Reviewerly/exHarmony huggingface dataset:
Towards Embedded Computation with Building Materials

We present results showing the capability of concrete-based information processing substrate in the signal classification task in accordance with in materio computing paradigm. As the Reservoir Computing is a suitable model for describing embedded in materio computation, we propose that this type of presented basic construction unit can be used as a source for “reservoir of states” necessary for simple tuning of the readout layer. We present an electrical characterization of the set of samples with different additive concentrations followed by a dynamical analysis of selected specimens showing fingerprints of memfractive properties. As part of dynamic analysis, several fractal dimensions and entropy parameters for the output signal were analyzed to explore the richness of the reservoir configuration space. In addition, to investigate the chaotic nature and self-affinity of the signal, Lyapunov exponents and Detrended Fluctuation Analysis exponents were calculated. Moreover, on the basis of obtained parameters, classification of the signal waveform shapes can be performed in scenarios explicitly tuned for a given device terminal.
Gravity-enhanced quantum spatial target detection

Quantum illumination can utilize entangled light to detect the low-reflectivity target that is hidden in a bright thermal background. This technique is applied to the detection of an object in the curved spacetime of the Earth, in order to explore how the curvature of spacetime affects quantum illumination. It is found that the spatial quantum illumination with entangled state transmitter outperforms that with coherent-state transmitter in the near-Earth curved spacetime. Moreover, either the quantum illumination system or the coherent-state system is employed, and gravity can enhance the spacetime target detection by reducing the thermal signal at the receiver. Besides, our model in principle can be applied to microwave quantum illumination and thus provides, to some degree, a theoretical foundation for the upcoming spatial quantum radar technologies.
An approach to remove duplication records in healthcare dataset based on Mimic Deep Neural Network (MDNN) and Chaotic Whale Optimization (CWO)

Duplication of data in an application will become an expensive factor. These replication of data need to be checked and if it is needed it has to be removed from the dataset as it occupies huge volume of data in the storage space. The cloud is the main source of data storage and all organizations are already started to move their dataset into the cloud since it is cost effective, storage space, data security and data Privacy. In the healthcare sector, storing the duplicated records leads to wrong prediction. Also uploading same files by many users, data storage demand will be occurred. To address those issues, this paper proposes an Optimal Removal of Deduplication (ORD) in heart disease data using hybrid trust based neural network algorithm. In ORD scheme, the Chaotic Whale Optimization (CWO) algorithm is used for trust computation of data using multiple decision metrics. The computed trust values and the nature of the data’s are sequentially applied to the training process by the Mimic Deep Neural Network (MDNN). It classify the data is a duplicate or not. Hence the duplicates files are identified and they were removed from the data storage. Finally, the simulation evaluates to examine the proposed MDNN based model and simulation results show the effectiveness of ORD scheme in terms of data duplication removal. From the simulation result it is found that the model’s accuracy, sensitivity and specificity was good.
Face Recognition by Using 2D Orthogonal Subspace Projections

In this paper, the implementations and comparison of some classifiers along with 2D subspace projection approaches have been carried out for the face recognition problem. For this purpose, the well-known classifiers such as K-Nearest Neighbor (K-NN), Common Matrix Approach (CMA), Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are conducted on low dimensional face representations that are determined from 2DPCA-, 2DSVD- and 2DFDA approaches. CMA, which is a 2D version of the Common Vector Approach (CVA), finds a common matrix for each face class. From the experimental results, we have observed that the SVM presents a dominant performance in general. When overall results of all datasets are considered, CMA is slightly superior to others in case of 2DPCA- and 2DSVD-based features matrices of the AR dataset. On the other side, CNN is better than other classifiers when it comes to develop a face recognition system based on original face samples and 2DPCA-based feature matrices of the Yale dataset. The experimental results indicate that use of these feature matrices with CMA, SVM, and CNN in classification problems is more advantageous than the use of original pixel matrices in the sense of both processing time and memory requirement.
Web Application Performance Analysis of ECommerce Sites in Bangladesh: An Empirical Study

The users of e-commerce sites are growing rapidly day by day for easy internet access where the performance of web applications plays a key role to satisfy the end-users.The performance of these websites or web applications depends on several parameters such as load time, fully loaded (time), fully loaded (requests), etc.This research tries to investigate and find out the parameters that affects the web performance and it has been tested on ecommerce applications of Bangladesh, where eleven parameters are considered and these are fully loaded (requests), first CPU idle, speed index, start render, load time, fully loaded (time), document complete (time), last painted hero, first contentful paint, and first byte.According to the analysis some applications need to take care of or the developers need to re-modify it.As per the investigation of scanned information, the applications fall under three classes.To start with, the applications do not demonstrate acceptable records to be investigated.The second and third classification applications required medium and high reaction times at the user end separately.Also, the fully loaded (requests)' and document complete (requests) show the most noteworthy required time at the user end, where maximum values are 347 and 344 seconds individually.
FedNLP: A Research Platform for Federated Learning in Natural Language Processing.

