Reviewerly/exHarmony huggingface dataset:
Little Genius: An Experiment in Internet of Tangible Learning Things

The Internet of Tangible Things (IoTT) provides new opportunities for building technology-enhanced learning systems that wirelessly connect tangible sensor data to remote learning services. This paper presents the design and implementation of an IoTT voice driven learning platform that uses Amazon Alexa along with sensor-enabled wireless dice to build tangible and interactive math games for kindergarten and primary school children. The wireless dice used sensors in a 9-axis IMU to accurately detect the face each die falls on and sent this information to Amazon Web Services (AWS) using the MQTT protocol using AWS-IoT service to enact arithmetic learning scenarios. An initial evaluation shows that most children from ages 5 to 9 enjoyed playing with this system. Half the children liked everything about the game, and they all liked the concept. This game is a first step towards a new genre of educational learning technology called Internet of Tangible Learning Things (IoTLT).
Using High Boost Bi-cubic Interpolation to Upscale and Enhance the Medical Image Details

Most of the interpolation methods used for upscaling medical images often give the blur version. It affects to accurate diagnosis of both doctor and veterinary. In this paper, we propose the interpolation method that can be both interpolated and sharpness increased simultaneously. This method is derived from a high boost operator and the third-order polynomial for the interpolation formula construction in the same fashion of the bi-cubic interpolation. As the uniform image has low contrast that leads to the low standard deviation(SD), we use SD as an indicator for our method and traditional bi-cubic interpolation. The experimental results show that our proposed method can be used to upscale and enhance the sharpness of the medical images. Because of the larger SD in our method, our processed image has higher contrast. Moreover, the consuming time of image upscaling is close to the bi-cubic consuming time. Besides, the algorithm quite helps us to see clearly the blur detail and the boundary of the organs. It also gives the undistinguished things which may be necessary for diagnosis. However, our proposed algorithm produces more unpleasant noise if we apply with the large boost coefficient.
Speaker Verification Method Using HTM for Security System

In recent years, we have been using biometric verification systems in various places such as daily life and businesses. However, it’s insufficient in hospital and food factory to introduction of the security system of the room access control. This is because they wear gloves, masks and hats in hospitals and factories, so they cannot verify faces or fingerprints. In this study, we propose a speaker verification system using HTM (Hierarchical Temporal Memory). We verify speaker and detection of unlearned speakers by experiments. In addition, we consider the use in hospitals and factories, and also evaluate it in a noisy environment. The evaluation values are Precision, Recall, and F-measure. The experimental results show that in the case of 10 learners, F-measure exceeded 0.6 in the classification of 2 classes of learners and non-learners.
Improvement of Merkle Signature Scheme by Means of Optical Quantum Random Number Generators

The corporation Google, NASA and have teamed up with D-Wave, the global manufacturer of quantum CPUs. Quantum computers will have the opportunity to hack almost every cryptosystem, which is used in the majority of products, for example, they will be able to break RSA. RSA crypto scheme is the part of many products, which are used in different areas on the various platforms. Nowadays, this crypto scheme is the part of the basic commercial systems; the number of these systems is actively increasing. Hash-based digital signature schemes offer an alternative, which can be considered safe against the attacks of quantum computers. These digital signature schemes use the cryptographic hash function. The security of the hash based digital signature schemes is based on the collision resistance of the hash function, which they use. In 1979, Ralph Merkle proposed Merkle signature scheme. Merkle signature scheme has an efficiency problems, so it cannot be used in practice. World scientists are working on improving the scheme. One of the improvements is integrating PRNG (pseudo random number generator) not to calculate and store the large amount of one-time keys pairs. This approach cannot be considered secure, because according to our research quantum computers are able to crack PRNG, which were considered safe against attacks of classical computers. In the article it is offered to use the concrete pseudo random number generator and the true random number generator for generating the seed. As a pseudo random number generator it is offered to use an algorithm based on a hash function, as the whole the algorithm is based on it. NIST has recommended two continuous hash based PRNGs: HASH_DBRG and HMAC_DBRG. In the article it is offered to use HASH_DBRG as it is more efficient. As the true random number generator it is offered to use physical quantum random number generator (QRNG) for generating the seed for HASH_DBRG. The implementation algorithm of the scheme is offered and the security of this scheme is analyzed. The offered scheme is rather efficient, because it does not store all the signature keys. The practical experiment shows that the algorithm is also rather fast. The offered scheme is secure and can be used as post-quantum security tool.
Exploring the Turning Points for Intelligent Traversal with Q-Learning Models

