Reviewerly/exHarmony huggingface dataset:
AraScore: Investigating Response-Based Arabic Short Answer Scoring

There are more than 80 million students in the Arab world. Students take tests and examinations as an intrinsic part of their educational journey to assess the quality of learning and understanding of the examined material. Exams require an immense amount of resources to be conducted, but even a bigger amount of resources to be scored. Scoring of exams and papers takes up enormous time and effort from teachers all over the Arab world. This time and effort can be better utilized in other teaching activities that would elevate the quality of learning in the region. For that reason, the work presented in this paper investigates multiple supervised learning approaches for the domain of Arabic short answer scoring. Moreover, due to the scarcity of Arabic datasets, and to our knowledge, there is only one publicly available dataset for this specific task(AR-ASAG). Thus, we introduce a new publicly available dataset AraScore-Dataset. The model proposed is evaluated on 3 different datasets: 1-AraScore-Dataset 2-AR-ASAG 3-Two translated answer sets taken from the Hewlett Foundation SAS dataset. Based on the investigation, the most effective approach for Arabic automated short answer scoring is proposed, and the performance of using our dataset is compared to other publicly available datasets. The aim is to scale efficient and unbiased content scoring applications across different Arabic educational domains. The results showed that our newly developed response-based system and AraScore-Dataset have achieved state-of-the-art performance on Arabic Automated Short Answer Scoring.
Modular Multi-Sensor Fusion: A Collaborative State Estimation Perspective

Fusing information from multiple sensors in aprobabilistic framework can significantly improve the estimation of the system's states, allow for redundancy, and for adaptations in dynamic environments. However, the computation complexity increases with the number of sensors and the dimension of the estimated states. In this letter, we show that techniques used in the field of CSE for distributed estimators on decoupled agents can be applied on local estimators to decouple states from different sensors. Bridging the gap between these domains allows us to propose a novel unified modular multi-sensor fusion strategy for recursive filters that (i) achieves constant maintenance complexity in propagation and private update steps, (ii) supports any-sensor-to-any-sensor observations, (iii) isolates state propagation supporting high rates for propagation sensors, (iv) isolates private sensor updates, and (v) isolates joint updates requiring only the participating sensors' estimates all while maintaining probabilistic consistency. We port existing CSE strategies into MMSF formulations and compare them as well as a classical MMSF approach against the proposed one in terms of execution time, accuracy, and credibility on real data.
Crowd estimation using key‐point matching with support vector regression

The crowd behaviour understanding and density estimation are some of the fast-growing fields in video surveillance. There are many techniques (detection and regression) that are used as the method of crowd analysis and estimation. In the present approach, SVR (support vector regression) is used as the basic analysis technique and the novel key-point matching with SURF (speedup robust feature) is used as the feature extractor for moving objects in the video. The traditional linear regression methods used mainly key-point as one of the statistical features instead of matching with consecutive frames, but we used the magnitude of the optical flow for foreground object extraction instead of inter-frame difference. The combination of the optical flow of foreground objects and key-point matching generates new features apart from conventional features such as areas and corners. In this new approach, key-point pairing with linear regression is tested with the PETS2009 dataset, and performance is compared with the existing approaches.
T-SNE Based on Simulated Plant Growth Optimization Algorithm with t-Distribution Parameters

SNE is a nonlinear dimension reduction method in the field of manifold learning in machine learning. In the field of high dimensionality, there will be some “noise” because the amount of de-space data is too large, that is, interference with the required data, so we need to reduce the dimension of these data. Although we obtain less information than high-dimensional, some features of the obtained data will become obvious for better observation and use. The loss function is KL divergence. The traditional method is solved by gradient descent method (GD) or stochastic gradient descent method (SGD) this method has some defects. In order to solve these defects, a t-SNE (t-distributed stochastic neighborhood embedding) algorithm is selected to simulate plant growth optimization algorithm.
The Effect of Restorative Environment and Presence Based on Virtual Reality for Anxiety and Depression

This research designed several virtual environments and interactive activities, explored how to design and develop VR psychological intervention scenarios for people with anxiety and depression mood, and optimized related products or services. Study 1 adopted 2 (pre-test and post-test) *2 (anxiety and depression state) *4 (VR scenarios) experimental design and Study 2 adopted 2 (pre-test and post-test) *5 (VR scenarios) experimental design. The results showed that: (1) restorative and present VR scenarios were effective in intervening anxiety and depression, especially for improving state anxiety; (2) environmental elements and interactive activities were key factors influencing the improvement of anxiety and depression; (3) EEG and EMG could study the mechanism of VR scenarios to provide a theoretical basis for designing VR psychological intervention tools.
Understanding Developer Security Archetypes

