Reviewerly/exHarmony huggingface dataset:
Image Processing Based Control System for Wheeled Mobile Robots

The paper presents the design and tests of a wheeled mobile robot. The construction of the machine is based on a differential drive. A three-layer structure of the robot control system has been developed. The lowest layer is based on an 8-bit microcontroller and its purpose is to control motors and sensors of the robot. The medium layer is based on a 32-bit ARM Cortex M4 microcontroller. There has been implemented an x86 architecture computational unit as the main part of the highest layer of the control system. A control software developed for this computer is responsible for the acquisition and processing of an image stream from the camera mounted on the robot board. The software has been implemented using Aforge.NET library. Selected image processing filters have been tested in order to enable object tracking and gesture recognition in the mobile robot control system. Finally, a power consumption of the computational unit operating in several developed control modes has been measured.
Virtual Teaching Strategies in Times of the COVID-19 Pandemic

The pandemic caused by COVID-19 changed the way of teaching. The transition to virtual education required the adaptation or development of different methods and strategies. The professor's skills had to be transferred to the online learning environment, preserving the quality of teaching. These challenges have modified the didactics, the forms of evaluation, the interaction with the students, and the coordination. This work shows the results of the preferences of tools chosen by university teachers that contemplate the four mentioned areas to maintain the efficiency of their classes in a virtual mode.
Design of Encoder in CNN for Effective Recognition of Odia Numerals

Deep learning is highly appreciated in the field of image processing as it follows the human brain like a training process. In general, artists look at a model and implement the structure in the canvas by observing the important features. Similarly, Autoencoders have attracted the attention of researchers as it learns the important features from the trained data to generate the structures similar to the data provided for training. Autoencoders are the basic building components of generative models. In this work, we have designed an Autoencoder (AE) to generate a large number of data to support the Generative Adversarial Network (GAN) Model applied to the IIT Bhubaneswar Odia handwritten numeral database. In this work, we have designed an encoder that generates the feature vectors by applying Convolutional layers activated by Leaky-ReLU followed by max pooling. It is verified that the decoder recognizes the features due to proper training. The generated images are quite similar to original data that validate the proposed AE is well reconstructive. To measure the performance of the model loss is calculated using mean square error. The proposed model of AE is trained with Adam Optimizer.
Clinical decision-making framework against over-testing based on modeling implicit evaluation criteria

Different statistical methods include various subjective criteria that can prevent over-testing. However, no unified framework that defines generalized objective criteria for various diseases is available to determine the appropriateness of diagnostic tests recommended by doctors. We present the clinical decision-making framework against over-testing based on modeling the implicit evaluation criteria (CDFO-MIEC). The CDFO-MIEC quantifies the subjective evaluation process using statistics-based methods to identify over-testing. Furthermore, it determines the test’s appropriateness with extracted entities obtained via named entity recognition and entity alignment. More specifically, implicit evaluation criteria are defined—namely, the correlation among the diagnostic tests, symptoms, and diseases, confirmation function, and exclusion function. Additionally, four evaluation strategies are implemented by applying statistical methods, including the multi-label k-nearest neighbor and the conditional probability algorithms, to model the implicit evaluation criteria. Finally, they are combined into a classification and regression tree to make the final decision. The CDFO-MIEC also provides interpretability by decision conditions for supporting each clinical decision of over-testing. We tested the CDFO-MIEC on 2,860 clinical texts obtained from a single respiratory medicine department in China with the appropriate confirmation by physicians. The dataset was supplemented with random inappropriate tests. The proposed framework excelled against the best competing text classification methods with a Mean_F1 of 0.9167. This determined whether the appropriate and inappropriate tests were properly classified. The four evaluation strategies captured the features effectively, and they were imperative. Therefore, the proposed CDFO-MIEC is feasible because it exhibits high performance and can prevent over-testing.
Detecting Invasive Ductal Carcinoma with Semi-supervised Conditional GANs

Invasive ductal carcinoma (IDC) comprises nearly 80% of all breast cancers. The detection of IDC is a necessary preprocessing step in determining the aggressiveness of the cancer, determining treatment protocols, and predicting patient outcomes, and is usually performed manually by an expert pathologist. Here, we describe a novel algorithm for automatically detecting IDC using semi-supervised conditional generative adversarial networks (cGANs). The framework is simple and effective at improving scores on a range of metrics over a baseline CNN.
Learning High-Dimensional Evolving Data Streams With Limited Labels

