Reviewerly/exHarmony huggingface dataset:
Quantum Information Theory in Infinite Dimensions with Application to Optical Channels

Abstract Information theory deals with the efficient representation of information sources as well as providing fundamental limits to the amount of information communicated reliably over channels. These sources and channels are generally classical, i.e., represented by standard probability distributions. Quantum information theory takes it to the next level where we allow for the sources as well as channels to be quantum. From the representation of quantum states to the communication over quantum channels, the theory not only essentially encapsulates classical information theoretic methods but also accounts for quantum effects such as superposition, entanglement, interference, etc. In this article, we will review and focus on the information theoretic analysis of quantum channels with infinite dimensions. Infinite dimensionality is needed to model quantum optical channels which are ubiquitous in today’s practical networks, distributed quantum communication and quantum internet. The infinite dimensionality introduces some unique problems when compared with finite-dimensional channels and has not been deeply explored in literature from the quantum information theoretic perspective. For these channels, we provide the essential concepts and state-of-the-art channel capacity results. To make this paper self-contained, we also recall the finite dimensional results.
Topikaliserade rums- och tidsadverbial i fornsvenskt lagspråk – texttypiska och informationsstrukturella drag

Topicalization is a common means of bringing about syntactic variation in Old Swedish. In topicalization, a sentence element for which the usual place is in the middle or final field is instead placed in the initial field. In Early Old Swedish and up to the mid-15th century, adverbials were more common than subjects in sentence-initial position. This study analyzes factors connected to genre and information structure that contribute to topicalization of adverbials in Early Old Swedish legal language. These factors are analyzed in relation to the structure of the text.The data consist of the Uppland law (UL) and are restricted to topicalized spatial and temporal adverbs (n=77). Three research questions are posed: 1) what features connected to genre contribute to topicalization in UL; 2) what functions topicalized spatial and temporal adverbials serve in information structure; and 3) whether there is a relationship between genre features and information structure in Early Old Swedish legal language.Topicalized spatial and temporal adverbials are significantly more common in main clauses (n=57) than in subordinate clauses (n=20) in UL. Clauses with a topicalized spatial or temporal adverb usually belong to legal declarations; topicalization occurs in both first and second clauses. Two central genre-related motivations for topicalization are textual binding and framing, which means that spatial and temporal adverbs can be used to refer backward and forward in the text. Factors connected with genre also include emphasis and contrast, which in old laws is not just a stylistic technique, but has a further function in shaping the text. The information structure functions of the adverbs studied are background and rheme, with the background function being the most common. The function of theme does not occur in the data. The analysis shows clearly that the genre features are connected with the information structure functions; together they contribute to topicalization in Old Swedish legal language.
EPLL image restoration with a bounded asymmetrical Student’s-t mixture model

The expected patch log-likelihood (EPLL) model is a patch prior-based image restoration method which received extensive attention in image processing in recent years for its outstanding ability to preserve the detail and structure. However, due to using the Gaussian mixture model (GMM) with the noise sensitivity as the local prior, the EPLL model suffers from undesired artifact and poor robustness frequently. In this paper, to restrain the generation of artifact of EPLL model, we replace the GMM with a bounded asymmetrical Student’s-t mixture model (BASMM), which is sufficiently flexible to fit different shapes of image data, such as non-Gaussian, non-symmetric, and bounded support data. Then, the anisotropic nonlocal self-similarity (ANSS) based regularization parameters are designed to improve the robustness of the proposed model. Experimental results demonstrate the competitiveness of our proposed model compared with that of state-of-the-art methods in performance both visually and quantitatively.
The Impact of Sustained Experiential Learning on Beginning Teachers' Process of Becoming: A Narrative Approach

of becoming: a narrative approach
The Impact of Conditional Dependency and Its Detection on Cognitive Diagnosis Modeling

Estimating cognitive diagnosis models (CDMs) typically assumes conditional independence.This study investigates how violating this assumption affects the validity of inferences obtained from CDMs, and how it can detected.Results indicate that conditional dependency yields biased estimates and understated standard errors, resulting in higher classification accuracy and item discrimination indices.' ' ' '
Development Technologies for Website Implementation

The most used web development technologies and tools are presented as a guide for those interested in building web portals. The document is presented as a reference document with some references that may be useful.
Solar Tracking System for Efficient Power Generation using Image Processing

