Reviewerly/exHarmony huggingface dataset:
Moving Embodied Design Education Online.

During the rapid and forced move to online teaching during the Covid-19 pandemic, university courses that never would have been considered suitable for online teaching under normal circumstances, were moved online. While challenging, this also opened opportunities for developing innovative strategies for teaching and learning. We report on the experiences of moving online a course in embodied interaction, which due to its focus on situated and embodied design faced extensive challenges in moving online. We highlight in particular the way in which the course brought forward innovative methods for bodystorming and user-assisted trialling. This paper has been written jointly by students and teachers from the course.
Detecting Anomalies from Video-Sequences: a Novel Descriptor

We present a novel descriptor for crowd behavior analysis and anomaly detection. The goal is to measure by appropriate patterns the speed of formation and disintegration of groups in the crowd. This descriptor is inspired by the concept of one-dimensional local binary patterns: in our case, such patterns depend on the number of group observed in a time window. An appropriate measurement unit, named “trit” (trinary digit), represents three possible dynamic states of groups on a certain frame. Our hypothesis is that abrupt variations of the groups' number may be due to an anomalous event that can be accordingly detected, by translating these variations on temporal trit-based sequence of strings which are significantly different from the one describing the “no-anomaly” one. Due to the peculiarity of the rationale behind this work, relying on the number of groups, three different methods of people group's extraction are compared. Experiments are carried out on the Motion-Emotion benchmark data set. Reported results point out in which cases the trit-based measurement of group dynamics allows us to detect the anomaly. Besides the promising performance of our approach, we show how it is correlated with the anomaly typology and the camera's perspective to the crowd's flow (frontal, lateral).
Translating Extensive Form Games to Open Games with Agency

We show open games cover extensive form games with both perfect and imperfect information.Doing so forces us to address two current weaknesses in open games: the lack of a notion of player and their agency within open games, and the lack of choice operators.Using the former we construct the latter, and these choice operators subsume previously proposed operators for open games, thereby making progress towards a core, canonical and ergonomic calculus of game operators.Collectively these innovations increase the level of compositionality of open games, and demonstrate their expressiveness.
HUMAN SUSPICIOUS ACTIVITY DETECTION SYSTEM USING CNN MODEL FOR VIDEO SURVEILLANCE

This paper brings forward one amongst the foremost significant applications of human suspicious activity recognition that is termed as anomaly detection. A key concern of any society today is providing safety to an individual. The main reason behind this concern is due to the constantly increasing activities causing threats, starting from deliberate ferocity to an injury caused through an accident. Simple installation of a traditional closed circuit television(CCTV) is not sufficient as it requires a person to continuously stay alert and monitor the cameras, which is quite inefficient. This call for the requirement to develop an security system which is fully automated system that recognizes anomalous activities in real time and brings instant help to the victims. Hence we proposed a system which will examine and detect the suspicious human action from real-time CCTV footage with help of machine learning techniques and generates the alert if the abnormal activity is occurred. The method is implemented on the dataset containing both normal and anomaly activity and experiment has shown better results.
Text Recognition - Real World Data and Where to Find Them

We present a method for exploiting weakly annotated images to improve text extraction pipelines. The approach uses an arbitrary end-to-end text recognition system to obtain text region proposals and their, possibly erroneous, transcriptions. The method includes matching of imprecise transcriptions to weak annotations and an edit distance guided neighbourhood search. It produces nearly error-free, localised instances of scene text, which we treat as “pseudo ground truth” (PGT). The method is applied to two weakly-annotated datasets. Training with the extracted PGT consistently improves the accuracy of a state of the art recognition model, by 3.7% on average, across different benchmark datasets (image domains) and 24.5% on one of the weakly annotated datasets <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Acknowledgements. The authors were supported by Czech Technical University student grant SGS20/171/0HK3/3TJ13, the MEYS VVV project CZ.02.1.01/0.010.0J16 019/0000765 Research Center for Informatics, the Spanish Research project TIN2017-89779-P and the CERCA Programme / Generalitat de Catalunya.
How do Technical Debt Payment Practices Relate to the Effects of the Presence of Debt Items in Software Projects?

