Reviewerly/exHarmony huggingface dataset:
Survey of Mathematical Expression Recognition for Printed and Handwritten Documents

In this paper, we provide a review of Mathematical Expression Recognition (MER) for both printed and handwritten domains. We describe the past work in a manner that clearly highlights the common and distinguishing aspects. The review reveals the advances in the techniques used for the detection and recognition of MEs for online and offline modalities. Systems have evolved from the use of domain-specific crafted modules to architectures learned in a completely data-driven manner. Learning tasks and datasets have been standardized, thus allowing performance comparison of the systems. With automated learning of improved feature representations and guided by the related fields such as machine translation and captioning, the systems have progressed to demonstrate improved robustness to the variability in the inputs.
Sensorimotor learning in immersive virtual reality: a scoping literature review

The benefits and drawbacks of using immersive virtual reality (IVR) for learning are increasingly being explored, with growing evidence that a major contributor to IVR learning benefits are the sensorimotor-based affordances of the technology. However, to our knowledge, there have been no reviews of sensorimotor-based IVR learning studies for academic or cognitive learning. In order to provide an overview of the field, we present a scoping review based on a comprehensive search that identified 14 documents reporting on experimental sensorimotor-focused learning studies. The review found universally positive learning outcomes for sensorimotor-led approaches across a variety of topics and approaches, although noted multiple areas of difference and potential issues across studies, including differing measures for learning success, potential common confounding factors, a lack of longitudinal investigations, a lack of a common methods for surveying sensorimotor engagement in IVR, and a disconnect between researchers in this area.
Road Sign Detection Using Variants of YOLO and R-CNN: An Analysis from the Perspective of Bangladesh

Road sign detection represents a feature that assures the safety of drivers, vehicles, and pedestrians by efficiently detecting road signs. This feature is designed to notify drivers about road signs whether he is missing the signs or not. This detecting and recognizing feature of the road signs' has improved a part of the advanced driver assistance system (ADAS). ADAS is an automated technology containing cameras and sensors intended to help the drivers with road signs, while traveling to a new road or having no knowledge about road signs. Before the work analysis, this topic has shown formidability as it has a real-time processing solution. This paper analyzed seven architectures for detecting the road signs: YOLO, YOLOv2, YOLOv3, PP-YOLO model and R-CNN, Fast R-CNN, Faster R-CNN. We have built a dataset based on Bangladesh's road sign named the "BD Road Sign 2021 (BDRS 2021)" dataset to evaluate the architectures. This dataset contains 16 categories (16 types of road-sign), and each has 168 images. Finally, we applied the seven advanced architectures to find the effective one to detect Bangladesh's road signs. This study implies that YOLOv3 and Faster R-CNN perform comparatively better for road sign detection.
Power estimation method of low-voltage distributed photovoltaic generation based on similarity aggregation

Regarding to the issue that there is no automatic data acquisition for low-voltage distributed photovoltaic generation, a power estimation method for low-voltage distributed photovoltaic generation based on similarity aggregation is proposed in this paper. Firstly, based on the time series power curve data characteristics of middle-voltage distributed photovoltaic generation and L2 norm distance, a middle-voltage distributed photovoltaic aggregation model is established, and a solution method of middle-voltage distributed photovoltaic aggregation based on K-means clustering is proposed; Secondly, based on the aggregation results of middle-voltage distributed photovoltaic, a parameter calculation method for the power estimation of low-voltage distributed photovoltaic based on geographical distribution and electricity data is proposed; Thirdly, considering the time series data characteristics of distributed photovoltaic changing with date, a strategy to determine whether to re-calculate the middle-voltage distributed photovoltaic aggregation and the power estimation parameters according to the cumulative error is proposed. Finally case studies are accomplished and the effectiveness of the proposed method are verified. The aggregation of middle-voltage distributed photovoltaic and the power estimation of low-voltage distributed photovoltaic, which is helpful to master the overall power generation of regional distributed photovoltaic, realize the observability of low-voltage distributed photovoltaic power generation, improve the safe operation level of distribution network, and has important theoretical and practical significance for serving the orderly development of distributed photovoltaic generations.
Can Good Products Drive Out Bad? A Randomized Intervention in the Antimalarial Medicine Market in Uganda

