Reviewerly/exHarmony huggingface dataset:
Utilizing Data from Twitter to Explore the UX of “Madrasati” as a Saudi e-Learning Platform Compelled by the Pandemic

Purpose: When a website or application is designed and deployed rapidly as a response to an urgent need, it may not satisfy users. Therefore, we decided to investigate users’ attitudes and (dis)satisfaction towards the UX of the Madrasati platform, an e-learning system that was developed by the Saudi Ministry of Education as an alternative to traditional learning during the COVID-19 crisis. Method: The study utilizes Twitter to collect a large volume of data (177,358 tweets) related to Madrasati. Two relevant hashtags #Madrasati ( مدرستي #) and #Minaset Madrasati ( منصة مدرستي #) were used to collect data within the first two months after the launch of the platform. The two-month period was split into four phases: Pre-Semester Phase, Familiarization Phase, Interaction Phase, and Use Phase. The Microsoft Product Reaction Cards (MPRC) tool was implemented to judge user satisfaction/dissatisfaction. Results: The findings show a sudden dissatisfaction about the platform upon launch, but a gradual increase in positive UX over time. Various categories of negative UX (e.g., errors, user denial, and speed issues) gradually became less and less over the observed two months. More importantly, the results show how big data from Twitter can be used for analyzing the UX of a new product. Conclusion: UX is not static; it can change positively over time as users gain more experience with the system.
Adapter-based fine-tuning of pre-trained multilingual language models for code-mixed and code-switched text classification

Code-mixing and code-switching are frequent features in online conversations. Classification of such text is challenging if one of the languages is low-resourced. Fine-tuning pre-trained multilingual language models is a promising avenue for code-mixed text classification. In this paper, we explore adapter-based fine-tuning of PMLMs for CMCS text classification. We introduce sequential and parallel stacking of adapters, continuous fine-tuning of adapters, and training adapters without freezing the original model as novel techniques with respect to single-task CMCS text classification. We also present a newly annotated dataset for the classification of Sinhala–English code-mixed and code-switched text data, where Sinhala is a low-resourced language. Our dataset of 10000 user comments has been manually annotated for five classification tasks: sentiment analysis, humor detection, hate speech detection, language identification, and aspect identification, thus making it the first publicly available Sinhala–English CMCS dataset with the largest number of task annotation types. In addition to this dataset, we also tested our proposed techniques on Kannada–English and Hindi–English datasets. These experiments confirm that our adapter-based PMLM fine-tuning techniques outperform or are on par with the basic fine-tuning of PMLM models.
ExCrowd: A Blockchain Framework for Exploration-Based Crowdsourcing

Because of the rise of cryptocurrencies and decentralized apps, blockchain technology has generated a lot of interest. Among these is the emergent blockchain-based crowdsourcing paradigm, which eliminates the centralized conventional mechanism servers in favor of smart contracts for task and reward allocation. However, there are a few crucial challenges that must be resolved properly. For starters, most reputation-based systems favor high-performing employees. Secondly, the crowdsourcing platform’s expensive service charges may obstruct the growth of crowdsourcing. Finally, unequal evaluation and reward allocation might lead to job dissatisfaction. As a result, the aforementioned issues will substantially impede the development of blockchain-based crowdsourcing systems. In this study, we introduce ExCrowd, a blockchain-based crowdsourcing system that employs a smart contract as a trustworthy authority to properly select workers, assess inputs, and award incentives while maintaining user privacy. Exploration-based crowdsourcing employs the hyperbolic learning curve model based on the conduct of workers and analyzes worker performance patterns using a decision tree technique. We specifically present the architecture of our framework, on which we establish a concrete scheme. Using a real-world dataset, we implement our model on the Ethereum public test network leveraging its reliability, adaptability, scalability, and rich statefulness. The results of our experiments demonstrate the efficiency, usefulness, and adaptability of our proposed system.
Performance of superconducting quantum computing chips under different architecture designs

Existing and near-term quantum computers can only perform two-qubit gates between physically connected qubits. Research has been done on compilers to rewrite quantum programs to match hardware constraints. However, the quantum processor architecture, in particular the qubit connectivity and topology, still lacks enough discussion, while it potentially has a huge impact on the performance of the quantum algorithms. We perform a quantitative and comprehensive study on the quantum processor performance under different qubit connectivity and topology. We select ten representative design models with different connectivities and topologies from quantum architecture design space and benchmark their performance by running a set of standard quantum algorithms. It is shown that a high-performance architecture almost always comes with a design with a large connectivity, while the topology shows a weak influence on the performance in our experiment. Different quantum algorithms show different dependence on quantum chip connectivity and topologies. This work provides quantum computing researchers with a systematic approach to evaluating their processor design.
Design – a new way to look at old molecules