Abstract: The emerging era is energy demanding and there is a drastic need to shift to more eco-friendly methods for power generation. The non-renewable resources generate 86% of the world's power. Around the world, coal and gas power plants are shutting down due to lack of fuel. With more and more industries and governments joining the Green Movement, the demand for cost effective renewable energy has surged. Solar power can be easily harnessed as compared to other eco-friendly methods, using solar panels and the conventional process of converting solar energy to electrical energy. Even though solar power is used in domestic as well as industrial applications, it is still a long way from making an impact on environmental change. Since solar panels can only be utilized up to their full potential in clear sky and sunny conditions, its usage is affected by geographic location. However, even in conditions of optimal usage, it's not used up to its full potential. As all solar panels are fixed due sunny side, they receive direct sunlight only for half a day. During the other half, the sun is behind the panels and hence, the power generation drops steadily. To rectify this, we introduce the principle used by sunflowers. Sunflowers intake energy by following the sun from sunrise till sunset. Using the same concept, we make the panels follow the sun resulting in significant increase in energy production. We take images of the sun throughout the day, using image processing make sure that the sun is directly incident to the panels, follow this procedure till sunset. This method makes sure that even in conditions of slight to moderate overcast weather, the sun will be directly incident on panels, such is the power of image processing. The status of the device as well as energy generated can be easily visualized using the mobile application provided. The system can further be made cost effective by modifying the existing panels. The concept is simple yet very efficient, robust and scalable. This method can be used for domestic as well as industrial applications. In optimal conditions, this method can generate up to 40% more energy than a conventional solar panel system of the same size. In conclusion, just by learning from nature, using only a gearbox and controller in addition to panels, we can generate significantly more electricity. In addition to this method, using most efficient solar panels that give maximum power output even in overcast conditions can further increase power output.
Web-based human activity recognition using images and descriptive web pages

Loss of autonomy is a problem that researchers have been trying to solve for the last two decades thanks to technologies that facilitate the prolongation of home care. In order to ensure the well being and security of residents in loss of autonomy, it is necessary to know the needs of the latter. The human activity recognition is a way to know these needs by tracking the actions of residents using recognition algorithms and sensors placed in the home or on the individual. The most efficient human activity recognition algorithms are mainly based on supervised learning, which itself relies on the quality of the learning data collected in the residences. However, data collection is a time-consuming, costly and complex process, especially when we want to create a representative dataset that guarantees the performance and generalization capacity of the model that uses it. In order to provide an alternative to this data collection, this work presents an approach to human activity recognition based on data from the web. We show that exploiting images and text retrieved from the web with the right algorithm allows to obtain good recognition performances with an average accuracy of 0.91 for 5 activities of daily living.
Interactive Restoration of Implicitly Defined Shapes

Shape representation using implicit functions is one of the popular geometric modeling methods and used for variety of important applications in the area of computer graphics and simulations, such as surface reconstruction from unorganized point sets, shape modeling based on constructive solid geometry and fluid simulation based on particles. Although many different generation and rendering approaches have been proposed, the generated surfaces are likely to have irregular components such as isolations of long-narrow parts, unexpectedly attached spikes and noise-like ripples on smooth curves. Such problems are caused by a variety of reasons, including the limitations of surface generation algorithms, lack of robustness of scanning machine that cause defects over the input point set and the complexity of the desired shape to generate. It is necessary to propose algorithms that are able to give restorations to the irregular represented shapes as post-processing. In this paper, three methods have been proposed to apply user-interactive restorations of 2D implicitly represented shapes with irregular components, where users are expected to specify locations of irregular part as people often have good understanding of the shape that they create. Experiments have shown that the proposed methods are competent to make fast and effective restorations and corrections of irregular components on 2D implicitly represented shapes.
State and Covariance Matrix Propagation for Continuous-Discrete Extended Kalman Filter Using Modified Chebyshev Picard Iteration Method

Imran, A. Wang, X. Yue, X.In this paper, we propose a new method for the extended Kalman Filter state estimation for nonlinear systems with no closed-form solutions, given noisy state measurements are available with known uncertainties. The system is defined by a couple of sets of equations called the “moment equations.” In the CD-EKF discrete, noisy state estimations are available at known time stamps. Propagation of the state estimation requires the integration of the moment equations that can diverge if the underlying system is stiff. We are employing the MCPI method at this stage, thus significantly improving the propagation accuracy compared to traditional methods. The proposed CD-EKF is applied to two problems (1) the famous Duffing Oscillator, a known stiff system, (2) to the Xu-Wang equations of relative orbital propagation, which define the relative motion of two satellites under the J2 perturbation of Earth.
Research on Fast Face Recognition Method Based on Double Decision Subspace

