Reviewerly/exHarmony huggingface dataset:
Bare conductive ink as a tool to teach pupils EFL vocabulary

Following the most recent models, smart technologies in education are identified as a way to promote multiple learning abilities through digital tools [1]. More specifically, the introduction of smart educational tools can emphasize personalized and custom-made knowledge, interactive learning activities, logical awareness, self-learning and team-based learning. Thanks to these features, it appears that Augmented Reality (AR) in education might be a way to make learning “smarter”. This technology has become more and more popular in language teaching thanks to the high level of active engagement it provides in learners and the favorable outcomes observed through its use [2] [3] [4] [5] [6] [7]. In order to integrate traditional teaching of English as a Foreign Language (EFL) with multimodal and multisensory interventions, a pilot experiment was carried out with the use of “Bare Conductive Ink”, a tool that helps creating interactive objects and environments through the use of sensors-electrodes and conductive ink. Fifteen students between eight and eleven years old carried out an English activity Learning Unit focused on A1 lexicon (Common European Framework of Reference for Languages) using the smart toolkit to listen, explore and repeat words. Despite further research with more participants is needed for more comprehensive results, the aim of the project was to observe the impact of the tool on students’ level of active engagement and interest in the activities and, above all, to investigate its effectiveness, applicability and ease of use, identifying strengths and hurdles when introducing the “Bare Conductive Ink” in class.
A Flexible HLS Hoeffding Tree Implementation for Runtime Learning on FPGA

Decision trees are often preferred when implementing Machine Learning in embedded systems for their simplicity and scalability. Hoeffding Trees are a type of Decision Trees that take advantage of the Hoeffding Bound to allow them to learn patterns in data without having to continuously store the data samples for future reprocessing. This makes them especially suitable for deployment on embedded devices. In this work we highlight the features of a HLS implementation of the Hoeffding Tree. The implementation parameters include the feature size of the samples (D), the number of output classes (K), and the maximum number of nodes to which the tree is allowed to grow (Nd). We target a Xilinx MPSoC ZCU102, and evaluate: the design’s resource requirements and clock frequency for different numbers of classes and feature size, the execution time on several synthetic datasets of varying sizes (N) and the execution time and accuracy for two datasets from UCI. For a problem size of $\mathrm{D}=3, \mathrm{K}=5$, and $\mathrm{N}=40000$, a single decision tree operating at 103MHz is capable of $8.3 \times$ faster inference than the 1.2GHz ARM Cortex-A53 core. Compared to a reference implementation of the Hoeffding tree, we achieve comparable classification accuracy for the UCI datasets.
The Artificial Intelligence Based Diagnostic Assistant – AIDA

Even though interest in Machine Learning Based Clinical Decision Support Systems (ML-CDSS) has been rapidly growing in recent years, most research and development is exclusively focused on secondary and tertiary care – even though effective diagnostic support in primary care could significantly improve both the circumstances, the process and the outcome of general practice. In this paper, we study the suitability of five supervised machine learning algorithms to the problem of multiclass classification with sparse Boolean features on a primary care data set, and we examine the robustness of the algorithms to incomplete data. We introduce our own classification algorithm, the Artificial Intelligence Based Diagnostic Assistant (AIDA), which is capable of incorporating both symptoms and contextual information into its diagnostic process, thus modeling the decision-making of physicians in a novel and accurate manner. Through our experimental results we show that AIDA is by far the most suitable classification algorithm for ML-CDSS applications in primary care, owing to its high accuracy and outstanding robustness to missing, sparse information.
Heal the Privacy: Functional Encryption and Privacy-Preserving Analytics

Secure cloud storage is an issue of paramount importance that both businesses and end-users should take into consideration before moving their data to, potentially, untrusted clouds. Migrating data to the cloud raises multiple privacy issues, as they are completely controlled by a cloud provider. Hence, an untrusted cloud provider can potentially breach users’ privacy and gain access to sensitive information. The problem becomes even more pronounced when the cloud provider is required to store a statistical database and periodically publish analytics. In this work, we first present a detailed example showing that the use of cryptography is not enough to ensure the privacy of individuals. Then, we design a hybrid protocol based on Functional Encryption and Differential Privacy that allows the computations of statistics in a privacy-preserving way.
Optimisation of Generation Models for Clusters of Photovoltaic Plants

The present work analyses the actual production profiles of a group of tens of thousands of PV plants. Actual PV generation profiles are represented as hourly average powers gathered from the energy meters measured at the point of common coupling with the grid. After filtering, data cleaning and statistical analysis, reference PV plants are selected for the improvements of the PV generation models. Production profiles are calculated by using literature generation models, with weather data as inputs. An optimisation is performed on the parameters of the literature models to minimise the differences between the cumulative distribution functions of calculated profiles and measured data. The study compares the performance of the different models and shows how the optimisation increases the quality of the calculated profiles.
Farming Your ML-based Query Optimizer's Food