Abstract How can quality be improved in markets in developing countries, which are known to be plagued by substandard and counterfeit (“fake”, in short) products? We study the market for antimalarial drugs in Uganda, where we randomly assign entry of a retailer (non-governmental organization (NGO)) providing a superior product—an authentic drug priced below the market—and investigate how incumbent firms and consumers respond. We find that the presence of the NGO had economically important effects. Approximately one year after the new market actor entered, the share of incumbent firms selling fake drugs dropped by more than 50% in the intervention villages, with higher quality drugs sold at significantly lower prices. Household survey evidence further shows that the quality improvements were accompanied by consumers expecting fewer fake drugs sold by drug stores. The intervention increased use of the antimalarial drugs overall. The results are consistent with a simple model where the presence of a seller committed to high quality, as opposed to an average firm, strengthens reputational incentives for competing firms to improve quality in order to not be forced out of the market, leading to “good driving out bad”.
Distributed Contact-Implicit Trajectory Optimization for Collaborative Manipulation

We present a distributed method for contact- implicit trajectory optimization which enables a group of robots to collaboratively manipulate an object through contact. Each robot computes its torques and contact forces locally to manipulate the object while communicating with its neighbors over a wireless network. While the global collaborative manipulation problem consists of multiple sets of non-smooth contact dynamics constraints for all robots, each robot in our method considers only its own contact dynamics constraints without those of other robots in its trajectory optimization problem, enabling each robot to efficiently compute its forces and torques. Our approach does not require each robot to share its torques, objective functions, and local constraints with other robots. We demonstrate our method in the manipulation of a sliding rod along a surface with friction, as well as in the manipulation of an object falling under gravity by a group of quadrotors.
Intelligent Malaysian Sign Language Translation System Using Convolutional-Based Attention Module with Residual Network

The deaf-mutes population always feels helpless when they are not understood by others and vice versa. This is a big humanitarian problem and needs localised solution. To solve this problem, this study implements a convolutional neural network (CNN), convolutional-based attention module (CBAM) to recognise Malaysian Sign Language (MSL) from images. Two different experiments were conducted for MSL signs, using CBAM-2DResNet (2-Dimensional Residual Network) implementing "Within Blocks" and "Before Classifier" methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time are recorded to evaluate the models' efficiency. The experimental results showed that CBAM-ResNet models achieved a good performance in MSL signs recognition tasks, with accuracy rates of over 90% through a little of variations. The CBAM-ResNet "Before Classifier" models are more efficient than "Within Blocks" CBAM-ResNet models. Thus, the best trained model of CBAM-2DResNet is chosen to develop a real-time sign recognition system for translating from sign language to text and from text to sign language in an easy way of communication between deaf-mutes and other people. All experiment results indicated that the "Before Classifier" of CBAMResNet models is more efficient in recognising MSL and it is worth for future research.
Perceptions about Teaching in Times of COVID-19 Pandemic: Experience of Secondary Education in Chile

&lt;p style="text-align: justify;"&gt;The following article is a qualitative study that analyses the perception of parents and high school students regarding teaching in times of Coronavirus disease COVID-19, with the idea of recognizing the facilitators and barriers for the teaching-learning process in the pandemic. The above, in the understanding that due to the sudden appearance of SARS-CoV-2, educational systems around the world had to adapt to virtual teaching, as a result of the confinement to which the population has been subjected during the year 2020 and a good part of the year 2021. The research is based on the interpretative-comprehensive paradigm, with a qualitative methodology, which considered the realization of four focus groups with students and four focus groups with secondary school parents, from two public high schools located in the province of Talca, Maule region, Chile. The findings of the study are related to the adverse effects of the pandemic on student learning, as a result of connectivity difficulties, and the emotional impact on the quality of life of young people and their families. On the other hand, learning at home is related to limited pedagogical strategies and evaluative aspects that do not allow verifying the real learning of the students.&lt;/p&gt;
A Reliability Optimization Framework for Public Cloud Services based on Markov Process and Hierarchical Correlation Modelling