Because a large number of labeled face data samples in special scenes need a large number of training samples with identity markers, and it is impossible to accurately extract the characteristics of small samples, a fast face recognition method based on double decision subspace is proposed. A feature recognition structure based on double decision subspace is constructed to preprocess the face image and separate the local features of several corresponding face images. The local binary pattern is used to extract the local texture features of the face, and the deep convolution network face fast recognition model is constructed. The convolution network is used to share the weight, pool, and downsampling to reduce the complexity of the model. The constructed recognition model is used to recognize the features of the face image, and the fast face recognition is effectively completed. The experimental results show that the designed method has high recognition accuracy, less average recognition time, and good recognition performance.
How Much Does Lookahead Matter for Disambiguation? Partial Arabic Diacritization Case Study

Abstract We suggest a model for partial diacritization of deep orthographies. We focus on Arabic, where the optional indication of selected vowels by means of diacritics can resolve ambiguity and improve readability. Our partial diacritizer restores short vowels only when they contribute to the ease of understandability during reading a given running text. The idea is to identify those uncertainties of absent vowels that require the reader to look ahead to disambiguate. To achieve this, two independent neural networks are used for predicting diacritics, one that takes the entire sentence as input and another that considers only the text that has been read thus far. Partial diacritization is then determined by retaining precisely those vowels on which the two networks disagree, preferring the reading based on consideration of the whole sentence over the more naïve reading-order diacritization. For evaluation, we prepared a new dataset of Arabic texts with both full and partial vowelization. In addition to facilitating readability, we find that our partial diacritizer improves translation quality compared either to their total absence or to random selection. Lastly, we study the benefit of knowing the text that follows the word in focus toward the restoration of short vowels during reading, and we measure the degree to which lookahead contributes to resolving ambiguities encountered while reading. L’Herbelot had asserted, that the most ancient Korans, written in the Cufic character, had no vowel points; and that these were first invented by Jahia–ben Jamer, who died in the 127th year of the Hegira. “Toderini’s History of Turkish Literature,” Analytical Review (1789)
Artificial Intelligence of Internet of Things Based on Machine Learning and College Student Management

Under the background of the development of higher education, according to the characteristics of college student management, after analyzing its background and practical significance, this study constructs an intelligent college student management system of Internet of things based on machine learning. The data volume of the Internet of things is huge, so ensuring the normal and efficient operation of the system is the primary goal. In this study, the data management model of the system is constructed with the help of cyclic neural network in machine learning algorithm to predict the data and optimize the computer program. At the same time, the system data are filled and classified by the k-nearest neighbor model, and the data are trained and simulated by constructing a safe bilstm neural network system. Because the information related to students in the university database involves personal privacy, in order to ensure the security of the system and avoid relevant data leakage, in the judgment standard of configuration error data flow, this study calculates the monitoring abnormal data and loss function through the dark network flow and ip2vec algorithm, so as to establish the system abnormal monitoring model and identify the system error data flow. Finally, it constructs the college student management system and expounds on the basic requirements of the system use cases. After a series of tests of system performance, capacity, and stability, the results meet the basic requirements of system operation, which provides a certain reference for the application of college student management in the future.
Bridging the Reality Gap via Progressive Bayesian Optimisation

The recent rapid development of deep-learning-based control strategies has made the reality gap a critical issue at the forefront of robotics, especially for legged robots. We propose a novel system identification framework, Progressive Bayesian Optimisation (ProBO), to bridge the reality gap by tuning simulation parameters. Since dynamic locomotion trajectories are usually harder to narrow the reality gap than their static counterpart, we train a Gaussian process model with the easier trajectory data set and make it a prior to start the learning process of a harder one. We implement ProBO on a quadruped robot to narrow the reality gaps of a set of bounding gaits at different speeds. Results show that our methods can outperform all other alternatives after training the initial gait.
Robotic Deployment of Stabilized Shearography Unit for Wind Turbine Blade Inspection

A wind turbine blade inspection system called Winspector has been developed by a European consortium to automate the in-situ non-destructive testing of wind blades. A robot platform is winched up a wind turbine tower to reach a blade locked into a 90° pitch angle. The blade region to be inspected is reached by a combination of an extension ladder and a 5-axis robot arm to place an end-effector onto the blade surface. The end-effector is critical to the success of the shearography technique used to detect subsurface defects when the blade is experiencing inevitable out-of-plane, in-plane, and root-to-tip vibrations. The paper describes the development of the end-effector which carries the shearography unit and ensures (with passive compliance and design based on analysis of aerodynamic blade shapes) that the unit remains at a constant distance from blade surface in the presence of blade vibrations. The system has been tested with three separate field trials in a wind farm near Athens in Greece. Sub-surface defect detection with shearography has been successfully demonstrated.
Healthy City for Organizing Effective and Multifaceted Actions at the Urban Level