Increasing concerns and regulations about data privacy, necessitate the study of privacy-preserving methods for natural language processing (NLP) applications. Federated learning (FL) provides promising methods for a large number of clients (i.e., personal devices or organizations) to collaboratively learn a shared global model to benefit all clients, while allowing users to keep their data locally. To facilitate FL research in NLP, we present the FedNLP, a research platform for federated learning in NLP. FedNLP supports various popular task formulations in NLP such as text classification, sequence tagging, question answering, seq2seq generation, and language modeling. We also implement an interface between Transformer language models (e.g., BERT) and FL methods (e.g., FedAvg, FedOpt, etc.) for distributed training. The evaluation protocol of this interface supports a comprehensive collection of non-IID partitioning strategies. Our preliminary experiments with FedNLP reveal that there exists a large performance gap between learning on decentralized and centralized datasets -- opening intriguing and exciting future research directions aimed at developing FL methods suited to NLP tasks.
Nonlinear Functional Modeling Using Neural Networks

We introduce a new class of nonlinear models for functional data based on neural networks. Deep learning has been very successful in nonlinear modeling, but there has been little work done in the functional data setting. We propose two variations of our framework: a functional neural network with continuous hidden layers, called the Functional Direct Neural Network (FDNN), and a second version that uses basis expansions and continuous hidden layers, called the Functional Basis Neural Network (FBNN). Both are designed explicitly to exploit the structure inherent in functional data. To fit these models we derive a functional gradient based optimization algorithm. The effectiveness of the proposed methods in handling complex functional models is demonstrated by comprehensive simulation studies and real data examples. Supplementary materials for this article are available online.
Stateless neural meta-learning using second-order gradients

Abstract Meta-learning can be used to learn a good prior that facilitates quick learning; two popular approaches are MAML and the meta-learner LSTM. These two methods represent important and different approaches in meta-learning. In this work, we study the two and formally show that the meta-learner LSTM subsumes MAML, although MAML, which is in this sense less general, outperforms the other. We suggest the reason for this surprising performance gap is related to second-order gradients. We construct a new algorithm (named TURTLE) to gain more insight into the importance of second-order gradients. TURTLE is simpler than the meta-learner LSTM yet more expressive than MAML and outperforms both techniques at few-shot sine wave regression and 50% of the tested image classification settings (without any additional hyperparameter tuning) and is competitive otherwise, at a computational cost that is comparable to second-order MAML. We find that second-order gradients also significantly increase the accuracy of the meta-learner LSTM. When MAML was introduced, one of its remarkable features was the use of second-order gradients. Subsequent work focused on cheaper first-order approximations. On the basis of our findings, we argue for more attention for second-order gradients.
An Anomaly Detection Method of Industrial Data Based on Stacking Integration

With the development of Internet technology, the computing power of data has increased, and the development of machine learning has become faster and faster. In the industrial production of industrial control systems, qua... | Find, read and cite all the research you need on Tech Science Press
EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings.

Recent medical prognostic models adapted from high data-resource fields like language processing have quickly grown in complexity and size. However, since medical data typically constitute low data-resource settings, performances on tasks like clinical prediction did not improve expectedly. Instead of following this trend of using complex neural models in combination with small, pre-selected feature sets, we propose EffiCare, which focuses on minimizing hospital resource requirements for assistive clinical prediction models. First, by embedding medical events, we eliminate manual domain feature-engineering and increase the amount oflearning data. Second, we use small, but data-efficient models, that compute faster and are easier to interpret. We evaluate our approach on four clinical prediction tasks and achieve substantial performance improvements over highly resource-demanding state-of-the-art methods. Finally, to evaluate our model beyond score improvements, we apply explainability and interpretability methods to analyze the decisions of our model and whether it uses data sources and parameters efficiently.1.
Mobile-Learning Platform for the Development of Entrepreneurial Competences of the Students