In the context of streaming data, learning algorithms often need to confront several unique challenges, such as concept drift, label scarcity, and high dimensionality. Several concept drift-aware data stream learning algorithms have been proposed to tackle these issues over the past decades. However, most existing algorithms utilize a supervised learning framework and require all true class labels to update their models. Unfortunately, in the streaming environment, requiring all labels is unfeasible and not realistic in many real-world applications. Therefore, learning data streams with minimal labels is a more practical scenario. Considering the problem of the curse of dimensionality and label scarcity, in this article, we present a new semisupervised learning technique for streaming data. To cure the curse of dimensionality, we employ a denoising autoencoder to transform the high-dimensional feature space into a reduced, compact, and more informative feature representation. Furthermore, we use a cluster-and-label technique to reduce the dependency on true class labels. We employ a synchronization-based dynamic clustering technique to summarize the streaming data into a set of dynamic microclusters that are further used for classification. In addition, we employ a disagreement-based learning method to cope with concept drift. Extensive experiments performed on many real-world datasets demonstrate the superior performance of the proposed method compared to several state-of-the-art methods.
Emoji Analysis Using Deep Learning

Emojis or avatars have been the ways for indicating nonverbal cues. These cues have become the most essential part of online chatting, product review, brand emotion, and so on. It has been leading to increasing data science research dedicated to emoji-driven storytelling. With advancements in computer vision and deep learning, it has been currently possible to detect human emotions from various images. In this deep learning project, we have classified human facial expressions to filter and mapped corresponding emojis or avatars. In 1999, Japanese artist Shigetaka Kurita created the first emoji. He worked with the people, who worked for a renowned mobile Internet platform, named ‘I-mode’ from DOCOMO which was Japan’s operator company for the mobile phones. The original 176 emojis introduced by Kurita are currently among the persistent collection at Museum of Modern Art which is situated in New York, which are the confidential symbols over faces, because it was DOCOMO’s objective and motive to search for better ways to communicate and convey ourselves. Emoji became a boon and sense of popularity in Japan very quickly. On the other hand, DOCOMO’s idea was being taken by other rival mobile companies.
Problematising Identity, Positionality, and Adequacy in HCI4D Fieldwork: A Reflection

Ontological and epistemological differences between Western and non-Western traditions makes investigating and understanding other cultures using stereotypical (Western) approaches and methods rather difficult. At the intersection of a crisis of identity, epistemic positionality and cultural adequacy, this paper reflects on the ethical and methodological implications of the practices of HCI4D fieldwork that seek to decode and deconstruct the mundane practices of designing and deploying educational technologies in Nigeria. The reflection identifies a range of issues concerning the limiting relevance of conventional methods of undertaking field studies in Africa, while also showing the appropriateness of indigenous approaches. This has significant importance for the practices of those wishing to work in/with African communities in design projects.
Enhancing video communication experience for low vision users

In this paper, we investigate the impact of various video enhancement techniques on the video chat user experience of people diagnosed with low vision. To this end, we present the results of an exploratory user study, in which participants with low vision were asked to utilize an accessibility-enhanced video chat client to perform two tasks (interviewing and facial emotion recognition). Participants were able to individually configure the appearance of the video by defining several media enhancement parameters (e.g. contrast adaption, edge detection and enhancement, gray scale modes, etc.) to compensate for their own individual visual impairments. Our results show that individual video enhancement configurations positively impacted the video chat experience of the interview task. Also the second task benefited from the video enhancement, i.e., more facial emotions were correctly identified.
Evaluation of an Augmented Reality Instruction for a Complex Assembly Task

Abstract This study compares the use of a marker-based AR instruction with a paper instruction commonly used in manual assembly. Hypotheses were tested as to whether the instruction type affects assembly time, number of errors, usability, and employee strain. Instead of student participants and artificial assembly tasks (e. g. Lego assemblies), the study was conducted with 16 trainees in a real workplace for the assembly of emergency door release handles in rail vehicles. Five assembly runs were performed. Assembly times and assembly errors were determined from recorded videos. Usability (SUS) and strain (NASA-TLX) were recorded with questionnaires. After a slower assembly at the beginning, the AR group assembled significantly faster in the fifth run. The comparable number of errors, usability and strain make marker-based AR applications interesting for knowledge transfer in manual assembly, especially due to the easy entrance and low costs.
Salient object detection via joint perception of region-level spatial distribution and color contrast

