Reviewerly/exHarmony huggingface dataset:
Home health monitoring for orthopaedic rehabilitation

The monitoring system has been developed for serious cases of orthopaedic injury and for the health of older people&apos;s lifestyles. It has been built for testing and discussion, and the functionality was the most important in contrast to the form aspect. Other issues, such as defence, maintenance and economies of scale, were significant but outside of our control. This is important if the device is utilised but not sufficient for our tests. Home orthopaedic rehabilitation health monitoring Connecting products and installations that eliminate human involvement to live a better existence. Acceleration data are supplied by employing accelerometers for different axis x, y, z in this project. The measuring signals in the motion of the two accelerometers were mounted one on the upper knee and the other on the bottom of the knee, from either side to the knee. Any motions in the joints modify the accelerometers which cause the accelerometer generate the output voltage in relation to gravity variations. Moreover, in combination with heartbeat, the temperature is generated by the output of the alcohol concentration sensor which is sent into the microcontroller. In addition, the microcontroller processes this information to deliver the output transmitted over Bluetooth on the Android mobile device. The android app translates incoming messages to give physician and caregiver detailed information and warning.
Efficient Homomorphic Evaluation on Large Intervals

Homomorphic encryption (HE) is being widely used for privacy-preserving computation. Since HE schemes only support polynomial operations, it is prevalent to use polynomial approximations of non-polynomial functions. We cannot monitor the intermediate values during the homomorphic evaluation; as a consequence, we should utilize polynomial approximations with sufficiently large approximation intervals to prevent the failure of the evaluation. However, the large approximation interval potentially accompanies computational overheads, and it is a serious bottleneck of HE application on real-world data. In this work, we introduce domain extension polynomials (DEPs) that extend the domain interval of functions by a factor of <i>k</i> while preserving the feature of the original function on its original domain interval. By repeatedly iterating the domainextension process with DEPs, we can extend with <i>O</i>(log<i>K</i>) operations the domain of a given function by a factor of <i>K</i> while the feature of the original function is preserved in its original domain interval. By using DEPs, we can efficiently evaluate in an encrypted state a function that converges at infinities, i.e., lim<sub><i>x</i>&#x2192;&#x221E;</sub> <i>f(x)</i> and lim<sub><i>x</i>&#x2192;-&#x221E;</sub> <i>f(x)</i> exist in R. To uniformly approximate the function on [&#x2013;<i>R,R</i>], our method exploits <i>O</i>(log<i>R</i>) operations and <i>O</i>(1) memory. This is more efficient than the previous approach, the minimax approximation and Paterson-Stockmeyer algorithm, which uses &#x03A9;(&#x221A;<i>R</i>) multiplications and &#x03A9;(&#x221A;<i>R</i>) memory for the evaluation. As another application of DEPs, we also suggest a method to manage the risky outliers from a large interval [&#x2013;<i>R,R</i>] by using <i>O</i>(log<i>R</i>) additional multiplications. As a real-world application, we trained the logistic regression classifier on large public datasets in an encrypted state by using our method. We exploit our method to the evaluation of the logistic function on large intervals, e.g., [-7683, 7683].
An Adaptive Geometry and Dual Graph Approach to Sign Prediction for Weighted and Signed Networks

In this paper, we propose a new SVM method for predicting signs of edges in weighted and signed networks. Our method is based on the notions of dual-graph operation and filtered neighborhoods of nodes in dual graphs, which allows to introduce a geometric structure on the set of nodes of dual graphs and lead to a modified SVM method for predicting edge signs in weighted and signed networks. We test our method on several real datasets.
The Effect of Trigonometric Basis Function on Functional Link Neural Network with Ant Lion Optimizer

This paper assessed the implementation of trigonometric basis function as input enhancement for Functional Link Neural Network (FLNN) trained with Ant Lion Optimizer (ALO) learning algorithm. The previous work of FLNN trained with ALO model used the tensor model to introduce nonlinearities in its inputs features enhancements. One of the major concerns of using the tensor model is that when FLNN has large number of input features, the network may contain many higher-order terms which could lead to combinatorial explosion in the number of weights as the order of the network becomes excessively high. To avoid this, trigonometric basis function in implemented in the model. The result on classification performance made by FLNN with trigonometric basis architecture and FLNN with tensor model architecture both trained with ALO were carried out. From the result achieved, the implementation of the FLNN with trigonometric basis trained with ALO performs the classification task quite well and yields better accuracy on the unseen data.
Institutions of Primary Professional Music Training in Russia in the 1990s (on the Example of the Ulyanovsk Region)