The presented paper consists of two main aims: the first one is to introduce the concept of Healthy City (HC), while the second one proposes the organization of a public action striving for the healthiness of cities. For this purpose, authors exploit research based on the experience approach concerning a specific topic related to HC (urban mobility) and the administrative procedure from developing a policy to its implementation phase. HC represents a highly complex system, and the practical actions emerge as a synthesis of different layers composing the public administration (at least considering the local scale). Generally speaking, managing the entire healthy process is fundamental to avoid losing the multifaceted attributes of objects, subjects, roles, and objectives. The grounded theory approach highlights the elements that compose the complex system substantially and clearly; moreover, it permits to keep control of the process while the singular steps are defined. Initially, the authors describe the HC idea and its main elements; then, they introduce a precise topic and organize a public action using the experiential approach.
Using Gaussian mixture model clustering to explore morphology and standardized production of ceramic vessels: A case study of pottery from Late Bronze Age Greece

This paper examines vessel morphology and degrees of standardization of ceramic vessels using Gaussian mixture model cluster analysis (GMMC). GMMC is an unsupervised data mining technique that identifies natural groups in a dataset. This project first tests whether GMMC can classify certain shape categories correctly according to human assigned Furumark Shape (FS) pot types using basic vessel dimensions. We then propose that vessels can be considered standardized if they were grouped into the cluster of their own shape category (or pot type). Vessel data are derived from the Late Bronze Age Petsas House ceramic workshop, located in the settlement of Mycenae in southern Greece. The sample comes from a sealed well deposit found within the workshop. GMMC identified three clusters within a group of 488 pots that correspond to three known vessel types with a high degree of sensitivity and specificity so that clustered shapes mostly align with shape categories, and each shape can be defined as standardized. The maximization of information enabled by GMMC and the ability to analyze multiple interrelated variables can thus indicate cognitive approaches to vessel production, such as the perception of vessel shape by potters, and socio-economic factors relating to use by consumers.
Routine analysis of community-dwelling older adults and anomaly detection by using room level location

In recent years there has been a considerable interest in monitoring people, especially the elderly, by using different technologies [1], with the aim of improving their quality of life and their independence. In addition, the growing of elderly population [2] implies an increase in medical costs, as they are the age group that requires the highest use of public health services. The detection of changes in the elderly people routines can be an indicator of the onset of physical or cognitive impairment. The early detection of that decline can alert to implement measures to alleviate the deterioration, and as a consequence, this can lead to both improve independent living and reduce health care costs. This work is focused on the analysis of routines of elderly people living in care homes. They are quite routine people since the ordinary activities they perform are supervised by carers. Currently, several volunteers are being monitored to obtain their symbolic location based on BLE (Bluetooth Low Energy) technology [3], and through this location the objective is to derive their routines. BLE beacons have been installed in each room of interest within the nursing home, and volunteers wear a smartwatch that stores the MAC (Media Access Control) address and RSSI (Received Signal Strength Indicator) of each beacon it receives. However, due to the lack of data at this stage of the project, we proceed to generate synthetic routines based on real data and carers information, to evaluate the proposed algorithms.
Electroglottography based voice-to-MIDI real time converter with AI voice act classification

Voice-to-MIDI real-time conversion is a challenging task that presents a series of obstacles and complications. The main issue is the tracking of the pitch. The frequency tracking of human voice can be inaccurate and computationally expensive due to spectral complexity of voice sounds. Moreover, with microphone-based systems, the presence of environmental noise and neighbouring sounds further affect the accuracy of the frequency tracking. Another issue with the conversion of voice into MIDI, is the presence of non-singing phonemes. As every sound picked up by the microphone would go through the conversion system, any voice or sounded phonemes produced by the user will result in a MIDI output. This research addresses such issues by applying a novel experimental method which employs electroglottography, known to the medical community as EGG, as a source for the pitch tracking operation. Electroglottography improves both the accuracy of the tracking and the ease of processing as it delivers a direct evaluation of the vocal folds operation whilst bypassing any contamination from other sound sources. Furthermore, to address the issue of non-singing phonemes, the proposed method employs the use of neural networks for a real-time classification of the vocal act produced by the user.