The aim of the study was to determine the effectiveness of using a mobile learning platform for students’ entrepreneurial competence improvement. The platform was implemented by the Russian State Vocational Pedagogical University and the Financial University under the Government of the Russian Federation. It allowed users to publish assignments, exchange ideas, generate photo and video content, demonstrate creative developments, and receive feedback. The research process enrolled 70 experimental group students, 25 control group students, 2 instructors, and 2 administrators from the considered educational institutions, as well as 3 practitioners invited to review projects. As a result of this examination (Cronbach’s alpha ranged from 0.678 to 0.714), it was noted that 41.4% of all research participants rather agreed that the experiment goals were accomplished and noted personal progress in mastering entrepreneurial competences, whereas 53.4% agreed unequivocally that the experiment was fully successful. Analysis of entrepreneurial competences and deviations from repeated competence evaluations before and after the experiment revealed that the experimental group was marked with notable progress (F (1.32) = 42.26, p = 0.00, = 0.56). Average entrepreneurial competence assessment results for this group were slightly higher than those for control – they constituted 4.5 points against 4.0. Research findings are believed to be universal and interesting to educators and administrative staff of educational institutions or those involved in creating and conducting entrepreneurship training courses.
Evaluating Model Robustness and Stability to Dataset Shift

As the use of machine learning in high impact domains becomes widespread, the importance of evaluating safety has increased. An important aspect of this is evaluating how robust a model is to changes in setting or population, which typically requires applying the model to multiple, independent datasets. Since the cost of collecting such datasets is often prohibitive, in this paper, we propose a framework for analyzing this type of stability using the available data. We use the original evaluation data to determine distributions under which the algorithm performs poorly, and estimate the algorithm's performance on the worst-case distribution. We consider shifts in user defined conditional distributions, allowing some distributions to shift while keeping other portions of the data distribution fixed. For example, in a healthcare context, this allows us to consider shifts in clinical practice while keeping the patient population fixed. To address the challenges associated with estimation in complex, high-dimensional distributions, we derive a debiased estimator which maintains $\sqrt{N}$-consistency even when machine learning methods with slower convergence rates are used to estimate the nuisance parameters. In experiments on a real medical risk prediction task, we show this estimator can be used to analyze stability and accounts for realistic shifts that could not previously be expressed. The proposed framework allows practitioners to proactively evaluate the safety of their models without requiring additional data collection.
Using Semantic Technology To Model Persona For Adaptable Agents

In state of the art research a growing interest in the application of agent models for the simulation of road traffic can be observed. Software agents are particularly suitable for the representation of travellers and their goal-oriented behaviour. Although numerous applications based on these types of models are already available, the options for modelling and calibration of the agents as goal-oriented individuals are either simplified to aggregated parameters or associated with overly complex and opaque implementation details. This makes it difficult to reuse available simulation models. In this paper, we demonstrate how the combination of persona models together with semantic methods can be applied to achieve a well-structured agent model that allows for improved reusability.
Athletes’ Freedom of Expression: The Relative Political Neutrality of Sport

Abstract Athletes are not just sports people; they are certainly among the most prominent figures of their present time, playing an important role in shaping opinions with their power to inspire. For this very reason, athletes’ freedom of expression is strongly limited by the sport authorities in light of the fundamental principle of sport neutrality. This study analyses and questions the traditional constraints to athletes’ free speech by taking into consideration the role of human rights in sports legal order and in sporting affairs. By assuming an emerging relativization of sport political neutrality, the essay investigates the case-law concerning athletes’ freedom of expression identifying limits and perspectives of the current evolutions on athletes’ public statements, establishing to what extent a reform of the present sporting regulation on freedom of expression is needed.
Improved gravitational search algorithm based on chaotic local search

The traditional gravitational search algorithm (GSA) maintains good diversity of solutions but often demonstrates weak local search ability. To promote the local search ability of GSA, a new GSA based on chaotic local search (CLSGSA) is introduced in this paper. In its search operations, CLSGSA first executes the conventional search operations of the basic GSA to maintain the diversity of solutions. After that, CLSGSA executes a chaotic local search with the search experience from the current best solution to increase the local search capability. In the experiments, we utilise a suite of benchmark functions to verify the performance of CLSGSA. Moreover, we compare the proposed CLSGSA with several GSA variants. The comparisons validate the effectiveness of CLSGSA.
Parallel algorithms for the creation of medical database

