Reviewerly/exHarmony huggingface dataset:
Automatic Question Generation: A Systematic Review

Today's educational systems need an efficient tool to perform competently assessment of students on their major concepts they learnt from study material. Preparing a set of questions for assessment can be time consuming for teachers while getting questions from external sources like assessment books or question bank might not be relevant to content studied by students. Automatic Question Generation (AQG) is the technique for generating a right set of questions from a content, which can be text. Automatic question generation (QG) is a very important yet challenging problem in NLP. It is defined as the task of generating syntactically sound, semantically correct and relevant questions from several input formats like text, a structured database or a knowledge base. Question generation can be naturally applied in many domains such as MOOC, automated help systems, search engines, chatbot systems (e.g. for customer interaction), and healthcare for analyzing mental health. AQG has the got the immense attention from researchers in a field of computational linguistics. The review paper focuses on the recants on-going research on NLP for generating automatic questions from the text through various methods.
Real-time sign languages character recognition

Deaf people face many challenges to communicate with the hearing world. Many studies and industrial solutions came up with interpretation from sign language to normal text but most of them are limited either to static images for letters or static animated character plays the word in motion. Therefore, this research worked on enhancing the feasible algorithms that have been used like image detection, image processing techniques and image translation methods. The contribution consists of two parts, the first one is using our data set, and the second one is proposing a new solution by extracting SURF features after using image filtering based on the existing methods to accelerate the translation process for the long sentences. Experimental results over our data set report improved accuracy compared with other studies in terms of efficiency time and recognition rate of sign language character recognition.
U-Net based image segmentation of mesenchymal stem cells

Cell-based therapy is an attractive strategy for the long-term management of various chronic diseases. Mesenchymal stem cells (MSCs) are a heterogeneous group of cells that have demonstrated clinically relevant therapeutic effects. The proliferative and therapeutic potential of MSCs can be characterized by the culture quality, which is reflected by their morphological phenotype. Morphological analysis has been a robust method for monitoring culture quality, but visual inspection is subjective and time-consuming. Our goal is to develop an automated algorithm to segment MSCs for an objective, non-invasive, and rapid cell assessment. We have built an algorithm to segment MSCs using U-Net architecture trained with 71 phase-contrast micro- graphs having 472 cells. MSC culture images are pre-processed and given as inputs to the trained U-Net model that provides a prediction map for cell segmentation. The U-Net output is then post-processed using morphological operations to get rid of false positive cell detections. Results were validated using visual inspection from MSC experts. Our independent test dataset of 36 images consisted of 186 cells. We obtained a sensitivity of 0.742 and a precision of 0.789 for cell detection and a Dice-Sorensen score of 0.823 ± 0.051 for segmentation. The proposed algorithm shows the potential to segment MSCs with accuracy and robustness higher than conventional U-Net. Automated cell segmentation would enable rapid quantification of cytomorphological features and may also drive stem cell quality control processes.
Choice of mutually unbiased bases and outcome labeling affecting measurement outcome secrecy

Mutually unbiased bases (MUBs) are a crucial ingredient for many protocols in quantum information processing. Measurements performed in these bases are unbiased to the maximally possible extent, which is used to prove the randomness or secrecy of measurement results. In this work we show that certain properties of sets of MUBs crucially depend on their specific choice, including, somewhat surprisingly, measurement outcome labeling. If measurements are chosen in a coherent way, the secrecy of the result can be completely lost for specific sets of MUB measurements but partially retained for others. This could potentially impact a broad spectrum of applications where MUBs are utilized.
Sign Language and Emotion Understanding

Sign language is the primary form of communication of the deaf community. It is one of the only channels of communication between the hearing impaired community and the rest of society. While there are many existing SLR systems that focus on the recognition of manual gestures, few consider the non manual components of sign language, such as facial expressions. The role of these non manual features are comparable to pitch, intonation and nuances that are observed in spoken language. Our prototype combines manual SLR with emotion recognition to form a singular system that can recognise both facial expressions and sign language gestures.
Analysing the Correlation of Geriatric Assessment Scores and Activity in Smart Homes

