaining Quantum Gate Models from Known Input and Output Values

Studies in the field of quantum computing have gained momentum with the emergence of quantum computers in recent years. Quantum computing, which is based on quantum principles, uses different techniques from known computational methods. The large size of quantum circuits causes noise in computers in the NISQ era. Optimizing quantum circuits and completing a job with a minimum of gates is important for the algorithms to work properly. In this study, the modelling of a quantum system with known input and output is investigated. By transferring the developed method to the programming environment, it is calculated whether the desired gate can be obtained and what form it should be. The created gate models are tested, and the results produced by the gates compared with the expected values.
Research on UAV Trajectory Planning Based on Artificial Bee Colony Algorithm

With the increasing status of Unmanned Aerial Vehicle (UAV) in the modern battlefield, the problem of its trajectory planning has become a research hotspot in the academic world, and the Artificial Bee Colony Algorithm(ABC) is simple to operate, fewer parameters, easy to implement, and robust. Strong, it has certain advantages in solving combined optimization problems such as UAV trajectory planning. This article mainly summarizes the current applied research status of the ABC algorithm in UAV trajectory planning, proposes the following solution to the limitations of the algorithm, and provides a reference for future UAV trajectory planning problems.
Implementation of IoT and Blockchain for Temperature Monitoring in Covid19 Vaccine Cold Chain Logistics

The current COVID-19 pandemic impacted globally and resulted one hundred million cases with almost two and half million death to-date. The concerns of further spread of the virus has caused the global economy to a halt when most countries globally implementing movement control. Recent approved vaccines developed by multiple fronts has shed some hoped for the recovery of the human activities back to pre-pandemic state. For Malaysia, the government has procured the vaccine from six suppliers and each vaccine require proper temperature monitoring to ensure the safety and the efficacy of the vaccines during transportation and distribution process. Effective vaccine Cold Chain Logistics (CCL) management will be required precise coordination and cooperation across multiple parties to ensure the quality of the vaccines which require temperature monitoring, distribution of records for traceability. Furthermore, the CCL process continuity is becoming critical from the handover point at Malaysia point of receipt from the manufacturer and further distribution from central storage to the two hundred vaccination center across the country. Key concerns will be the vaccines’ CCL process for remote areas in Malaysia. This paper will describe the architecture required to integrate IoT and Blockchain into the CCL management system to monitor the temperature of insulated container or cooler box. While this project may not be able to cover all the parameters it will serve an indication of the required system engineering consideration for the IoT and Blockchain application for the vaccine CCL management