The real reason behind a success or failure need not be the decisions or actions taken in the immediate past, but can be an event occurred in the distant past, as mentioned in Blame Attribution problems. The crucial action points capable of influencing or even deciding the ultimate nature of future outcomes are known as Turning Points. The turning points in real life or gaming situations are similar to junctions of roads diverting to one among the more favorable, less favorable or unfavorable destinations. Intelligent systems with adaptive learning capabilities can help to identify the real turning points using score values assigned to various states transition possibilities during previous epochs. This is a study on attaining knowledge about the turning points through repeated alternate exploration epochs using a basic Q-Learning model of a maze and the scope of exploiting this knowledge during the decision making situations in the future which demand crucial selection among multiple alternative options.
Face Recognition Based on Multiple Video Cameras

Face recognition is a fundamental task that enables advanced applications in video surveillance, human-computer interaction, and security. Existing methods of face recognition do not perform well on non-frontal faces, which often come from multiple surveillance cameras. This research aims to develop a method for partial face recognition, using images from multiple video cameras as a source and recognizing them against frontal images in a database. A key idea in the proposed method is to carefully evaluate a similarity between a set of video images from cameras and a frontal facial image from the database. We design two methods to evaluate the similarity. The first method directly measures the similarity from transformed video images. The second method fuses facial features of video images before measuring the similarity. We quantify performance of the proposed methods by comparing them with a competitive baseline using a public dataset. The comparison shows that the proposed methods have the highest recognition rates in four out of six test cases and have the recognition rates of thirty to seventy percent. The proposed methods of face recognition are promising and can recognize a face in difficult situations, including faces under occlusion or in a challenging environment.
Privacy-Preserving Mobile Video Sharing using Fully Homomorphic Encryption

Increased availability of mobile cameras has led to more opportunities for people to record videos of significantly more of their lives. Many times people want to share these videos, but only to certain people who were co-present. Since the videos may be of a large event where the attendees are not necessarily known, we need a method for proving co-presence without revealing information before co-presence is proven. In this demonstration, we present a privacy-preserving method for comparing the similarity of two videos without revealing the contents of either video. This technique leverages the Similarity of Simultaneous Observation technique for detecting hidden webcams and modifies the existing algorithms so that they are computationally feasible to run under fully homomorphic encryption scheme on modern mobile devices. The demonstration will consist of a variety of devices preloaded with our software. We will demonstrate the video sharing software performing comparisons in real time. We will also make the software available to Android devices via a QR code so that participants can record and exchange their own videos.
Method of Recognition and Indexing of People’s Faces in Videos Using Model of Machine Learning

This paper describes a machine learning approach for visual object detection in particular people's faces. Developed an algorithm based on the Viola-Jones algorithm. Images are used in the integral representation, which allows you to quickly calculate the necessary objects; signs of Haar are used, with the help of which the search for the needed faces and their features; boosting is used to select the most suitable features for the desired object in a given part of the image. Features of implementation of AdaBoost algorithm for solution a problem of construction of the classifiers cascade for effective detection of objects on images are considered in this paper. The algorithm is tested on an image containing several persons. Graphs of dependence of quality of detection on quantity of iterations are received.
Educational Group Recommendations with Virtual Leaders

Recommender systems have been applied in the area of education to suggest learning materials, assist informal learning, recommend projects or advisors, and so forth. In the process of teaching or learning, students may work together in a group. In this case, the group recommendations may come into play. In this paper, we propose to build a novel group recommendation model for education by introducing a virtual leader to each group. More specifically, we represent these virtual leaders by the personality traits, and learn these representations towards the optimization of the recommendations. Our experimental results demonstrate the effectiveness of the proposed models in comparison with other group recommendation models.
Scientific Potential of the Teacher: in a Specialized School and College

The effectiveness of the educational process in a modern College largely depends on the level of scientific potential of teachers. Scientific potential is shown in the development of educational programs and methods of conducting classes. The scientific content of College education contributes to the formation of professional competencies of the future mid-level specialist in demand on the labor market. At the same time, it is equally important to conduct advanced training of personnel, taking into account the new technological structure that is emerging in our country and in the world. The continuity of the educational process in the profile school with the content of education in the College will contribute to the successful entry of former students into the system of secondary vocational education.
Multi-Agent Implementation of Filtering Multiset Grammars