Relevance. In the first decades of the XXI century, the quality of prefecting musical education occurred in Russia. Outlooking programs implemented in children's music schools and musical departments of children's art schools do not allow students to fully master the game on musical instruments and get vocal skills necessary to continue vocational education. The beneficial effect on the improvement of the named region may also have an analysis and subsequent introduction into the practice of past experience, the achievements of the preceding generations of compatriots. The purpose of this article is to cover approaches to the organization of educational activities and the educational process in institutions of initial professional music training of the Ulyanovsk region in the 1990s. The objectives of the study: to determine the basic principles of the state policy of the 1990s in the field of do-mestic education as a whole and musical education in particular; explore and summarize published and unpublished materials containing information on approaches to the organization of educational activities and educational process in the children's schools of the arts of the Ulyanovsk region in the 1990s. Methodology. Regulatory legal acts and archival documents were made as sources of this work. The study was also carried out with a support for published materials (monographs, articles). The paper used a number of scientific approaches, the main of which were the principle of objectivity, the method of system analysis, historical and comparative and historical and typological methods. Results. The study showed that in the 1990s, the Ulyanovsk region had accumulated positive experience in the implementation of educational programs in the field of musical art in institutions of primary professional music training, which can be used today. In particular, issues were effectively solved with the creation of the publicly available to this type of education, ensuring the continuity of the training of educational and continuity of educational programs. Conclusion. The results of the study seems to be able to use when creating educational programs in the field of musical art in the Russian Federation at the present stage.
Research on the Evaluation Method of Military Maintenance Material’s Distribution Capability Based on Cloud Logistics

How to efficiently select the distribution company and complete the distribution task according to the needs of military equipment has become a key link to solve the problem of sporadic distribution of maintenance equipment. In view of the shortage of resources, low efficiency and lack of information solutions in sporadic distribution of maintenance material, this paper puts forward the idea of maintenance material distribution based on cloud logistics. On the basis of analyzing the distribution process of maintenance material based on cloud logistics, this paper confirms that the evaluation of distribution capacity is the premise and foundation of carrying out maintenance material distribution based on cloud logistics. Then, through the construction of evaluation index system and evaluation model, the evaluation method of maintenance material distribution capacity based on cloud logistics is deeply studied. Finally, the feasibility of the evaluation method is verified by an example.
An Improved Algorithm for Occlusion Tracking Based on FDSST and Kalman Filtering

Aiming at the problem that fast discriminant scale-space tracking (FDSST) algorithm cannot solve the tracking problem of the agent target reappearing after a long-time and large-area occlusion, an improved FDSST algorithm based on Kalman filter is proposed. Firstly, the agent image sequence is labeled under FDSST architecture; Then, the maximum response value and average peak correlation energy are introduced as the occlusion judgment criteria. When the occlusion leads to the tracking failure, the Kalman filter is used to evaluate and predict the location of the agent, and the tracking of the agent target is realized combined with the incomplete information of FDSST; Finally, compared with the original FDSST algorithm, the improved FDSST algorithm is verified to get better performance by evaluating the data sets under four different attributes: occlusion, scale transformation, fast motion and motion blur in the online tracking benchmark (OTB) and the agent image sequence on the CoppeliaSim simulation platform.
Using Random Forest Distances for Outlier Detection

In recent years, a great variety of outlier detectors have been proposed in the literature, many of which are based on pairwise distances or derived concepts. However, in such methods, most of the efforts have been devoted to the outlier detection mechanisms, not paying attention to the distance measure – in most cases the basic Euclidean distance is used. Instead, in the clustering field, data-dependent measures have shown to be very useful, especially those based on Random Forests: actually, Random Forests are partitioners of the space able to naturally encode the relation between two objects. In the outlier detection field, these informative distances have received scarce attention. This manuscript is aimed at filling this gap, studying the suitability of these measures in the identification of outliers. In our scheme, we build an unsupervised Random Forest model, from which we extract pairwise distances; these distances are then input to an outlier detector. In particular, we study the impact of several Random Forest-based distances, including advanced and recent ones, on different outlier detectors. We evaluate thoroughly our methodology on nine benchmark datasets for outlier detection, focusing on different aspects of the pipeline, such as the parametrization of the forest, the type of distance-based outlier detector, and most importantly, the impact of the adopted distance.
On the Value Demand and Implementation Strategy of Innovative Education in Colleges and Universities in the Information Age