A continuous monitoring of the physical strength and mobility of elderly people is important for maintaining their health and treating diseases at an early stage. However, frequent screenings by physicians are exceeding the logistic capacities. An alternate approach is the automatic and unobtrusive collection of functional measures by ambient sensors. In the current publication, we show the correlation among data of ambient motion sensors and the well-established mobility assessments Short-Physical-Performance-Battery, Tinetti and Timed Up & Go. We use the average number of motion sensor events as activity measure for correlation with the assessment scores. The evaluation on a real-world dataset shows a moderate to strong correlation with the scores of standardised geriatrics physical assessments.
Structure of an ontology used in a test generation environment

The paper presents the UMLOntology structure used to automatically generate test questions. It also considers the algorithm for automatic generation of questions in the Test Generation Environment and discusses supplementary annotation elements added to the ontology. Moreover, the research presents examples of generated meaningful test questions by the Test Generation Environment using the implemented UMLOntology.
Five considerations for the transposition and application of Article 17 of the DSM Directive

Five considerations for the transposition and application of Article 17 of the DSM Directive Eleonora Rosati Eleonora Rosati Email: eleonora@e-lawnora.com https://orcid.org/0000-0002-7357-5978 Search for other works by this author on: Oxford Academic Google Scholar Journal of Intellectual Property Law & Practice, Volume 16, Issue 3, March 2021, Pages 265–270, https://doi.org/10.1093/jiplp/jpab051 Published: 03 March 2021
Observation of the tradeoff between internal quantum nonseparability and external classical correlations

The monogamy relations of entanglement are highly significant. However, they involve only amounts of entanglement shared by different subsystems. Results on monogamy relations between entanglement and other kinds of correlations, and particularly classical correlations, are very scarce. Here we experimentally observe a tradeoff relation between internal quantum nonseparability and external total correlations in a photonic system and found that even purely classical external correlations have a detrimental effect on internal nonseparability. The nonseparability we consider, measured by the concurrence, is between different degrees of freedom within the same photon, and the external classical correlations, measured by the standard quantum mutual information, are generated between the photons of a photon pair using the time-bin method. Our observations show that to preserve the internal entanglement in a system, it is necessary to maintain low external correlations, including classical ones, between the system and its environment.
Domain Adaptation in Dialogue Systems using Transfer and Meta-Learning

Current generative-based dialogue systems are data-hungry and fail to adapt to new unseen domains when only a small amount of target data is available. Additionally, in real-world applications, most domains are underrepresented, so there is a need to create a system capable of generalizing to these domains using minimal data. In this paper, we propose a method that adapts to unseen domains by combining both transfer and meta-learning (DATML). DATML improves the previous state-of-the-art dialogue model, DiKTNet, by introducing a different learning technique: meta-learning. We use Reptile, a first-order optimization-based meta-learning algorithm as our improved training method. We evaluated our model on the MultiWOZ dataset and outperformed DiKTNet in both BLEU and Entity F1 scores when the same amount of data is available.
Kernel correlation filter tracking strategy based on adaptive fusion response map

Aiming at the problem that the tracking performance of the traditional kernel correlation filter tracking algorithm is easy to be affected by illumination variation, occlusion and motion blur during tracking, an improved tracking strategy is proposed. A new Histogram of Hue Gradient (HHG) feature is designed, and the new HOG-HHG feature is obtained by connecting the HOG and the HHG in series. Two features, CN and HOG-HHG, are extracted respectively, and two kernel correlation filter classifiers are constructed base on the two features above to establish the corresponding response maps of the tracking scenes, respectively. The response maps are fused adaptively to improve the tracking robustness to the complex situations in the tracking process. The updating strategy of the target model is designed based on peak sidelobe ratio (PSR) and its difference, and the adaptive thresholds are used to improve the stability of the target model. Simulation results show that the proposed method has better tracking adaptability to the illumination variation, occlusion and motion blur. Both the precision and the success rate can be enhanced.
Modernization of a cooling water control system for the production of plastic pipes using fuzzy logic

Fuzzy logic is successfully used in the area of control systems where different problems are decided in flexible way leading to more efficient systems’ performance. Such approach is suitable when the modeled system is complex and some parameters cannot be defined precisely. In this paper a fuzzy model of improved cooling water control system utilized in plastic pipes production is proposed. Its aim is more effective regulation of the freon compressor load to be achieved that influences on economy in energy consumption. The developed model is verified through simulation in FisPro environment where the knowledge base, fuzzy membership functions and inference engine are modeled.
Augmented Reality Navigation in Spine Surgery