Machine learning (ML) is becoming a core component in query optimizers, e.g., to estimate costs or cardinalities. This means large heterogeneous sets of labeled query plans or jobs (i.e., plans with their runtime or cardinality output) are needed. However, collecting such a training dataset is a very tedious and time-consuming task: It requires both developing numerous jobs and executing them to acquire ground-truth labels. We demonstrate Datafarm,a novel framework for efficiently generating and labeling training data for ML-based query optimizers to overcome these issues. Datafarmenables generating training data tailored to users' needs by learning from their existing workload patterns, input data, and computational resources. It uses an active learning approach to determine a subset of jobs to be executed and encloses the human into the loop, resulting in higher quality data. The graphical user interface of Datafarmallows users to get informative details of the generated jobs and guides them through the generation process step-by-step. We show how users can intervene and provide feedback to the system in an iterative fashion. As an output, users can download both the generated jobs to use as a benchmark and the training data (jobs with their labels).
E-Health Security on Could Computing and its Challenges

Healthcare credentials digitized on Cloud has reformed the access of healthcare solutions widely; where patient&#x2019;s medical history is recorded periodically on Cloud and can be accessed based on necessity legally. Cloud services and storage has become a gateway for many services globally with multiple storage providers providing storage facilities to access. This paper enumerates the benefits of Electronic Healthcare on cloud and tribulations based on security and privacy, where the concern is a substance, which itself holds a pack of catalogues such as Confidentiality, Integrity, Data Violation, Reliability, Network eavesdropping, Denial of service, Collusion, etc. The foreground of this paper is a survey with different problems and solution possibilities discussed based on cloud storage and communication on security and privacy issues faced and discussed on work papers which are originally published for Electronics Health Records (EHR) or Electronics Medical Records (EMR). The pros and cons of each approach and results are conferred in this literature survey along with the definitions of cloud computing, its types and existing technologies.
TEmoDec: Emotion Detection from Handwritten Text using Agglomerative Clustering

Handwriting analysis is the practice of understanding a person better by examining their handwriting. The traditional approach involves the examination of several parameters like Pen-Pressure, Slant, Baseline, Zone, Margin, and Size of the handwritten text, by an expert. These parameters are indicative of the mental state of the person and can be used to judge the honesty, stress, and depression levels of the person. The disadvantage of this practice is that it is time-consuming and accuracies vary according to the skills of the examiner. In this paper, we propose a novel method based on the Agglomerative Hierarchical Clustering technique, that is able to identify the emotional state of the person by looking at the image of the handwritten text. Thus we are able to achieve reliable accuracies without the need for large annotated datasets by using unsupervised learning. After preprocessing, the image pixels are clustered, based on a threshold value of intra-cluster distances. All pixels with distance, from the centroid of the cluster, lower than the threshold value belong to that cluster. Each cluster corresponds to one of the predefined emotions. We test our model to predict 5 emotions, namely, Anger, Sadness, Depression, Happiness and Excitement. However, the proposed method can be used for more emotions as necessary by changing the threshold distance value. We achieve an accuracy above 75&#x0025; for each of these emotions. Our work may potentially be used in mental health diagnosis, in the hiring process by the industry as well as in criminal investigation.
A Comparative Investigation on the use of Machine Learning Techniques for Currency Authentication

In the present banking sector, identifying the real and the fake note is a very challenging task because if we do it manually, it takes a long time to check which is real and which is fake. This research study article aims to authenticate the money between real and fake by using different machine algorithms facilitating learning, such as K-means Clustering, Random Forest Classification, Support Vector Machines, and logistics Regression. Specifically, we consider the banknote dataset. The data of money is extracted from various banknote images by using the wavelet transform tool, which is primarily used to remove elements from the images. However, we are mainly concerned with the different machine learning algorithms, so we take the two variables, where the first variable indicates image variance and the second indicates image skewness. We use these two variables to train our machine learning algorithms. So, majorly, by applying the different machine learning algorithms, which are supervised and unsupervised, we find the accuracy for the respective machine learning algorithms and then visualize and classify the real and fake notes separately. Finally, the prediction is based on integrity, which means the efficiency value is based on how much the mechanism system can uncover the fake notes. Then, after calculating the accuracy of currency authentication, there is a high possibility that the accuracy of the particular algorithm is the best algorithm, so the application of currency authentication will be very useful for the bank to easily find duplicate notes.
Recognition and Classification of Smiles using Computer Vision