With the development of Information Technology, university innovation education based on information technology has gradually attracted great attention of colleges and universities, with the overall improvement of the quality of the educated as the main means of implementation, to train them to become an all-round development of the new era of talent and all-round development of human values. Therefore, in the era of information technology, colleges and universities should establish a sound talent training mechanism, improve the construction of curriculum system, stimulate students’ learning motivation, encourage students to explore learning, and deal well with the relationship between innovative education and basic education, truly realizing the well-being of mankind brought about by innovative education [1].
Improving Type Error Reporting for Type Classes

Debugging type errors when type inference fails is a challenging problem since there are many different ways to remove the type error, and it’s unclear which way is intended. While numerous approaches have been proposed to more precisely locate the real error causes, most of them do not deal with popular type system extensions, such as type classes. A second problem is that most approaches do not provide enough information for removing the type error or do so for a few error causes only. In this work, we develop an approach called TEC to address both problems. Given an ill-typed expression that may involve type classes, TEC finds comprehensive error causes and generates for each cause an error fix with detailed information for removing the type error. TEC computes all error fixes, ranks them, and iteratively presents the most likely error fix to the user until a fix is accepted. TEC reduces the problem of finding all error fixes to variational typing, which systematically reuses typing results. Our main technical innovation is a variational context reduction algorithm that simplifies type class constraints containing variations. We have evaluated the precision of TEC and found that it outperforms existing approaches in locating type errors arising from type class uses.
Artificial Intelligence Based Techniques for Underwater Image Restoration and Enhancement

Under-water picture enhancement has received much attention in under-water vision research. Under-water picture suffers from serious color distortion and low contrast problems because of complex light propagation in the ocean. However, raw under-water pictures easily suffer from underexposure, and fuzz caused by the under-water scene. Many of the research are going on to recover this under-water blurred image. Artificial intelligence based machine learning (ML) and deep learning (DL) techniques are optimizing better restoration rather than the conventional approaches. This paper presents an artificial intelligence based techniques for underwater image restoration and enhancement.
Augmented Reality Interventions in Shared Space: Subversion and Social Impact

A conversation between writer/curator Jesse Damiani and artist Nancy Baker Cahill about her innovative augmented reality practice, their artistic collaborations, and the potential of augmented reality technology to affect social change. The interview examines the possibilities and limitations of contemporary AR as an artistic medium, the ways AR can be used as a novel form of public art and idea activation, and the evolving relationships that AR interventions can foster among artists and audiences. The interview focuses on the intervention as a form that artists can use to invite new understandings of physical and virtual space. By intervening physical spaces with augmented experiences, artists can highlight unseen or underexplored social issues, provoke new conversations around them, and drive new understandings of these sites in ways that prompt social change within local communities and among the broader public.
Implementation of Artificial Intelligence Based Sustainable Smart Voice Assistance

Artificial Intelligence (AI) technologies are being part in the human life. This article is related to the implementation of an artificial intelligence-based voice assistance system, which works by the user given commands as a request and give back the output as a response in the speech format. Core innovations are voice initiation, automated speech synthesis, speech-To-Speech, understanding common language. The proposed voice assistance system helps us to make a hand-free model, which acts as a personal assistance and mimics same as like human. Applicability and usability of the proposed model is to create an intelligent mechanism between human and computers as a natural language. Python plugins are used to train the system by using various libraries such as speech recognition, pyttsx, pyAudio. The customization of this project model makes it more flexible and freer to add new features and functionalities without disturbing the current system functionalities. It assists to eradicate the unnecessary kind of manual work required in the user life which will be performed in daily activities, not only does it operate on human instructions, but it also refers to the user based on the question or terms demanded. This Intelligent assistance communicate with the user as a result it gives a desired output as a response to the user as a voice and displays its response on the screen of the user gadget.
Adaptive Structure-Oriented Full-Waveform Inversion with Restarted L-Bfgs Algorithm

Summary FWI requires a lot of simulations, which make it computationally intensive. The restarted L-BFGS algorithm makes full use of the characteristics of shot-encoding method and L-BFGS algorithm to reduce the computational cost. However, the FWI with restarted L-BFGS algorithm suffers from crosstalk. To reduce crosstalk problem, we propose an adaptive structure-oriented FWI with restarted L-BFGS algorithm. We carry out the structure-oriented implementation by solving implicit anisotropic diffusion system. We use multi-scale inversion strategy and add structure-oriented smoothing around the dominant frequency. The structural information used for the smoothing in the current step is obtained from the inversion result of previous step. Moreover, Structure-oriented smoothing is added to the iteration only when the restarted L-BFGS algorithm coding remains unchanged. An overthrust model is applied to test the validity of this algorithm. The result indicates that adaptive structure-oriented FWI can effectively mitigate crosstalk and produce a high-resolution result.
Avoiding Shortcuts in Unpaired Image-to-Image Translation