Spine has several complicated joints and nerves that need to look after well during the surgery procedure. Spine surgery in some ways easily creates scoliosis, degenerative disc disease, spinal stability problem. All these procedures need highly skilled operators with a specific program. The previous traditional program has, unfortunately, plenty of variations in image registration over precision rate and technical issues. As the technical problem, human mistakes, and shortage of standard equipment happen widely, the aim of this research is to increase the surgeon's efficiency in navigation location assurance and to decrease the exposure of radiation for patients and operators. This study is a review-based approach. A variety of methods are applied to evaluate the different works conducted across the similar circumstances. 30 state of the art publications are categorised to elaborate and further elaborate the utility of the Augmented Reality (AR) navigation system in spine surgery. The criterions recognised for verifying AR navigation in spine surgery are system recognition, system comparison and its robustness. This paper will provide an analysis of the navigation systems methodology to spine surgery by Augmented Reality in present studies and propose an improved navigation framework that emphasises the characteristics and effectiveness of AR in regard of accuracy, radiation exposure and reliability in surgery compared to the present surgical procedure.
Sentiment Analysis of Colloquial Arabic Tweets with Emojis

Sentiment Analysis is the process of classifying data according to its sentiment polarity as positive or negative or multiclass. In this paper, our goal is twofold: Firstly, to experiment and evaluate different approaches to dialectal Arabic sentiment analysis, including various classifiers and features. Secondly, we have curated a dataset of Arabic dialect tweets to validate its usefulness via utilization in the experiments. We collected the dataset via Twitter, the Twitter Arabic Dialect dataset, and its subset (which we used in this paper) the Twitter Arabic Dialect Emoji (TADE) datasets. TADE is automatically annotated for sentiment utilizing emojis encountered in the tweets. Our method favors real-word application of emoji as opposed to the theoretic meaning of the emoji. We use traditional (shallow) and deep learning classifiers for sentiment analysis of the TADE dataset. We experiment with good shallow classifiers including Gradient Boosting, Logistic Regression, Nearest Centroid, Decision Tree, MultinomialNB, SVM, XGB, Random Forest, AdaBoost, and a voting classifier. For the deep learning classifiers, we use MLP and CNN classifiers. Further, we experiment with TF-IDF and word embeddings for feature selections.
Investigating genre distinctions through discourse distance and discourse network

The notion of genre has been widely explored using quantitative methods from both lexical and syntactical perspectives. However, discourse structure has rarely been used to examine genre. Mostly concerned with the interrelation of discourse units, discourse structure can play a crucial role in genre analysis. Nevertheless, few quantitative studies have explored genre distinctions from a discourse structure perspective. Here, we use two English discourse corpora (RST-DT and GUM) to investigate discourse structure from a novel viewpoint. The RST-DT is divided into four small subcorpora distinguished according to genre, and another corpus (GUM) containing seven genres are used for cross-verification. An RST (rhetorical structure theory) tree is converted into dependency representations by taking information from RST annotations to calculate the discourse distance through a process similar to that used to calculate syntactic dependency distance. Moreover, the data on dependency representations deriving from the two corpora are readily convertible into network data. Afterwards, we examine different genres in the two corpora by combining discourse distance and discourse network. The two methods are mutually complementary in comprehensively revealing the distinctiveness of various genres. Accordingly, we propose an effective quantitative method for assessing genre differences using discourse distance and discourse network. This quantitative study can help us better understand the nature of genre.
Empathy VR and the BeAnotherLab collective

BeAnotherLab were recipients of the 2017 STARTS Prize Honorary Mention for their work Library of Ourselves , an interdisciplinary and distributed project to create transformative encounters between communities in conflict. Described by the collective as an open book of embodied narratives, the ongoing archive of the Library of Ourselves is composed of stories recorded from a first-person perspective of individuals, belonging to various communities. The art collective based in Barcelona was formed almost a decade ago and consists of nine members from wide-ranging backgrounds. In this article I will discuss their engagement with empathy VR. I will draw on my interviews with a number of members of the collective and my meetings with them in Barcelona. I also discuss the significance of their work as an art collective and the impact this has on their arts practice.
Symbolic Integration by Integrating Learning Models With Different Strengths and Weaknesses