As software systems penetrate our everyday lives, security has risen to be a key concern. Despite decades of research leading to new tools and practices for writing secure code, achieving security as a key attribute remains highly challenging. We observe that much of the literature considers developers to be homogeneous and interchangeable. The differing circumstances of developers that might play a role in the writing of secure code have not been clearly defined. In this position paper we introduce the concept of developer security archetypes. Specifically, we suggest two key factors: developers' personal interest in security, and the support that developers receive from their environment. Together, these two dimensions define four archetypes which can be uniquely characterized. By distinguishing developer archetypes, we seek to better understand how developers perceive security-related issues in systems development, as well as how to better support them.
UAV Path Planning Using on-Board Ultrasound Transducer Arrays and Edge Support

As UAVs become increasingly autonomous with decreased physical size, it will become more difficult for human operators to perform a demanding job with many challenges. These challenges include keeping track of UAVs in low-level airspace while considering the psychological state of the operators. The main objective of the operator is to eliminate the high risk of UAV collisions with each other or with unexpected obstacles while in flight. By using AI, the operators psychological state can be assessed in a non-intrusive manner, while equipping the system with the capability to take over and activate the teleoperated mode. In particular, affective computing sensing can aid the user in the teleoperated mode when combined with a particular control technique. A control technique is proposed on the basis of sensory measurements for adjusting the velocity and direction, therefore averting obstacle collisions. Ultrasound sensors are used to provide more real-time local data, however, these sensors can be vulnerable to missing data. To mitigate the issue of sensors with missing data, GANs can generate synthetic values that can be used in an AI-based prediction algorithm to direct the UAV on a path. In this paper, real-time data is collected through on-board Ultrasound sensors mounted on a commercial UAV. The collected data is reported to the edge server where the GAN is implemented along with the ML algorithm to predict the path. The results demonstrate the effectiveness of this approach with an accuracy of more than 96.96%.
Boiling Mind - A Dataset of Physiological Signals during an Exploratory Dance Performance

The relationship between audience and performers is crucial to what makes live events so special. The aim of this work is to develop a new approach amplifying the link between audiences and performers. Specifically, we explore the use of wearable sensors in gathering real-time audience data to augment the visuals of a live dance performance. We used the J!NS MEME, smart glasses with integrated electrodes enabling eye movement analysis (e.g. blink detection) and inertial motion sensing of the head (e.g. nodding recognition). This data is streamed from the audience and visualised live on stage during a performance, alongside we also collected heart rate and eye gaze of selected audience. In this paper we present the recorded dataset, including accelerometer, electrooculography(EOG), and gyroscope data from 23 audience members.
When form deviates from the norm: attitudes towards old and new vernacular features and their impact on the perceived credibility and usefulness of Facebook consumer reviews

We investigate the impact of deviations from Standard Dutch in the form of old (e.g. tussentaal) and new (e.g. emoji and chatspeak) vernacular features on consumer perceptions of the credibility and usefulness of Facebook reviews in a 2 (positive, negative) × 2 (old vernacular features or not) × 2 (new vernacular features or not) between-subjects vignette-based experiment with 483 Flemish Facebook users. Additionally, we probe into our respondents' attitudes towards these non-standard features in this setting. While the use of Standard Dutch is evaluated positively and generally associated with the highest credibility and usefulness ratings, there does seem to be a certain tolerance towards non-standard language, especially new vernacular features. Our findings show that the use of old vernacular features, on the other hand, is evaluated rather negatively by our respondents and leads to slightly lower message credibility ratings (compared to strict adherence to Standard Dutch). Source credibility, however, was not significantly influenced by the presence of non-standard language features. While Standard Dutch can be considered the benchmark for written communication in this setting as well, it appears to be acceptable for consumers to be creative and express their emotions via new vernacular features.
Learning to Identify Narrators in Classical Arabic Texts