Nowadays, many RGB-D saliency detection models have been proposed, and they are conducted based on the pixel-level depth data obtained by active depth sensors or dense stereo matching algorithms in advance. However, the pixel-level depth maps may have a negative impact on saliency ranking, especially when they are inconsistent or invalid within the object area. It can be found that people tend to observe an object as a whole and ignore the details of the target surface. Inspired by this characteristic, we propose a salient object detection method via joint perception of region-level spatial distribution and color contrast. First, a twice segmentation strategy based on multifeature fusion is used to compute the region-level information. Then, the region-level spatial distribution maps are constructed instead of pixel-level depth maps, which is helpful for avoiding the interference of dense depth information. To improve the integrity of object detection, color saliency maps are also computed based on regional segmentation information. After that, we adopt a fusion strategy to achieve the effective complementarity of the two kinds of information. Two optimization strategies are employed to further improve the results of saliency ranking. Experimental results on two benchmark datasets demonstrate that the proposed method has better performance than most of the state-of-the-art methods, and it also shows competitive capability compared with deep learning-based methods.
Pretrained Word Embeddings for Image Captioning

All modern methods in one way or another related to natural language processing problems use vector representations of words. These can be either representations learned for a specific task or pretrained vector representations learned from a huge corpus of texts. Image captioning is not an exception. Mostly pretrained vector representations are not used, but they are trained along with the rest of the model during the training models that generate a textual description of an image. In this work, we decided to investigate whether the use of pretrained vector representations for words will improve the quality of the model as it did for other tasks. Our research shows that the use of such representations as Word2vec and GloVe improves the quality of the model, while GloVe embeddings are even more suitable for this task. Moreover, even greater gain is obtained if they are used as an initial approximation and fine-tuned in the process of training the entire model.
Performance of the SegNet in the Segmentation of Breast Ultrasound Lesions

This work presents a performance analysis of a convolutional neural network (CNN), named SegNet, applied for automatic segmentation of breast ultrasound images. Defining the viability of CNN's could help with feature extraction and posterior classification of lesions, causing a decrease in the number of unnecessary biopsies. The breast ultrasound (BUS) dataset consists of 2054 images from 659 female patients, and each image was manually segmented by an experienced radiologist. The dataset is fed into the CNN without any filtering process applied to it and the test output is then compared with the radiologist's labels. We compared the results of different loss and activation functions, and the experiments reveal that the best performance was achieved using dice loss function, with dice coefficient of 81.1%.
Homotopic Distance and Generalized Motion Planning

Abstract We prove that the homotopic distance between two maps defined on a manifold is bounded above by the sum of their subspace distances on the critical submanifolds of any Morse–Bott function. This generalizes the Lusternik–Schnirelmann theorem (for Morse functions) and a similar result by Farber for the topological complexity. Analogously, we prove that, for analytic manifolds, the homotopic distance is bounded by the sum of the subspace distances on any submanifold and its cut locus. As an application, we show how navigation functions can be used to solve a generalized motion planning problem.
Artificial Intelligence's Grand Challenges: Past, Present, and Future

Innovative, bold initiatives that capture the imagination of researchers and system builders are often required to spur a field of science or technology forward. A vision for the future of artificial intelligence was laid out by Turing Award winner Raj Reddy in his 1988 Presidential address to the Association for the Advancement of Artificial Intelligence. It is time to provide an accounting of the progress that has been made in the field, over the last three decades, toward the challenge goals. While some tasks such as the world‐champion chess machine were accomplished in short order, many others, such as self‐replicating systems, require more focus and breakthroughs for completion. A new set of challenges for the current decade is also proposed, spanning the health, wealth, and wisdom spheres.
Feasibility study of earthquake early warning in Tehran, Iran

Abstract In this study, we examine the scientific feasibility of an Earthquake Early Warning System in Tehran, Iran, by the integration of the Tehran Disaster Mitigation and Management Organization (TDMMO) accelerometric network and the PRobabilistic and Evolutionary early warning SysTem (PRESTo). To evaluate the performance of the TDMMO-PRESTo system in providing the reliable estimations of earthquake parameters and the available lead-times for The Metropolis of Tehran, two different approaches were analyzed in this work. The first approach was assessed by applying the PRESTo algorithms on waveforms from 11 moderate instrumental earthquakes that occurred in the vicinity of Tehran during the period 2009–2020. Moreover, we conducted a simulation analysis using synthetic waveforms of 10 large historical earthquakes that occurred in the vicinity of Tehran. We demonstrated that the six worst-case earthquake scenarios can be considered for The Metropolis of Tehran, which are mostly related to the historical and instrumental events that occurred in the southern, eastern, and western parts of Tehran. Our results indicate that the TDMMO-PRESTo system could provide reliable and sufficient lead-times of about 1 to 15s and maximum lead-times of about 20s for civil protection purposes in The Metropolis of Tehran.
Recognition and determination of fuzzy logical relationship in the system fault evolution process