Chapter is dedicated to the application of multi-agent technology to generation of sets of terminal multisets (TMS) defined by filtering multiset grammars (FMG). Proposed approach is based on creation of multi-agent system (MAS), corresponding to specific FMG in such a way, that every rule of FMG is represented by independently acting agent. Such MAS provides high-parallel generation of TMS and may be effectively used in any proper hardware environment. Directions of further development of the proposed approach are discussed.
Learning to Compare with Few Data for Personalised Human Activity Recognition

Recent advances in meta-learning provides interesting opportunities for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist view of learning-to-learn, such that models are rapidly transferable to related but different new tasks. Unlike task-specific model training; a meta-learner’s training instance, referred to as a meta-instance is a composite of two sets: a support set and a query set of instances. In our work, we introduce learning-to-learn personalised models from few data. We motivate our contribution through an application where personalisation plays an important role, mainly that of human activity recognition for self-management of chronic diseases. We extend the meta-instance creation process where random sampling of support and query sets is carried out on a reduced sample conditioned by a domain-specific attribute; namely the person or user, in order to create meta-instances for personalised HAR. Our meta-learning for personalisation is compared with several state-of-the-art meta-learning strategies: 1) matching network (MN) which learns an embedding for a metric function; 2) relation network (RN) that learns to predict similarity between paired instances; and 3) MAML, a model agnostic machine learning algorithm that optimizes the model parameters for rapid adaptation. Results confirm that personalised meta-learning significantly improves performance over non personalised meta-learners.
Capturing User-defined Facial Features for Scientific Evidence of Elderly Care

To achieve the scientific long-term care supported by interpretable evidence, we propose the “facial expression sensing service” in this paper. The proposed service allows a user to define custom facial features, so as to capture subtle changes of facial expression. Once the features are defined, the service automatically measures and records the values from real-time media stream obtained from a camera. In the operation, the service recognizes a face of a target person within a media stream, measures the features with timestamp, and records the data in a database. The data can be used to understand how the target person changes the emotion before, during, and after the care treatments. To show the practical feasibility, we conduct an experiment that investigates emotion of elderly people talking to a virtual agent.
Comparative Assessment of Different Deep Learning Models for Aircraft Detection

Object detection in satellite imagery is very important for a wide array of applications in surveillance system, monitoring tasks etc. The satellite images have lower resolution as compared to aerial images and hence detecting smaller objects such as vehicles, aircrafts in a remotely sensed image is a challenging task. In this paper, we focus on the comparative study of three different models namely YoloV3, SSD and RCNN. We have tested all the three models to find out which model performed best for the task of airplane detection when trained on aerial images and tested for small object detection (airplanes in our case) on satellite images. Finally, we illustrated the comparison of the three models on the basis of accuracy, losses etc.
Effects of Color Quantization on JPEG Compression

This paper analyzes the effects of color quantization on standard JPEG compression. Optimized color palettes were used to quantize natural images, using dithering and chroma subsampling as optional. The resulting variations on file size and quantitative quality measures were analyzed. Preliminary results, using a small image database, show that file size suffered an average 20% increase and a concomitant loss in quality was perceived ([Formula: see text]6dB PSNR, [Formula: see text]0.16 SSIM and [Formula: see text]9.6 Butteraugli). Color quantization present itself as an ineffective tool on JPEG compression but if necessarily imposed, on high quality compressed images, it might lead to a negligible increase in data size and quality loss. In addition dithering seems to always decrease JPEG compression ratio.
SVM on HPC Clouds: Choosing the Appropriate Classification Algorithm and Kernel Type According to the Data Set Characteristics

Support Vector Machines algorithms, have been and are still widely used for several Machine Learning tasks, thanks to many advantages including efficiency, precision, and great performance. Lately, the need for processing large scale data is dramatically increasing, in which High-Performance Computing Clouds have proved to be the ultimate solution. However, in the specific case of SVMs, with their different types, these applications focus on the search for performance in terms of precision and reduction of the error rate, generally on one or several data sets representing a concrete use case. This work seeks to extract a clearer idea about the suitability of each SVM and Kernel type, according to the example studied, and the impact of the distributed computing on the results.
Translational Personas and Hospital Library Services