We discuss how design enriches molecular science, particularly structural biology and bioinformatics. We present two use cases, one in academic practice and the other to design for outreach. The first case targets the representation of ion channels and their dynamic properties. In the second, we document a transition process from a research environment to general-purpose designs. Several testimonials from practitioners are given. By describing the design process of abstracted shapes, exploded views of molecular structures, motion-averaged slices, 360-degree panoramic projections, and experiments with lit sphere shading, we document how designers help make scientific data accessible without betraying its meaning, and how a creative mind adds value over purely data-driven visualizations. A similar conclusion was drawn for public outreach, as we found that comic-book-style drawings are better suited for communicating science to a broad audience.
Waveguide-coupled deterministic quantum light sources and post-growth engineering methods for integrated quantum photonics

Integrated photonic quantum circuits (IPQCs) have attracted increasing attention in recent years due to their widespread applications in quantum information science. While the most envisioned quantum technologies such as quantum communications, quantum computer and quantum simulations have placed a strict constraint on the scalability of chip-integrated quantum light sources. By introducing size-confined nanostructures or crystal imperfections, low-dimensional semiconductors have been broadly explored as chip-scale deterministic single-photon sources (SPSs). Thus far a variety of chip-integrated deterministic SPSs have been investigated across both monolithic and hybrid photonic platforms, including molecules, quantum dots, color centers and two-dimensional materials. With the rapid development of the chip-scale generation of single photons with deterministic quantum emitters, the field of IPQCs has raised new challenges and opportunities. In this paper, we highlight recent progress in the development of waveguide-coupled deterministic SPSs towards scalable IPQCs, and review the post-growth tuning techniques that are specifically developed to engineer the optical properties of these WG-coupled SPSs. Future prospects on stringent requirement for the quantum engineering toolbox in the burgeoning field of integrated photonics are also discussed.
Semantics-aware Content Representations for Reproducible Recommender Systems (SCoRe)

In the traditional categorization of recommendation techniques, content-based methods are often considered as an alternative to the most widely adopted collaborative filtering approaches. Content-based recommender systems suggest items similar to a user profile by matching attributes obtained by processing textual content. In order to deal with natural language ambiguity, semantics-aware representations can help to build more precise representations of users and items, and, in turn, to generate better recommendations. This tutorial (i) presents the most recent trends in the area of semantics-aware content-based recommender systems, including novel representation methods based on knowledge graphs and embedding techniques, (ii) discusses how to implement reproducible pipelines for semantics-aware recommender systems, and (iii) presents a new and comprehensive Python framework called ClayRS to deal with semantics-aware recommender systems.
Two stream Non-Local CNN-LSTM network for the auxiliary assessment of mental retardation

At present, the assessment of mental retardation is mainly based on clinical interview, which requires the participation of experienced psychiatrist and is laborious. Studies have shown that there are correlations between mental retardation and abnormal behaviors (such as, hyperkinetic, tics, stereotypes, etc.). On the basis of this fact, a two stream Non-Local CNN-LSTM network has been proposed to learn the features of upper body behavior and facial expression of patients, thus, to achieve the preliminary screening of mental retardation. Specifically, RGB and optical flow are extracted separately from interview videos, and a two stream network based on contribution mechanism is designed to effectively fuse the information of two kinds of images, which may update the network in a new approach of alternating iteration training to find the optimal model. Besides, by introducing non-local mechanism and adopting it to the network, the global feature sensing can be established more effectively to reduce the background interference for video clip in a short time zone. Experiments on clinical video dataset show that the performance of proposed model is better than other prevalent deep learning methods of behavioral feature learning, the accuracy reaches 89.15% in basic experiment, and is further improved to 89.52% in the supplementary experiment. Furthermore, the experimental results show that this method still has a lot of room for improvement. In general, our work indicates that the proposed model has potential value for the clinical diagnosis and screening of mental retardation.
Brain Segmentation using MATLAB