Context: Knowing the effects of technical debt (TD) can support software development teams in the prioritization of TD items to pay off. However, little is known about the relations between the effects of TD and TD payment practices. Having this knowledge can provide valuable information for decision making about which payment practice can be applied given the presence of specific effects of TD. Aims: To investigate, from the point of view of software practitioners, (i) which TD payment practices have been used when certain effects of the presence of debt are felt in software projects and (ii) the reasons for not paying debt items despite the effects they are causing to the project. Method: We analyze quantitatively and qualitatively data collected from a survey with 432 practitioners across four countries. Results: Among the identified relations, the practice "code refactoring" is commonly used to pay debt items off when the effects "delivery delay" and "rework" are felt in software projects. On the other hand, when practitioners face the TD effects "low external quality" and "delivery delay", ，they commonly justify the non- payment of the debt items indicating the need of "focusing on short term goals". Conclusion: We organize the relationship between TD effects, and payment practices and reasons for not eliminating debt items. All this information is structured in an alluvial diagram, which can facilitate the visualization of the identified relations.
A Survey of Published Literature on Conversational Artificial Intelligence

Conversational artificial intelligence (AI), as a rapidly emerging technology, has made a huge impact in the fields of e-commerce, education, entertainment, health, journalism and productivity, and thus arousing the interest of governments, businesses and research institutions. Therefore, it is necessary to generate an overview of the recent developments in the field in order to plan further study effectively. To achieve the goal, we analyze the conversational AI literature between 2015 and 2020 with CiteSpace, which is used for the systematic evaluation of the knowledge structures. Based on the results of the analysis, we find the following insights: (1) The evolution of conversational AI research involves many categories while two major disciplines-computer science and ergonomics-lead the way. (2) The current research can be divided into two research areas: underlying technology architecture and smart scene applications. By using multiple complementary scientometric methods, our study visually presents the research history, current research hotspots and emerging trends in the field of conversational AI, to further promote its technology and application research.
Decoupling Pronunciation and Language for End-to-End Code-Switching Automatic Speech Recognition

Despite the recent significant advances witnessed in end-to-end (E2E) ASR system for code-switching, hunger for audio-text paired data limits the further improvement of the models’ performance. In this paper, we propose a decoupled transformer model to use mono-lingual paired data and unpaired text data to alleviate the problem of code-switching data shortage. The model is decoupled into two parts: audio-to-phoneme (A2P) network and phoneme-to-text (P2T) network. The A2P network can learn acoustic pattern scenarios using large-scale monolingual paired data. Meanwhile, it generates multiple phoneme sequence candidates for single audio data in real time during the training process. Then the generated phoneme-text paired data is used to train the P2T network. This network can be pre-trained with large amounts of external unpaired text data. By using monolingual data and unpaired text data, the decoupled transformer model reduces the high dependency on code-switching paired training data of E2E model to a certain extent. Finally, the two networks are optimized jointly through attention fusion. We evaluate the proposed method on the public Mandarin-English code-switching dataset. Compared with our transformer baseline, the proposed method achieves 18.14% relative mix error rate reduction.
A multiunit compensation for organic light‐emitting diode burn‐in in mobile displays

Abstract In this paper, a cost‐effective organic light‐emitting diode (OLED) display burn‐in compensation method on a mobile system is proposed. The subpixel wise compensation map is estimated using the data‐counting approach and is reallocated to multiunit regional map according to amount of burn‐in details. The degraded luminance compensation is applied for the display driver integrated circuit (IC) without the additional external storage. With the optimized multiunit map, perceptual quality can be enhanced after compensation with limited internal memory. Experiment shows that the luminance uniformity of the degraded panel has been improved by 20% after proposed compensation with 1/16 memory space usage.
Blended Learning Model Based on Massive Open Online Courses (MOOCs) Assisted by Augmented Reality (BMA) Model as the Electronic Learning Media in the Pandemic Covid-19