Abstract Analysis of parallel algorithms for graphics processors allows you to determine the bottlenecks of the algorithm that affect its performance on a particular computing system. Algorithms can be analyzed both at the AGM level and at the level of the CPU-GPU system. Despite the fact that the model does not reflect the overlap of the execution of computational operations and operations of access to the global memory, but estimates the upper limit of the execution time of the algorithm on the AGM, in any parallel algorithms for the AGM it is necessary to reduce this parameter of the algorithm due to the large number of clock cycles spent by the multiprocessor to implement global memory access operations.
Blockchain, Fog and IoT Integrated Framework: Review, Architecture and Evaluation

In the next-generation computing, the role of cloud, internet, and smart devices will be capacious. Nowadays we all are familiar with the word smart. This word is used a number of times in our daily life. The Internet of Things (IoT) will produce remarkable different kinds of information from different resources. It can store and process big data in the cloud. The fogging acts as an interface between cloud and IoT. The IoT nodes are also known as fog nodes, these nodes are able to access anywhere within the range of the network. The blockchain is a novel approach to record the transactions in a sequence securely. Developing new blockchains based integrated framework in the architecture of the IoT is one of the emerging approaches to solving the issue of communication security among the IoT public nodes. This research explores a novel approach to integrate blockchain technology with the fog and IoT networks and provides communication security to the internet of smart devices. The framework is tested and implemented in the IoT network. The results are found positive.
The Effect of Digital Activities on the Technology Awareness and Computational Thinking Skills of Gifted Students (eTwinning Project Example)

The aim of this study was to examine the effect of interdisciplinary activities organised online within the scope of an eTwinning project carried out with gifted students on the students’ technology awareness and computational thinking (CT). The study was carried out through web-based tools for a period of 3 months in the year 2020. A single-group pretest-posttest design, one of the pre-experimental designs, was used in the study. The participants were 50 gifted students continuing their education at Science and Art Centres affiliated to the National Education Ministry in 6 different provinces of Anatolia. Quantitative and qualitative methods were used together in the study. A technology awareness and computational thinking scale was chosen as the quantitative data collection tool, while mind maps were used as the qualitative data collection tool. As a result of the research, a significant increase in the participants’ technology awareness and computational thinking was determined in favour of the posttest, while this increase was verified through the mind-mapping technique applied to the students. At the end of the study, the findings were discussed, and recommendations were made for future studies.
Responsive and Personalized Web Layouts with Integer Programming

Over the past decade, responsive web design (RWD) has become the de facto standard for adapting web pages to a wide range of devices used for browsing. While RWD has improved the usability of web pages, it is not without drawbacks and limitations: designers and developers must manually design the web layouts for multiple screen sizes and implement associated adaptation rules, and its "one responsive design fits all" approach lacks support for personalization. This paper presents a novel approach for automated generation of responsive and personalized web layouts. Given an existing web page design and preferences related to design objectives, our integer programming -based optimizer generates a consistent set of web designs. Where relevant data is available, these can be further automatically personalized for the user and browsing device. The paper includes presentation of techniques for runtime adaptation of the designs generated into a fully responsive grid layout for web browsing. Results from our ratings-based online studies with end users (N = 86) and designers (N = 64) show that the proposed approach can automatically create high-quality responsive web layouts for a variety of real-world websites.
Multilingual Sentiment Analysis and Toxicity Detection for Text Messages in Russian

In this paper, we discuss an approach to sentiment analysis and emotion identification for user comments. The solution is threefold: 1) topic detection, 2) sentiment evaluation, 3) toxicity detection and toxic spans localization. The lack of significantly large training data for the Russian language is handled by utilizing multilingual word embeddings, the adversarial domain adaptation model, and data augmentation. We present an overview of various preprocessing pipelines for topic modeling and highlight the LDA- Mallet model which demonstrates the best performance. For sentiment analysis and toxicity detection, we examine the efficacy of a support vector machine and a deep neural network with a multilingual language model and adversarial domain adaptation that allows us to train algorithms with datasets in the English language. All methods are tested with a dataset of user comments to various online-courses and adjusted to provide support for the development of a virtual dialogue assistant for conducting virtual exams.
Dr. Proctor: A Multi-modal AI-Based Platform for Remote Proctoring in Education

Technological advancements have enabled remote exams as a viable alternative to in-person proctoring. In light of the COVID-19 pandemic, educational institutions relied heavily on remote operation. The sudden shift exposed the weaknesses in available proctoring solutions, as pertains to fairness, economic viability, data privacy, network issues and usability. Moreover, whether they are equal in function to physical proctoring is questionable. Based on extensive research, we establish the system requirements and design for Dr. Proctor, a non-commercial solution that addresses many of the exposed concerns about remote proctoring.