A surprising mass of tissue wherein some cells duplicates and develops wildly is calledcerebrum growth. It should be identified at a beginning phase utilizing X-ray or CT checked pictures when it is just about as little as conceivable in light of the fact that the growth might perhaps result to malignant growth. This paper, for the most part centers around identifying and limiting the growth locale existing in the cerebrum by proposed philosophy utilizing patient’s X-ray pictures. The proposed procedure comprises of different stages which assume an alternate parts to deliver the examination with at most precision. division is applied to obviously show the growth impacted locale in the X-ray pictures. There are two main objectives of our project: 1) To analyze brain tumor 2) If there is, specify the location of the tumor.
A Critical Review of Molybdenite <scp><sup>187</sup>Re Parent‐<sup>187</sup>Os</scp> Daughter <scp>Intra‐Crystalline</scp> Decoupling in Light of Recent <i>In Situ</i><scp>Micro‐Scale</scp> Observations

Two recent advances in instrumentation convey rhenium and osmium elemental and isotopic distribution in molybdenite at the micro‐scale: (1) NanoSIMS imaging which reveals heterogeneities that could preclude micro‐scale geochronology (spot dating), and (2) reaction‐cell LA‐ICP‐MS/MS which partially resolves overlapping masses of isobaric 187 Re and 187 Os which otherwise confound micro‐scale analyses. The results from each of these measurement principles led respective researchers to conclude that no apparent parent‐daughter decoupling occurs, at least in their chosen samples. This contradicts 20 years of prior macro‐ to micro‐scale Re‐Os molybdenite data. Given these contradictions, we compare their results to our macro‐scale isotope dilution (ID) N‐TIMS measurement results of NIST Reference Material 8599, Henderson molybdenite. We document the homogeneity of the NIST molybdenite, including model age variability, precision of ID N‐TIMS measurement results and statistical treatment. We demonstrate that parent‐daughter decoupling cannot be precluded by visual inspection of NanoSIMS isotopic maps. In addition, we prove mathematically that quantifying reaction‐cell reacted 187 Os by LA‐ICP‐MS/MS is far too imprecise to preclude parent‐daughter decoupling at an extent that hinders high‐precision geochronology, although full 2‐D ablation of a molybdenite crystal surface may yield an accurate Re‐Os age. Nevertheless, LA‐ICP‐MS/MS is useful for documenting the spatial extent of heterogeneous parent‐daughter distribution within individual crystals and supports whole crystal ID N‐TIMS.
Game Technologies and Gamification Techniques in Teaching English: An Analysis of Pedagogical Experience

Game technologies (including computer-based ones) applied for teaching English have long occupied an important and permanent place in the structure of the lesson. They are very helpful in developing intercultural communicative competences as well as increasing motivation and self-organization. The purpose of this study is to objectively show how game methods of work and gamification techniques are actually implemented by modern teachers in English classes in both full-time and distance education. The theoretical significance of the study lies in the analysis of empirical psychological and pedagogical research on the problems of teaching English by both domestic and foreign scientists, on the basis of which the authors have compiled a questionnaire to summarize the experience of English teachers, classify electronic games and gamification techniques in the classroom. The study sample consisted of 48 English teachers with teaching experience from 1 to 13 years, the age range of their students being from 3 to 60 years old. The teachers used the questionnaire to analyze their experience in using gamification techniques and game technologies both in face-to-face and online lessons. All the teachers used game methods of work and gamification techniques in their classes regardless of the students’ age. The games that the teachers included in their lessons could be conveniently classified into didactic games (used by two-thirds of the teachers), multiplayer cooperation games (included by every fifth teacher) and hidden object games with a non-linear plot (used in class by a quarter of the teachers in the sample). Most of the teachers who used the gamification techniques noted a steady increase in students’ motivation in the classroom. The practical significance of the research results lies in the possibility of building an individual trajectory of self-education and professional development of English language teachers both in basic and in supplementary education. The generalized practical experience of the teachers who participated in the study can be used as the basis for methodological and pedagogical recommendations for improving informational and communicational competences and pedagogical skills of English language teachers.
A Multistage Framework With Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation

In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem and propose a novel multi-stage framework to solve real-world situations when the target data are unlabeled and arriving online sequentially in batches. Most of the traditional manifold-based methods on the OUDA problem focus on transforming each arriving target data to the source domain without sufficiently considering the temporal coherency and accumulative statistics among the arriving target data. In order to project the data from the source and the target domains to a common subspace and manipulate the projected data in real-time, our proposed framework institutes a novel method, called an Incremental Computation of Mean-Subspace (ICMS) technique, which computes an approximation of mean-target subspace on a Grassmann manifold and is proven to be a close approximate to the Karcher mean. Furthermore, the transformation matrix computed from the mean-target subspace is applied to the next target data in the recursive-feedback stage, aligning the target data closer to the source domain. The computation of transformation matrix and the prediction of next-target subspace leverage the performance of the recursive-feedback stage by considering the cumulative temporal dependency among the flow of the target subspace on the Grassmann manifold. The labels of the transformed target data are predicted by the pre-trained source classifier, then the classifier is updated by the transformed data and predicted labels. Extensive experiments on six datasets were conducted to investigate in depth the effect and contribution of each stage in our proposed framework and its performance over previous approaches in terms of classification accuracy and computational speed. In addition, the experiments on traditional manifold-based learning models and neural-network-based learning models demonstrated the applicability of our proposed framework for various types of learning models.
Patterns for legal compliance checking in a decidable framework of linked open data