One widespread historical method of transmitting and recording information about important events and people in the Middle East is the narration-based method. In this method, each saying about a person or event is transmitted from person to person until a systematic collector records and compiles such sayings in a stable collection. At each stage of transmission, the narrator not only transmits the saying but also the person he got it from going back to the earliest narrator. Identifying each narrator in these collections is important to better measure the accuracy of the narrations and identify the date and geographies of their circulation. In this work, we propose a natural language processing technique to automate the identification of narrators in classical Arabic texts. Our proposed technique consists of two models: 1) a model for detecting the narrators in the text, and 2) a model for linking narrators to their biographies. We train our two models on a large collection of annotated classical Arabic texts and achieve F1-scores of 96.15% and 95.74% for narration detection and linking respectively.
NLP for Chatbot Application

The chatbot is one of the increasing number applications in the era of conversational series. It is a virtual application that can efficiently interact with any human being using the deep natural language processing skills. In NLP, for chatbot application, the various techniques needed for chatbot using NLTK tool are explained and implemented. The process of converting the text to numerical value is called text embedding. In NLTK tool, various text embedding tools are available such as TF-IDF vectorization and bag of words. Deep NLP is an efficient way to implement the chatbot. Thus the chatbot is implemented with sequence-to-sequence networks.
Matrix Gaussian Mechanisms for Differentially-Private Learning

The wide deployment of machine learning algorithms has become a severe threat to user data privacy. As the learning data is of high dimensionality and high orders, preserving its privacy is intrinsically hard. Conventional differential privacy mechanisms often incur significant utility decline as they are designed for scalar values from the start. We recognize that it is because conventional approaches do not take the data structural information into account, and fail to provide sufficient privacy or utility. As the main novelty of this work, we propose <i>Matrix Gaussian Mechanism</i> (MGM), a new <inline-formula><tex-math notation="LaTeX">$ (\epsilon,\delta)$</tex-math></inline-formula> -differential privacy mechanism for preserving learning data privacy. By imposing the unimodal distributions on the noise, we introduce two mechanisms based on MGM with an improved utility. We further show that with the utility space available, the proposed mechanisms can be instantiated with optimized utility, and has a closed-form solution scalable to large-scale problems. We experimentally show that our mechanisms, applied to privacy-preserving federated learning, are superior than the state-of-the-art differential privacy mechanisms in utility.
SPARQL Query Generator (SQG)

Abstract This paper describes a program—SPARQL Query Generator (SQG)—which takes as input an OWL ontology, a set of object descriptions in terms of this ontology and an OWL class as the context, and generates relatively large numbers of queries about various types of descriptions of objects expressed in RDF/OWL. The intent is to use SQG in evaluating data representation and retrieval systems from the perspective of OWL semantics coverage. While there are many benchmarks for assessing the efficiency of data retrieval systems, none of the existing solutions for SPARQL query generation focus on the coverage of the OWL semantics. Some are not scalable since manual work is needed for the generation process; some do not consider (or totally ignore) the OWL semantics in the ontology/instance data or rely on large numbers of real queries/datasets that are not readily available in our domain of interest. Our experimental results show that SQG performs reasonably well with generating large numbers of queries and guarantees a good coverage of OWL axioms included in the generated queries.
PDE-guided reservoir computing for image denoising with small data

While network-based techniques have shown outstanding performance in image denoising in the big data regime requiring massive datasets and expensive computation, mathematical understanding of their working principles is very limited. Not to mention, their relevance to traditional mathematical approaches has not attracted much attention. Therefore, we suggest how reservoir computing networks can be strengthened in combination with conventional partial differential equation (PDE) methods for image denoising, especially in the small data regime. Given image data, PDEs generate sequential datasets enhancing desired image features, which provide the network with a better guideline for training in reservoir computing. The proposed procedure, reservoir computing in collaboration with PDEs (RCPDE), offers a synergetic combination of data-driven network-based methods and mathematically well-established PDE methods. It turns out that RCPDE outperforms both the usual reservoir computing and existing PDE approaches in image denoising. Furthermore, RCPDE also excels deep neural networks such as a convolutional neural network both in quality and in time in the small data regime. We believe that RCPDE reveals the great potential of reservoir computing in collaboration with various mathematically justifiable dynamics for better performance as well as for better mathematical understanding.
Transform DOI system into a science hub

To bestow a worthwhile value to DOI assignations, I suggest the establishment of a unique persistent DOI database (for e.g., as a DOI hub, DOI library, or DOI indexer) in which all the DOI assigned by publishers and journals will be listed in one and same place with basics bibliographic metadata and complete citation information, including the DOI link itself, authors' names, manuscripts' titles, publishing source, date of publication, and ideally abstracts if available. As a result, when a DOI is searched in the DOI hub, full bibliographic information should be retrievable regardless of its status in the publishing source. Basic indexation information and metadata associated with published articles will be always accessible and findable independently from the publishing sources. A unique, general and long-term preserved DOI hub will make it easy to search, find and cite scientific literature from the various scientific fields even if a journal or publisher ceases its publishing activity.
Perspective paper: Can machine learning become a universal method of laser photonics?