With the advancement of IoT and Smart City, public cloud computing systems are required to be powerful in data processing and be dependable as a service provider. Thus, reliability analysis of cloud computing systems has been widely investigated but far from being solved. Reliability of the public cloud computing system is indeed affected by many factors, such as service performance, system energy consumption. Researchers can analysis such important correlation to find correlation factors that can cause significant changes in the correlation, and further optimize those correlation factors dynamically and intelligently. This would be an effective approach to improve the reliability of the public cloud system. This paper tries to establish a Reliability analysis framework covering four levels, i.e., component, system, mission and data, by using of Markov process and hierarchical correlation modelling. Numerical results indicate that the proposed methods improve the reliability by reliability planning, optimizes energy utilization, and uses stand-by policies.
Effective motion planning of manipulator based on SDPS-RRTConnect

Abstract In order to improve the speed of motion planning, this paper proposes an improved RRTConnect algorithm (SDPS-RRTConnect) based on sparse dead point saved strategy. Combining sparse expansion strategy and dead point saved strategy, the algorithm can reduce the number of collision detection, fast convergence, avoid falling into local minimum, and ensure the completeness of search space. The algorithm is simulated in different environments. The results show that in complex environments, the sparse dead point saved strategy plays a good effect. In simple environments, the greedy connection strategy works well. Compared with the standard RRT algorithm, the standard RRTConnect algorithm, and the SDPS-RRT algorithm, the SDPS-RRTConnect algorithm has the shortest planning time, and it is suitable for both simple and complex environments. The 500 experiments show that the algorithm has strong robustness. The actual robot experiments show that the path planned by SDPS-RRTConnect algorithm is effective.
Controlling Eye Blink for Talking Face Generation via Eye Conversion

A real talking face video includes not only the movement of the mouth, but also realistic blinking details. For a computer generated talking face video, realistic eye movements are critical to overcome the uncanny valley effect. However, it remains a great challenge to introduce realistic eye movements into talking face generation systems. In this paper, we propose a two-stage system for generating talking face video with realistic controllable blinking actions. Through eye conversion and frame replacement, our architecture can ensure the controllability of the blinking motion generation. We propose an eye conversion GAN, which can convert a face image into any stages of blinking, and maintain the consistency of facial identity features. In this network, we design joint training to increase the network's ability of generating closed and half-closed eye images, which improves the authenticity of the eyes. Experiments on two popular data sets show that compared with previous work, our method can not only guarantee the authenticity of mouth movements, but also generate realistic and controllable eye blinks.
Efficient and Robust Online Trajectory Prediction for Non-Cooperative Unmanned Aerial Vehicles

As an important type of dynamic data-driven application system, unmanned aerial vehicles (UAVs) are widely used for civilian, commercial, and military applications across the globe. An increasing research effort has been devoted to trajectory prediction for non-cooperative UAVs to facilitate their collision avoidance and trajectory planning. Existing methods for UAV trajectory prediction typically suffer from two major drawbacks: inadequate uncertainty quantification of the impact of external factors (e.g., wind) and inability to perform online detection of abrupt flying pattern changes. This paper proposes a Gaussian process regression (GPR)-based trajectory prediction framework for UAVs featuring three novel components: 1) GPR with uniform confidence bounds for simultaneous predictive uncertainty quantification, 2) online trajectory change-point detection, and 3) adaptive training data pruning. The paper also demonstrates the superiority of the proposed framework to competing trajectory prediction methods via numerical studies using both simulation and real-world datasets.
Multidimensional modelling of quality of experience for video streaming