Abstract This paper presents an approach for legal compliance checking in the Semantic Web which can be effectively applied for applications in the Linked Open Data environment. It is based on modeling deontic norms in terms of ontology classes and ontology property restrictions. It is also shown how this approach can handle norm defeasibility. Such methodology is implemented by decidable fragments of OWL 2, while legal reasoning is carried out by available decidable reasoners. The approach is generalised by presenting patterns for modeling deontic norms and norms compliance checking.
RMDC: Rotation-mask deformable convolution for object detection in top-view fisheye cameras

Object detection in top-view fisheye cameras is more difficult than standard cameras. This article summarizes the issues regarding object detection accuracy in top-view fisheye cameras into two challenges: Rotation and Distortion. There is a lack of methods to deal with both challenges simultaneously. To solve this problem, we propose the Rotation-Mask Deformable Convolution, named RMDC, which rotates convolution filters adaptively and introduces the Center-Fixed Deformable Convolution. The RMDC improves the learning ability of the convolution kernels for rotated objects in top-view fisheye images and addresses the spatial distortion problem. The effectiveness of the RMDC is verified on the SEU-fisheye which contains 10233 top-view fisheye images and annotates 127053 people. Finally, we confirm the optimal network structure in the baseline with the RMDC.
Industrial Control Systems Security via Runtime Enforcement

With the advent of Industry 4.0 , industrial facilities and critical infrastructures are transforming into an ecosystem of heterogeneous physical and cyber components, such as programmable logic controllers , increasingly interconnected and therefore exposed to cyber-physical attacks , i.e., security breaches in cyberspace that may adversely affect the physical processes underlying industrial control systems . In this article, we propose a formal approach based on runtime enforcement to ensure specification compliance in networks of controllers, possibly compromised by colluding malware that may locally tamper with actuator commands, sensor readings, and inter-controller communications. Our approach relies on an ad-hoc sub-class of Ligatti et al.’s edit automata to enforce controllers represented in Hennessy and Regan’s Timed Process Language . We define a synthesis algorithm that, given an alphabet 𝒫 of observable actions and a timed correctness property e , returns a monitor that enforces the property e during the execution of any (potentially corrupted) controller with alphabet 𝒫, and complying with the property e . Our monitors do mitigation by correcting and suppressing incorrect actions of corrupted controllers and by generating actions in full autonomy when the controller under scrutiny is not able to do so in a correct manner. Besides classical requirements, such as transparency and soundness , the proposed enforcement enjoys deadlock- and diverge-freedom of monitored controllers, together with scalability when dealing with networks of controllers. Finally, we test the proposed enforcement mechanism on a non-trivial case study, taken from the context of industrial water treatment systems, in which the controllers are injected with different malware with different malicious goals.
Applications of augmented reality and virtual reality on computer-assisted teaching for analytical sketching of architectural scene and construction

Analytical sketching is a method to document an inspired understanding from direct observations and analysis of an architectural environment through freehand sketching. A course dedicated to teaching analytical sketching has encountered problems of how to integrate different teaching activities involving in-class lectures and on-site practices. A mobile learning application (Architectural Sketching) was previously developed to bring the in-class instruction on hand to the field. However, how to assist in the development of key sketching ability of interrelating conceptual architectural configurations and their perceptual spatial experiences through mobile learning remains unexplored. This study proceeds from prior action research and reports the experimental implementation of augmented reality and virtual reality to a mobile learning application. To teach architectural scene sketching, live demonstrations of on-site sketching were provided in different interactive display methods of viewing as regular video, interactive 180-degree video on handheld devices, and 180-degree VR video with VR headset. For architectural construction, augmented reality and 3D holograms, along with 3D animations of geometrical analysis, were used to create digital learning tools. Preference rating and preference scales were established from students’ responses. The results reveal that the user-friendly interactivity and functional realism enhance the mobile learning applications to assist on-site sketching training.