This study aims to analyze educators' responses to implementing the blended learning model based on MOOCs assisted by augmented reality or the so-called BMA model. This BMA model is applied to the learning process using the MOOCs system that has been developed with augmented reality assistance in the midst of the COVID-19 pandemic. To determine the response of prospective educators in the study, data collection techniques were carried out using a questionnaire given to 47 students. This questionnaire consists of 17 question items with 4 Likert scales. The data analysis technique used qualitative, quantitative data analysis. To test the quality of the questionnaire given, data analysis was used using the Rasch model and the winsteps application. Based on the research results, it was found that the instrument data used were valid and reliable. The results of student responses obtained are in very good criteria. This is reinforced by the results of the validity calculation in the rasch analysis which was carried out which stated that 17 items were declared valid and no items were issued. In addition, the value of the calculation of data reliability using the alpha cronbach method with a score of 0.91 is higher than the r table value for α = 0.05 0.2876. Thus it can be said that the data used is reliable and prospective educators respond very well to the use of this BMA model in the implementation of the learning process through the MOOCs system during the Covid-19 pandemic. This is reinforced by the results of the validity calculation in the rasch analysis which was carried out which stated that 17 items were declared valid and no items were issued. In addition, the value of the calculation of data reliability using the alpha cronbach method with a score of 0.91 is higher than the r table value for α = 0.05 0.2876. Thus it can be said that the data used is reliable and prospective educators respond very well to the use of this BMA model in the implementation of the learning process through the MOOCs system during the Covid-19 pandemic. This is reinforced by the results of the validity calculation in the Rasch analysis, which was carried out, which stated that 17 items were declared valid and no items were issued. In addition, the value of the calculation of data reliability using the alpha cronbach method with a score of 0.91 is higher than the r table value for α = 0.05 0.2876. Thus, the data used is reliable, and prospective educators respond very well to the use of the BMA model in the implementation of the learning process through the MOOCs system during the Covid-19 pandemic.
Inmplode: A framework to interpret multiple related rule‐based models

Abstract There is a growing trend to split problems into separate subproblems and develop separate models for each (e.g., different churn models for separate customer segments; different failure prediction models for separate university courses, etc.). While it may lead to better predictive models, the use of multiple models makes interpretability more challenging. In this paper, we address the problem of synthesizing the knowledge contained in a set of models without a significant loss of prediction performance. We focus on decision tree models because their interpretability makes them suitable for problems involving knowledge extraction. We detail the process, identifying alternative methods to address the different phases involved. An extensive set of experiments is carried out on the problem of predicting the failure of students in courses at the University of Porto. We assess the effect of using different methods for the operations of the methodology, both in terms of the knowledge extracted as well as the accuracy of the combined models.
Enhancing Detection Performance of Face Recognition Algorithm Using PCA-Faster R-CNN

In this paper, we present a new face detection scheme using deep learning and achieving state-of-the-art recognition performance using real-world datasets. We designed and implemented a face recognition system using Principal Component Analysis (PCA) and Faster R Convolutional Neural Network (Faster R CNN). In particular, we improve the state-of-the-art Faster RCNN framework by using Principal Component Analysis (PCA) technique and Faster R CNN to detect and recognise faces in a face database. The Principal Component Analysis (PCA) was used to extract features and dimensionality reduction from the face database, while the Faster R Convolutional Neural Network algorithm was used to identify patterns in the dataset via training the neural network. The three real-world datasets used in our experiment are ORL, Yale, and California face dataset. When implemented on the ORL face dataset, the algorithm achieved average recognition accuracy of 99%, with a recognition time of 147.72 seconds for 10 runs, and the recognition time/image was 0.3 sec/image on 400 images. The Yale face dataset achieved average recognition accuracy of 99.24% with a recognition time of 63.45 seconds for 10 runs, and the recognition time/image was 0.53 sec/image on 120 images. Finally, on California Face Database (CFD), it achieved average recognition accuracy of 99.52% with a recognition time of 226.05 seconds for 10 runs, and the recognition time/image was 0.27 sec/image on 827 images. On the CFD dataset, however, the proposed approach has excellent classification performance when the recall ratio is high. The proposed method achieves a higher recall and accuracy ratio than the Faster RCNN without PCA method. For the F-score, the proposed method achieved 0.98, which is significantly higher than the 0.95 achieved by the Faster-RCNN. This demonstrates the superiority of our model performance-wise as against state-of-the-art, both in terms of accuracy and fast recognition. Therefore our model is more efficient when compared to the latest researches done in the area of facial recognition.
Pathology Informatics Education during the COVID-19 Pandemic at Memorial Sloan Kettering Cancer Center (MSKCC)

&lt;p&gt;This review details the development and structure of a four-week rotation in pathology informatics for a resident trainee at Memorial Sloan Kettering Cancer Center (MSKCC) in New York City so that other programs interested in such a rotation can refer to. The role of pathology informatics is exponentially increasing in research and clinical practice. With an ever-expanding role, training in pathology informatics is paramount as pathology training programs and training accreditation bodies recognize the need for pathology informatics in training future pathologists. However, due to its novelty, many training programs are unfamiliar with implementing pathology informatics training. The rotation incorporates educational resources for pathology informatics, guidance in the development, and general topics relevant to pathology informatics training. Informatics topics include anatomic pathology related aspects such as whole slide imaging, laboratory information systems, image analysis, and molecular pathology associated issues such as the bioinformatics pipeline and data processing. Additionally, we highlight how the rotation pivoted to meet the department’s informatics needs while still providing an educational experience during the onset of the COVID-19 pandemic.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion&lt;/strong&gt;. As pathology informatics continues to grow and integrate itself into practice, infor- matics education must also grow to meet the future needs of pathology. As informatics programs develop across institutions, such as the one detailed in this paper, these programs will better equip future pathologists with informatics to approach disease and pathology.&lt;/p&gt;
AksharaNet: A GPU Accelerated Modified Depth-Wise Separable Convolution for Kannada Text Classification