A simple method for the recognition and classification of varied types of smiles using the basics of machine learning is proposed in this paper. Machine-human interaction has seen exponential growth in the last decade. Key features of this interaction include emotion detection. A smiling face is often considered a sign of euphoria and excitement. The analysis is performed on real-time video sequence. The algorithm used for detection is a 68-point facial landmark recognition with aspect ratio calculation of facial features such as mouth and eyes.
Inter country poetry classification using Topic modeling

Poetry is an art of arranging the carefully picked words in a specific order to express the authors experience and emotions. Poetry in India has its strong roots with world famous and excellent poets, amongst few renowned poets are &#x201C;Universal Poet&#x201D; Rabindranath Tagore, &#x201C;Nightingale of India&#x201D; Sarojini Naidu and &#x201C;Swami&#x201D; Vivekananda. Poetry style varies from country to country depending on the author. Author&#x2019;s poetry topics, words and style depends on the circumstances they raised in, situations they faced, and their mind set. Many authors who belongs to India but settled in western countries and written their poems, in this context automatically identifying a poem&#x2019;s author is a challenging task for the literary scholars who analyze the poetry. In this work authors proposed a method based on Latent Dirichlet Allocation(LDA) topic modeling to classify the poetry written by Indian or western(American)poet based on the distribution of topics per document. The experiment is performed on 3 data sets 128, 1600 and author wise poems respectively. This experiment is performed based on semantic features. Best result 91&#x0025; precision and 88&#x0025; accuracy is achieved on author wise poems data set using random forest algorithm
Weakly-Supervised Medical Image Segmentation Based on Multi-task Learning

Medical image segmentation plays an important role in the diagnosis and treatment of diseases. Using fully supervised deep learning methods for medical image segmentation requires large medical image datasets with pixel-level annotations, but medical image annotation is time-consuming and labor-intensive. On this basis, we study a medical image segmentation algorithm based on image-level annotation. Existing weakly supervised semantic segmentation algorithms based on image-level annotations rely on class activation maps (CAM) and saliency maps generated by preprocessing as pseudo-labels to supervise the network. However, the CAMs generated by many existing methods are often over-activated or under-activated, while most of the saliency map generated by preprocessing is also rough and cannot be updated online, which will affect the segmentation effect. In response to this situation, we study a weakly supervised medical image segmentation algorithm based on multi-task learning, which uses graph convolution to correct for improper activation, and uses similarity learning to update pseudo-labels online to improve segmentation performance. We conducted extensive experiments on ISIC2017 skin disease images to validate the proposed method and experiments show that our method achieves a Dice evaluation metric of 68.38% on this dataset, show that our approach outperforms state-of-the-art methods.
Multiple-Point Obstacle Avoidance Based on 3D Depth Camera Skeleton Modeling and Virtual Potential Field for the Redundant Manipulator

For use in unstructured domains, highly redundant robotic systems need both deliberative and compliant control schemes, to avoid collision and safely interact with the dynamic environment. Aiming at the shortcoming of the traditional method of path planning using merely on the typical structure of the manipulator, a new algorithm, named the “skeleton extraction based on 3D-depth camera”, is proposed for the real-time generation of collision avoidance motions. The algorithm is applied to get the distances of the multiple possible collision points and to establish a new form of a repulsive force, which includes the radial repulsive force and tangential repulsive force. For the redundant manipulator, the equilibrium angles through incremental iteration of the moment instead of inverse kinematics to reduce calculation cost. Finally, the method was tested by a 7-DOF manipulator in MATLAB environment. The results show that the proposed method can avoid local minima traps and eliminate oscillations effectively.
A Deep Multi-task Generative Adversarial Network for Face Completion

Face completion is a challenging task that requires a known mask as prior information to restore the missing content of a corrupted image. In contrast to well-studied face completion methods, we present a Deep Multi-task Generative Adversarial Network (DMGAN) for simultaneous missing region detection and completion in face imagery tasks. Specifically, our model first learns rich hierarchical representations, which are critical for missing region detection and completion, automatically. With these hierarchical representations, we then design two complementary sub-networks: (1) DetectionNet, which is built upon a fully convolutional neural net and detects the location and geometry information of the missing region in a coarse-to-fine manner, and (2) CompletionNet, which is designed with a skip connection architecture and predicts the missing region with multi-scale and multi-level features. Additionally, we train two context discriminators to ensure the consistency of the generated image. In contrast to existing models, our model can generate realistic face completion results without any prior information about the missing region, which allows our model to produce missing regions with arbitrary shapes and locations. Extensive quantitative and qualitative experiments on benchmark datasets demonstrate that the proposed model generates higher quality results compared to state-of-the-art methods.
Outlier Constrained Unsupervised Domain Adaptation Algorithm for Gaze Estimation