Integration is indispensable, not only in mathematics, but also in a wide range of other fields. A deep learning method has recently been developed and shown to be capable of integrating mathematical functions that could not previously be integrated on a computer. However, that method treats integration as equivalent to natural language translation and does not reflect mathematical information. In this study, we adjusted the learning model to take mathematical information into account and developed a wide range of learning models that learn the order of numerical operations more robustly. In this way, we achieved a 98.80% correct answer rate with symbolic integration, a higher rate than that of any existing method. We judged the correctness of the integration based on whether the derivative of the primitive function was consistent with the integrand. By building an integrated model based on this strategy, we achieved a 99.79% rate of correct answers with symbolic integration. In summary, we have developed a more accurate method of selecting the correct model than the existing method by judging the result of symbolic integration based on whether the output of the model equals the input formula when the output is differentiated.
Lightweight Image Restoration Network for Strong Noise Removal in Nuclear Radiation Scenes

In order to remove the strong noise with complex shapes and high density in nuclear radiation scenes, a lightweight network composed of a Noise Learning Unit (NLU) and Texture Learning Unit (TLU) was designed. The NLU is bilinearly composed of a Multi-scale Kernel Module (MKM) and a Residual Module (RM), which learn non-local information and high-level features, respectively. Both the MKM and RM have receptive field blocks and attention blocks to enlarge receptive fields and enhance features. The TLU is at the bottom of the NLU and learns textures through an independent loss. The entire network adopts a Mish activation function and asymmetric convolutions to improve the overall performance. Compared with 12 denoising methods on our nuclear radiation dataset, the proposed method has the fewest model parameters, the highest quantitative metrics, and the best perceptual satisfaction, indicating its high denoising efficiency and rich texture retention.
EDUCATIONAL MICROENVIRONMENT AS A CONDITION FOR THE GIFTEDNESS DEVELOPMENT IN SENIOR PRESCHOOL AGE

The article is devoted to the problem of conditions for the development of gifted children in kindergarten. The main attention is paid to the peculiarities of the modern educational microenvironment that contributes to the development of gifted older preschoolers. The article presents the results of a study in which 113 children of 6-7 years old with high intellectual abilities took part. Children attended preparatory groups for kindergartens with different microenvironments. A comparative analysis of the developmental features of extraordinary preschoolers was carried out in an enriched educational microenvironment (40 children) and in a typical microenvironment (73 children). The assessment of intellectual development was carried out using the methodology for diagnosing intellectual abilities of MEDIA, intellectual and creative activity - using a diagnostic game lesson. As a result of univariate analysis of variance, it was found that the educational microenvironment, which allows an intellectually gifted preschooler to realize his need for exploring the world and show his creative abilities, makes a significant contribution to the development of his cognitive initiative and intellectual and creative activity (10.6% of the variance of this indicator at p &lt;0.000), thereby contributing to the favorable development of the potential of children's giftedness. It is shown that the features of the content of education (interdisciplinary nature, cognitive complexity and variety of topics), the dominance of research and creative activity, the relationship of cooperation, the encouragement of creative activity and independent search are specific characteristics of the developing educational microenvironment for gifted preschoolers. It is substantiated that the creation of a developing educational microenvironment for gifted preschoolers is impossible without special training of educators aimed at teaching them to accept and appreciate the uniqueness of the child, to help him interact with other children, to discover his hobbies and to explore the world.
Optimal allocation of quantum resources

The optimal allocation of resources is a crucial task for their efficient use in a wide range of practical applications in science and engineering. This paper investigates the optimal allocation of resources in multipartite quantum systems. In particular, we show the relevance of proportional fairness and optimal reliability criteria for the application of quantum resources. Moreover, we present optimal allocation solutions for an arbitrary number of qudits using measurement incompatibility as an exemplary resource theory. Besides, we study the criterion of optimal equitability and demonstrate its relevance to scenarios involving several resource theories such as nonlocality vs local contextuality. Finally, we highlight the potential impact of our results for quantum networks and other multi-party quantum information processing, in particular to the future Quantum Internet.
Assessing automation: Methodological insights from experimenting with computer vision for public life research

Today, there is a growing interest in designer-led assessment efforts focused on social performance. While this interest has led to significant developments in our understanding of how people occupy urban public space, the spatial data-collection methodologies that are currently being employed are similar to those used in early foundational studies. This paper explores how advancements in digital automation might expand the designer toolbox and, in turn, help to inform design decision making. To do this, the paper unpacks a recent research project that experimented with an automated tabulation technique using computer vision to visualize the spatial distribution of urban public space users. The goal of the computer-vision output is to help designers observe and identify common social patterns across ten plazas in New York City. A key finding from the study is that the automated data-collection technique employed does not fully replace manual techniques, but is complementary.