For content-based indexing and retrieval applications, text characters embedded in images are a rich source of information. Owing to their different shapes, grayscale values, and dynamic backgrounds, these text characters in scene images are difficult to detect and classify. The complexity increases when the text involved is a vernacular language like Kannada. Despite advances in deep learning neural networks (DLNN), there is a dearth of fast and effective models to classify scene text images and the availability of a large-scale Kannada scene character dataset to train them. In this paper, two key contributions are proposed, AksharaNet, a graphical processing unit (GPU) accelerated modified convolution neural network architecture consisting of linearly inverted depth-wise separable convolutions and a Kannada Scene Individual Character (KSIC) dataset which is grounds-up curated consisting of 46,800 images. From results it is observed AksharaNet outperforms four other well-established models by 1.5% on CPU and 1.9% on GPU. The result can be directly attributed to the quality of the developed KSIC dataset. Early stopping decisions at 25% and 50% epoch with good and bad accuracies for complex and light models are discussed. Also, useful findings concerning learning rate drop factor and its ideal application period for application are enumerated.
Review of augmented reality display technology

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed
Improving Energy Efficiency in UAV Attitude Control using Deep Reinforcement Learning

Attitude control refers to controlling the rotational motion of an Unmanned Aerial Vehicle (UAV) about its axes.Any movement requires energy consumption and UAV batteries store limited energy.We propose the use of reinforcement learning to optimise energy usage in UAVs.We use Proximal Policy Optimization (PPO) algorithm to train the model, modifying the existing algorithm to incorporate sigmoid activation function.We have introduced Ornstein Uhlenbeck noise to the policy function with the intention of adding the unpredictability found in real world environments.We have designed the reward function of our algorithm such that the quadcopter aims to change its existing angular velocity to achieve the target angular velocity.While this attitude control takes place, energy is spent due to motor/propeller revolutions.Our reward function minimizes the rapid change in motor speeds which causes fast battery depletion, thus saving energy and enhancing UAV flight time.
Self-resonating Vibrotactile Feedback Instruments ||: Making, Playing, Conceptualising :||

Self-resonating vibrotactile instruments (SRIs) are hybrid feedback instruments, characterised by an electro-mechanical feedback loop that is both the means of sound production and the expressive interface. Through the lens of contemporary SRIs, we reflect on how they are characterised, designed, and played. By considering reports from designers and players of this species of instrument-performance system, we explore the experience of playing them. With a view to supporting future research and practice in the field, we illustrate the value of conceptualising SRIs in Cybernetic and systems theoretic terms and suggest that this offers an intuitive, yet powerful basis for future performance, analysis and making; in doing so we close the loop in the making, playing and conceptualisation of SRIs with the aim of nourishing the evolution of theory, creative and technical practice in this field.
STUDENTSâ€™ PERCEPTION ON THE USE OF YOUTUBE IN ENGLISH LANGUAGE LEARNING DURING PANDEMIC IN WIRALODRA UNIVERSITY

The Indonesian Minister of Education and Culture has instructed schools to carry out online learning since March 17, 2020 along with widespread COVID-19. Among all online learning media, YouTube has become increasingly popular for learning tools, especially with young adults. Thus, this research tried to investigate the studentsâ€™ perception of using YouTube in the study of English skills class. The qualitative approach with case study design was implemented to six undergraduate students of the 3rd semester who voluntarily participated in this research. The instruments used in this research are questionnaires and interviews. The data was analyzed descriptively to answer the research question. The finding reported that YouTube videos make students happy to learn English; it helps them understand the material. However, youtube makes communication with lecturers and classmates difficult without Whatsapp. In terms of strengths and weaknesses, Youtube can keep the students from feeling bored; they can study anytime and anywhere, they can also re-watch the video at will. However, YouTube videos can be a poor learning media if the videos are too long. Youtube is considered expensive without Wi-Fi access. Then the last is the possibility of signal interference.