• The concepts of system fault evolution process and space fault network theory are proposed by the authors. • Referring to 14 kinds of logical relationships in flexible logic, the author transforms them into the event occurrence logical relationship formula, which can be regarded as the basic logical relationships; the latter uses enumeration method to change the fuzzy membership degree, and obtains the optimal fuzzy membership degree when the fitness function is closest to 0. Finally, the concrete form of fuzzy logical structure function is obtained. • The fuzzy logical relationship between the cause event and the result event is determined by using neural network; and the algorithm analysis diagram and steps are given. The method can effectively solve the problem of determining the fuzzy logical relationship between events in the system fault evolution process. To study the fuzzy logical relationship between the cause event and result event in the system fault evolution process, a method of forming the expression of the fuzzy logical relationship by superposition of the basic logical relationships and fuzzy membership degree is proposed. It is called the fuzzy logical structure function. Two problems need to be solved to determine the fuzzy logical structure function. One is to determine the basic logical relationships; the other is to determine the fuzzy membership degree. The authors transform the 14 kinds of flexible logical relationships into the event occurrence logical relationship formula, which can be regarded as the basic logical relationships; the latter uses the enumeration method to change the fuzzy membership degree and obtains the optimal fuzzy membership degree when the fitness function is closest to 0. Finally, the form of the fuzzy logical structure function is obtained. The fuzzy logical relationship between the cause event and the result event is determined by using a neural network. With an example, it can be seen that the logical relationships between the cause event and the result event are implication and average, and its fuzzy membership degree is more than 80%; when using a neural network, the fuzzy membership degree has no practical significance, but it can obtain the result event probability more accurately through the cause event probability. Finally, the advantages and disadvantages of the method are summarised.
<i>dxpr</i>: an R package for generating analysis-ready data from electronic health records—diagnoses and procedures

Enriched electronic health records (EHRs) contain crucial information related to disease progression, and this information can help with decision-making in the health care field. Data analytics in health care is deemed as one of the essential processes that help accelerate the progress of clinical research. However, processing and analyzing EHR data are common bottlenecks in health care data analytics.The dxpr R package provides mechanisms for integration, wrangling, and visualization of clinical data, including diagnosis and procedure records. First, the dxpr package helps users transform International Classification of Diseases (ICD) codes to a uniform format. After code format transformation, the dxpr package supports four strategies for grouping clinical diagnostic data. For clinical procedure data, two grouping methods can be chosen. After EHRs are integrated, users can employ a set of flexible built-in querying functions for dividing data into case and control groups by using specified criteria and splitting the data into before and after an event based on the record date. Subsequently, the structure of integrated long data can be converted into wide, analysis-ready data that are suitable for statistical analysis and visualization.We conducted comorbidity data processes based on a cohort of newborns from Medical Information Mart for Intensive Care-III (n = 7,833) by using the dxpr package. We first defined patent ductus arteriosus (PDA) cases as patients who had at least one PDA diagnosis (ICD, Ninth Revision, Clinical Modification [ICD-9-CM] 7470*). Controls were defined as patients who never had PDA diagnosis. In total, 381 and 7,452 patients with and without PDA, respectively, were included in our study population. Then, we grouped the diagnoses into defined comorbidities. Finally, we observed a statistically significant difference in 8 of the 16 comorbidities among patients with and without PDA, including fluid and electrolyte disorders, valvular disease, and others.This dxpr package helps clinical data analysts address the common bottleneck caused by clinical data characteristics such as heterogeneity and sparseness.
Deep CNN Based Automatic Detection and Identification of Bengal Tigers

A system for individual identification of The Royal Bengal Tigers (Panthera tigris) is absolutely necessary not only for monitoring the population of tigers but also for saving the precious lives of those workers whose job is to count the exact number of tigers present in a particular region like Sundarban in West Bengal, India. In this paper, a solution has been proposed for individual identification of Bengal Tigers using an autonomous/manually controlled drone. In the proposed system, the drone camera will search for the tigers using a Tiger Detection Model and then the flank (the body part which contains the stripes) of the detected tiger will be passed through a Fine-tuned state-of-art network. The system based on deep CNN will detect the uncommon features for individual counting of the tiger in a particular forest. The proposed system will enhance the accuracy of tiger detection technique that will be followed by the human experts. It also reduces the risk of accidents relating to animal attacks.