Image-to-image translation is a very popular task in deep learning. In particular, one of the most effective and popular approach to solve it, when a paired dataset of examples is not available, is to use a cycle consistency loss. This means forcing an inverse mapping in order to reverse the output of the network back to the source domain and reduce the space of all the possible mappings. Nevertheless, the network could learn to take shortcuts and softly apply the target domain in order to make the reverse translation easier therefore producing unsatisfactory results. For this reason, in this paper an additional constraint is introduced during the training phase of an unpaired image-to-image translation network; this forces the model to have the same attention both when applying the target domains and when reversing the translation. This approach has been tested on different datasets showing a consistent improvement over the generated results.
User Experience and Service Mode of Telecare System with Handheld Devices

In developed countries, aging, chronic diseases, and preventive health management have been important topics of concern. Many entrepreneurial have entered the start-up market, trying to integrate design concepts related to medical institutions, wearable devices, health services, cloud computing to build a better and more convenient platform. How to realize online medical services of traditional medical institutions through a handheld user interface and provide users with friendly and reassuring medical services is a thorny problem faced by interactive designers. This study focuses on telecare systems' user experience and service mode with handheld devices. The research methodology uses the concepts of co-creation workshops and the discount usability engineering. In this study, six experts with relevant telecare or interaction knowledge were invited to participate in a co-creation workshop. A persona and customer journey map were created after the workshop. Existing applications were then used to integrate typical tasks. Participants would use the think-aloud technique to simultaneously manipulate the tasks and speak their thoughts when conducting heuristic evaluations. Researchers recorded their task operations and conduct brief interviews to help investigate their internal feelings. This stage is to sort out the relationship between the relevant stakeholders in the service process and help summarize the user's in-depth interaction needs in terms of user experience.
Fuzzy Multicontext Systems

Multicontext systems provide an effective representation and reasoning framework for integrating heterogeneous knowledge obtained from different sources and have been applied in different fields. Because many application fields in real life have to deal with uncertain and fuzzy knowledge, this article aims to combine the multicontext system and fuzzy logic theory effectively and systematically to deal with the representation and reasoning of uncertainty in heterogeneous contexts. The current research in this area is still relatively limited, especially in terms of systematic integration. Specifically, this article proposes a class of heterogeneous nonmonotonic fuzzy multicontext systems based on nonmonotonic multicontext systems, in which an abstract logic is proposed to capture different types of logic and is used as a theoretical basis for fuzzy multicontext knowledge representation and setting up bridging rules to integrate heterogeneous knowledge. Fuzzy equilibria are used to describe the semantics of fuzzy multicontext systems. The syntactic and semantic framework of heterogeneous nonmonotonic fuzzy multicontext systems is then systematically established. Finally, we show that the proposed fuzzy multicontext system not only extends the nonmonotonic multicontext system to fuzzy settings, but also could expand the probabilistic multicontext system and the possibilistic multicontext system in the similar way.
Automatic Detection of Leukemia from Blood Cells Using Soft Computing Methods

Hematologists have benefited greatly from medical image processing techniques for the diagnosis and investigation of various illnesses such as leukemia. Soft computing algorithms can be used to effectively treat chronic granulocyte leukemia. Proliferation of white blood cells results in leukemia, a lethal disease with an unknown cause that generates fear of death. Leukemia can be detected in white blood cells. However, these diseases can be minimized and controlled by the early diagnosis and treatment. In this paper, we have discussed about clustering and histogram based techniques to detect leukemia blast cells and evaluated their performance based on error rate and computation time.
One-Shot Neural Architecture Search

In the previous chapter, we explored Multi-trial Neural Architecture Search, which is a very promising approach. And the reader might wonder why Multi-trial NAS is called that way. Are there any other non-Multi-trial NAS approaches, and is it really possible to search for the optimal neural network architecture in some other way without trying it? It looks pretty natural that the only way to find the optimal solution is to try different elements in the search space. In fact, it turns out that this is not entirely true. There is an approach that allows you to find the best architecture by training some Supernet. And this approach is called One-shot Neural Architecture Search. As the name "one-shot" implies, this approach involves only one try or shot. Of course, this "shot" is much longer than single neural network training, but nevertheless, it saves a lot of time. In this chapter, we will study what One-shot NAS is and how to design architectures for this approach. We will examine two popular One-shot algorithms: Efficient Neural Architecture Search via Parameter Sharing (ENAS)Efficient neural architecture search via parameter sharing (ENAS) and Differentiable Architecture Search (DARTS)Differentiable architecture search (DARTS). Of course, we will apply these algorithms to solve practical problems.