Academic health centers, CTSA hubs, and hospital libraries experience similar funding challenges and charges to do more with less. In recent years academic health center and hospital librarians have risen to these challenges by examining their service models, and beyond that, examining their patron base and users’ needs. To meet the needs of employees, patients, and those who assist patients, hospital librarians can employ the CTS Personas, a project of the Clinical and Translational Science Awards (CTSA) Program National Center for Data to Health. The Persona profiles, which outline the motivations, goals, pain points, wants, and needs of twelve employees and two patients in translational science, provide vital information and insights that can inform everything from designing software tools and educational services, to advertising these services, to designing impactful and collaborative library spaces.
Surrogate dropout: Learning optimal drop rate through proxy

Dropout is commonly used in deep neural networks to alleviate the problem of overfitting. Conventionally, the neurons in a layer indiscriminately share a fixed drop probability, which results in difficulty in determining the appropriate value for different tasks. Moreover, this static strategy will also incur serious degradation on performance when the conventional dropout is extensively applied to both shallow and deep layers. A question is whether selectively dropping the neurons would realize a better regularization effect. This paper proposes a simple and effective surrogate dropout method whereby neurons are dropped according to their importance. The proposed method has two main stages. The first stage trains a surrogate module that can be jointly optimized along with the neural network to evaluate the importance of each neuron. In the second stage, the output of the surrogate module is regarded as a guidance signal for dropping certain neurons, approximating the optimal per-neuron drop rate when the network converges. Various convolutional neural network architectures and multiple datasets, including CIFAR-10, CIFAR-100, SVHN, Tiny ImageNet, and two medical image datasets are used to evaluate the surrogate dropout method. The experimental results demonstrate that the proposed method achieves a better regularization effect than the baseline methods.
Reply to “An Approach to Manage Epistaxis in the COVID‐19 Pandemic in Resource‐Constrained Settings”

titled ''Clinical Recommendations for Epistaxis Management During the COVID-19 Pandemic,'' published in Otolaryngology-Head and Neck Surgery.I appreciate the authors sharing the important guidelines at this important time.I also agree with them about the staged recommendations: personal protection, clinical assessment, room setting, treatment, and postprocedural.However, I feel that in a resource-constrained country such as India, these recommendations cannot be strictly followed because of underfunded health care infrastructure and the burden of patients.The tertiary care institutes across the country have kept COVID-19 testing to a limited number of patients, who present with definitive symptoms or have a strong suspected history.These are according to government guidelines for the judicious use of resources. 2Most institutes have made a screening desk where all patients coming to emergency care are screened for COVID-19 symptoms and related history.All health care professionals are instructed to follow the universal precautions of hand sanitization, mask wearing, and social distancing.Even if the patient is asymptomatic and has tested negative for COVID-19, one cannot be considered entirely safe, as real-time reverse-transcriptase polymerase chain reaction has a high rates of false-negative results. 3Moreover, clearing the screening desk does not guarantee a patient's safety profile.Personal protective equipment is also in limited supply, and we can face further scarcity as the pandemic is still unfolding.So it is the duty of health care professionals to not compromise clinical care, while keeping themselves protected from the exposure.Another point of concern regards diagnostic nasal endoscopy, as it is not mentioned.Nasal endoscopy helps to rule out various mass lesions and gives a clear picture of the nasal cavity and nasopharynx.It is also considered a minimal invasive procedure.As such, it is advisable in cases where bleeding stops after a noninvasive measure.Another point that can be considered is the role of posterior nasal packing in recommended cases and giving it importance in this COVID-19 era.Finally, I thank the authors and hope that they and readers find some of this information useful.
Information Needs: Lessons for Programming Tools

Why is programming sometimes so frustrating and annoying and other times so fast and painless? This article surveys a few of the important lessons emerging from studies of programming and the new programming tools they motivate.
Silencing and freedom of speech in UK higher education

Freedom of speech in universities is currently an issue of widespread concern and debate. Recent empirical findings in the UK shed some light on whether speech is unduly restricted in the university, but it suffers from two limitations. First, the results appear contradictory. Some studies show that the issue of free speech is overblown by media reportage, whilst others track serious concerns about free speech arising from certain university policies. Second, the findings exclude important issues concerning restrictions to speech on campus that fall outside the traditional debate around violations to free speech rights. This is particularly the case when certain voices are excluded from important policy conversations, and in issues around diversifying the curriculum. This article overcomes these two limitations by developing a novel conceptual framework within which to situate current debates concerning speech‐related matters in universities. It does so by developing a taxonomy around the concept of ‘silencing’. It then considers the current issue of speech matters in universities within this framework to determine whether, and to what extent, speech is indeed unduly restricted, and where this is a concern for free speech violations, and where it falls outside this issue.