Machine learning methods are being successfully applied in various domains of human activity, including optics. Can they become a universal instrument capable of improving user aspect of photonic devices? Is their application a necessary element of such improvement? Answers to fundamental questions arising from application of machine learning methods in ultrashort-pulsed lasers are discussed. Future prospects and current fundamental limitations of such methods are analysed.
Learning to Map Degrees of Freedom for Assistive User Control

This paper presents a novel approach to shared control for an assistive robot by adaptively mapping the degrees of freedom (DoFs) for the user to control with a low-dimensional input device. For this, a convolutional neural network interprets camera data of the current situation and outputs a probabilistic description of possible robot motion the user might command.
Designing Augmented Reality Learning Systems with Real-Time Tracking Sensors

This study aims to develop an advanced augmented reality (AR) system by integrating an AR system and a real-time tracking system. Lab experiments are critical parts of engineering education, and it is possible to revolutionize engineering labs to be self-paced learning by using AR technology. However, the current hand gesture interactions to communicate with AR devices have shown several limitations. Hence, in this study, we developed a location-based AR system by integrating a real-time tracking sensor and an AR device. This new AR learning system could reduce transition time between learning modules and improve learners’ interaction between the modules and an AR device.
Agile Iteration Reviews in a Project Course: A key to Improved Feedback and Assessment Practice

Agile development is increasingly taught at universities worldwide. Project courses are redesigned in order to better fit these methods, both with respect to content taught and how courses are organised. This position paper builds on experience with reviews as a feedback practice in a bachelor level project course. Reviews are a key element in agile development, but there has been little discussion in the software engineering education literature on the role of such reviews in improving feedback and assessment. Through examples of course improvement work in a course with about 140 students in 24 teams, we show how review practices are tailored to better comply with principles of good feedback practice which intend to empower students and help self-regulate learning. We argue that reviews can provide formative assessment and improve the learning outcome. Finally, we conclude with five lessons learned from three years of continuous improvement.
Usage of Cloud Storage for Data Management in the Built Environment

Nowadays, all organization without exception depend on data, its management and storage to ensure success is achieved on a project. Data management involves the process of collecting, storing, sharing, controlling, and retrieval of information contained in the project coming from different sources. These data need to be stored properly to serve as a reference whenever needed (availability) for smooth project execution. The aim of this study is to discuss the capabilities of cloud storage for data management in the built environment during various projects execution phases. The study adopted a literature review methodology to draw knowledge on the way cloud storage operates as well as the services it provides. It is evident from the reviewed literature that data storage is highly important for data record and protection during the project lifecycle. This is because it gives maximum security to data owners compared to traditional data storage facilities that are available. The study concluded that a good data storage platform will help project parties to avoid data and information loss, scattered data, data incompleteness, project interruption due to data unavailability as well as theft of data. The study recommended that construction parties need to have a knowledge of the different technologies required for cloud data storage to improve data management on built environment projects.
One-Layer vs. Two-Layer SOM in the Context of Outlier Identification: A Simulation Study

In this work, we applied a stochastic simulation methodology to quantify the power of the detection of outlying mixture components of a stochastic model, when applying a reduced-dimension clustering technique such as Self-Organizing Maps (SOMs). The essential feature of SOMs, besides dimensional reduction into a discrete map, is the conservation of topology. In SOMs, two forms of learning are applied: competitive, by sequential allocation of sample observations to a winning node in the map, and cooperative, by the update of the weights of the winning node and its neighbors. By means of cooperative learning, the conservation of topology from the original data space to the reduced (typically 2D) map is achieved. Here, we compared the performance of one- and two-layer SOMs in the outlier representation task. The same stratified sampling was applied for both the one-layer and two-layer SOMs; although, stratification would only be relevant for the two-layer setting—to estimate the outlying mixture component detection power. Two distance measures between points in the map were defined to quantify the conservation of topology. The results of the experiment showed that the two-layer setting was more efficient in outlier detection while maintaining the basic properties of the SOM, which included adequately representing distances from the outlier component to the remaining ones.