The increasing use of video streaming services that followed the Covid-19 pandemic has more than ever driven a rising interest among various stakeholders in service provisioning chain to understand factors influencing quality of experience (QoE). Many research activities have so far addressed different influence factors in order to understand and improve QoE when using video streaming service. However, we have recognized the requirement to address QoE as multidimensional concept, and show the relationship between QoE, perceptual dimensions, and influence factors. In this paper, we provide the multidimensional modelling and analysis of QoE for video streaming. Result analysis has shown that QoE for video streaming can be modelled by using perception of quality of video and perception of ease of use of application as predictors. The analysis of influence of individual system (i.e., resolution, coding tree unit (CTU), and constant rate factor (CRF)), context (i.e., location, lighting, and video type), and human (i.e., gender, education level, and prior experience) factors and their interactions on QoE perceptual dimensions (i.e., perception of quality and perception of ease of use) show statistically significant impacts, which means that alternations of these factors can enhance perceptual dimensions and consequently QoE for video streaming which is a final goal.
A Multi-Factor Authenticated Blockchain-Based OTA Update Framework for Connected Autonomous Vehicles

The Connected Autonomous Vehicles (CAVs) are embedded with dozens of electronic units and sensors heavily reliant on vehicle&#x0027;s software systems for their secure and reliable operations. This software system must always stay updated for providing a secure and uninterrupted service to CA V. While the automated OTA (Over-The-Air) software and firmware updates for autonomous vehicles is essential for their safe operation, the potential system failure and cybersecurity threats remain a major concern for secure OTA update. This paper introduces a blockchain-based, highly adaptable solution for keeping the CA V OTA software systems updated in real-time while offering faster processing speed and a high level of security against possible cyber-attacks. Our proposed scheme ensures that only authorized OEM (Original Equipment Manufacturer) can upload new software and updated versions in the cloud, and only the certified vehicles download and install the updates. Our framework guarantees a faster, scalable, and multi-factor authentication system for a secure real-time software update service for CA V s.
A FHIR based architecture of a multiprotocol IoT Home Gateway supporting dynamic plug of new devices within instrumented environments

Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection over multiple protocols (e.g, Bluetooth, MQTT, CoAP, ZigBEE etc.) the interpretation, as well as the harmonization of the data format that derive from the existing huge amounts of heterogeneous IoT medical devices. In this respect this study aims at proposing an advanced Home Gateway architecture that offers a unique data collection module, supporting direct data acquisition over multiple protocols (i.e. BLE, MQTT) and indirect data retrieval from cloud health services (i.e, GoogleFit). Moreover the solution propose a mechanism to automatically convert the original data format, carried over BLE, in HL7 FHIR by exploiting device capabilities semantic annotation implemented by means of FHIR resource as well. The adoption of such annotation enables the dynamic plug of new sensors within the instrumented environment without the need to stop and adapt the gateway. This simplifies the dynamic devices landscape customization requested by the several telemedicine applications contexts (e.g. CVD, Diabetes) and demonstrate, for the first time, a concrete example of using the FHIR standard not only (as usual) for health resources representation and storage but also as instrument to enable seamless integration of IoT devices. The proposed solution also relies on mobile phone technology which is widely adopted aiming at reducing any obstacle for a larger adoption.
On sparse optimal regression trees

In this paper, we model an optimal regression tree through a continuous optimization problem, where a compromise between prediction accuracy and both types of sparsity, namely local and global, is sought. Our approach can accommodate important desirable properties for the regression task, such as cost-sensitivity and fairness. Thanks to the smoothness of the predictions, we can derive local explanations on the continuous predictor variables. The computational experience reported shows the outperformance of our approach in terms of prediction accuracy against standard benchmark regression methods such as CART, OLS and LASSO. Moreover, the scalability of our approach with respect to the size of the training sample is illustrated.
Student Use of E-Learning During the Coronavirus Pandemic

The breakout of the coronavirus disease has brought about a seismic shift in the daily lives of people worldwide. Educational institutions have been temporarily closed, and different types of lockdowns and social distancing approaches have been applied in countries around the world to delay the spread of the disease. This study investigates the determinants influencing university student intentions and use of electronic learning during the pandemic. Data were collected online from 229 respondents in different academic disciplines at various Saudi Arabian universities. The findings revealed that two factors positively affected students' behavioral intention to utilize electronic learning: trust and performance expectancy. Moreover, the findings showed the significance of facilitating conditions and behavioral intention on students' use of electronic learning during the lockdown. The study findings provide some important practical insights on how curriculum and pedagogy should be modified by adjusting learning standards and assessment tools.