In recent years, gaze estimation has been applied to numerous application areas, such as driver monitor system, autism assessment, and so on. However, current practical gaze estimation algorithms require a large amount of data to obtain better results. The collection of gaze data requires specific equipment, and the collection process is cumbersome, tedious and lengthy. Moreover, in some scenarios, like the autism assessment scenario, it is impossible to obtain the gaze training data of autistic children due to their social communication disorders. Therefore, we need to generalize a model trained on public datasets to a new scenario without gaze ground truth labels. In this study, we tackle this problem by leveraging adversarial learning to implement domain adaptation. Besides, we propose an outlier loss to supervise the outputs of the target domain. We test our domain adaptation algorithm on the XGaze-to-MPII domain adaptation task, and achieve a performance improvement of 14.7%.
A Self-attention Network for Face Detection Based on Unmanned Aerial Vehicles

Face detection based on Unmanned Aerial Vehicles (UAVs) faces following challenges: (1) scale variation. When the UAVs fly in the air, the size of faces is different owing to the distance, which increases the difficulty of face detection. (2) lack of specialized face detection datasets. It results in a sharp drop in the accuracy of algorithm. To address these two issues, we make full advantage of existing open benchmarks to train our model. However, the gap is too huge when we adapt face detectors from the ground to the air. Therefore, we propose a novel network called Face Self-attention Network (FSN) to achieve high performance. Our method conducts extensive experiments on the standard WIDER FACE benchmark. The experimental results demonstrate that FSN can detect multi-scale faces accurately.
Effectiveness of the Education System: Comparative Analysis of the Estimated Data Parameters

The development of the education system and globalization of forms of interaction require the construction of general evaluation parameters of the data. First of all, this issue consists in studying the indicative base, which makes it possible to assess the effectiveness of the education system. The selected condition is due to the importance and significance of taking into account quantitative and qualitative changes that slow down the level of development of the education system in the Russian Federation and foreign countries. The main purpose of this study is to conduct a comparative analysis of the estimated parameters of the data in the area of determining the effectiveness of the education system. The result of the scientific research is the following aspects: the system of the Russian education is considered, the main institutions for monitoring the estimated parameters of data in the area of education are identified, and comparative analysis of the estimated parameters of data on the effectiveness of the education system of foreign countries and the Russian Federation is carried out. The research tools are the following methods: indicative analysis, grouping and generalization of data, comparison and consolidation of forms, sentences, identification, as well as strange differentiation of statistical information.
Subsea Pipeline Inspection Based on Contrast Enhancement Module

Due to the high turbidity of the water and lack of lighting in deep sea, the image of subsea pipeline are blurred and lack of brightness. In the paper an algorithm is proposed to extract centerline of underwater pipeline using image enhancement and pipeline edge detection. The enhancement module based on the color space transformation is given to improve image contrast. Also the threshold segmentation algorithm is put forward to calculate the parameters of Canny operator for edge detection. The centerline of the pipeline is extracted based on the probabilistic Hough transform. Experimental results show that the proposed algorithm is effective and robust.
Overtaking Trajectory Planning Based on Model Predictive Control

Autonomous driving technology can greatly increase road safety and reduce accidents, and has become a hot research topic in academia and industry today. However, traditional vehicle motion planning methods often have difficulty in balancing real-time performance with trajectory quality. This paper designs a trajectory planning method based on model predictive control technology, which transforms the motion planning problem into a quadratic planning problem. Compared with previous methods, the proposed method can generate trajectories that meet the requirements of vehicle kinematics and satisfy the requirements of comfort and energy saving while ensuring obstacle avoidance and real-time, and verifies the feasibility of this method in simulation experiments.
An Active Obstacle Avoidance Method

Swarm robots often encounter dynamic obstacles when performing tasks, such as moving objects in the scene or other individuals in the robot group. The traditional passive obstacle avoidance method makes the robots take emergency avoidance behaviour when it is about to encounter obstacles. Hoverer this may destroy the group cooperation behaviour, thereby affecting the efficiency of the system. Active obstacle avoidance perceives a dynamic target and predicts the movement of the target and takes the initiative to avoid obstacles, minimizes the impact of obstacle avoidance on the system’s cooperative behaviour. Considering that the defects in the structural design of swarm robots and the avoidance strategy of swarm robots, it is necessary to focus on active obstacle avoidance of swarm robots that is based on the prediction of dynamic targets. An improved obstacle avoidance method is therefore proposed, which enables robots to avoid both static and dynamic obstacles.