Reviewerly/exHarmony huggingface dataset:
White Blood Cells Segmentation and Classification Using Swarm Optimization Algorithms and Multilayer Perceptron

This study proposes a segmentation and classification system for early detection of blood disease; the proposed system consists of three phases. The first phase is segmenting white blood cells using multi-level thresholding optimized by the butterfly optimization algorithm to select the optimal threshold value to increase the accuracy. The second phase is extracting geometric and shape features of the segmented cells. The third phase is using the gray wolf optimizer to adopt the weights and biases of the multilayer perceptron to enhance the accuracy of classification between normal and leukemia cells, classify the normal cells to their five categories, and classify the leukemia to their four categories. The proposed system applies to different data sets (ALL-IDB2, LISC, and ASH-Image bank) and overcomes the segmentation and classification problems of microscopic images and shows an outstanding segmentation result, 98.02%; and the average classification accuracy between normal and leukemia cells is 98.58%, between white blood cell categories is 98.9%, and between leukemia types is 98.93%.
Energy Use in Quantum Data Centers: Scaling the Impact of Computer Architecture, Qubit Performance, Size, and Thermal Parameters

As quantum computers increase in size, the total energy used by a quantum data center, including the cooling, will become a greater concern. The cooling requirements of quantum computers, which operate at temperatures near absolute zero, are determined by computing system parameters, including the number and type of physical qubits, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. When combined with thermal system parameters such as cooling efficiency and cryostat heat transfer, the total energy use can be determined using a first-principles energy model. These models show that cooling of quantum computers differs in two fundamental ways from conventional data centers: (1) the energy required for cooling is much greater than the energy required for computation, and (2) the cooling loads are sensitive to the computational architecture. The temperature requirements for different qubit types can change energy requirements by orders of magnitude. Power use and computational power, as quantified by quantum volume, are analytically correlated. Approaches are identified for minimizing energy use in integrated quantum systems relative to computational power. Designing a sustainable quantum computer will require both efficient cooling and system design that minimizes cooling requirements.
Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications

Most unsupervised NLP models represent each word with a single point or single region in semantic space, while the existing multi-sense word embeddings cannot represent longer word sequences like phrases or sentences. We propose a novel embedding method for a text sequence (a phrase or a sentence) where each sequence is represented by a distinct set of multi-mode codebook embeddings to capture different semantic facets of its meaning. The codebook embeddings can be viewed as the cluster centers which summarize the distribution of possibly co-occurring words in a pre-trained word embedding space. We introduce an end-to-end trainable neural model that directly predicts the set of cluster centers from the input text sequence during test time. Our experiments show that the per-sentence codebook embeddings significantly improve the performances in unsupervised sentence similarity and extractive summarization benchmarks. In phrase similarity experiments, we discover that the multi-facet embeddings provide an interpretable semantic representation but do not outperform the single-facet baseline.
Control of spectroscopic features of multiphoton transitions in two coupled qubits by driving fields

The quantum level population behavior of two coupled flux qubits depending on the external driving field characteristics is studied. Explicit expressions for the multiphoton transition probabilities at an arbitrary control field amplitude are obtained for the case of small tunnel splitting energies. We describe the controllable features of their formation and thereby the creation or destruction of entanglement on the direct interlevel transition and during the transition through intermediate states by system bias tuning. We found a feature of the qubit population inversion that results in the independence of the resonance positions from the qubit coupling strength. Using the Floquet-Markov equation, we numerically demonstrate that the positions of multiphoton resonances are stable to dissipative processes.
SERVQUAL Model in HEI Monitoring Survey as a Part of Quality Assurance Policy

Significant influence of the quality of educational services in the perception of students is demonstrated by research conducted by scientists at various universities around the world.Such research is the basis of quality assurance policy as part of the strategic management of the university.The quality of educational services is studied by the authors on the basis of the normalized SERVQUAL model.Five partial indices and total index are based on the data collected in eight Ukrainian universities.The high value of the Cronbach"s Alpha coefficient confirmed the internal consistency of the model indicators.Results of ANOVA and comparison of confidence intervals confirmed the differentiating ability of the model: data for different universities differ statistically significantly.The influence of the university"s ranking place on the general index is revealed.
Interdisciplinarity and transdisciplinarity

The production of new scientific knowledge and practical solutions to complex problems require increasing amounts of interdisciplinary collaboration, while requirements for transdisciplinary cooperation have recently likewise become more frequent. In practice, however, they are rarely implemented adequately; what occurs instead is merely multidisci­plinary collaboration. True implementation of inter- and/or transdisciplinary collaboration is often met with certain difficulties and obstacles: problems due to limited disciplinary competence, problems due to protecting knowledge and power, the problem of competence required for inter- and transdisciplinary collaboration, complexity problems, method­ological problems and problems caused by differences in cultural traditions. It is necessary to acquire clear general defi­nitions of the concepts of multidisciplinarity, interdisciplinarity and transdisciplinarity, to define and implement general guidelines for the development of multidisciplinary and transdisciplinary practice and to develop a new general culture of collaboration in research and practice of complex problem-solving.
DIGITALIZATION AND VIRTUALIZATION OF THE LIFEWORLD: ASSESSMENTS AND POSITIONS

The development of digital technologies, the emergence of digital objects, their increasing influence on society, culture and people determine changes in various spheres of social and human life. The concepts of digitalization and virtualization are used to describe these processes. The article is aimed at consideration of the impact of modern technologies on the culture and lifeworld of a person. For this, an analysis of the concepts of digitalization, virtualization and virtualization of the lifeworld is carried out, as well as approaches and assessments of these processes presented in the philosophical literature are proposed. The article uses the works of Russian (Ivanov D. V., Kasavina N. A., Mironov V. V., Leshkevich T. G., Sokuler Z. A., Frolov A. V.) and Western researchers (A. Kirby, Yuk Hui, O. Ollinaho, E. Schmidt, J. Cohen). The methodological basis is the principle of sociocultural determination, which determines the impact of the modern digital era on the lifeworld of a person, as well as the semantic analysis of the concepts. The main findings are as follows. First, digitalization and virtualization are two interconnected processes, while virtualization is driven by digitalization. Secondly, the concept of digitalization means the modern stage of scientific, technical and technological development, the concept of virtualization is endowing all spheres of human life with the features of virtual reality, the concept of virtualization of the lifeworld is the addition and change of the lifeworld of a person with the world of virtual reality. Thirdly, digital objects and technologies determine the emergence of new research areas — digital ontology and digimodernism. Fourthly, digitalization and virtualization of the lifeworld receive both positive (expanding the possibilities for personal development, saving resources, etc.) and negative assessments (replacing the real world with a virtual one, lack of integrity of consciousness, Internet addiction). Opposite assessments problematize the study of these processes from the standpoint of both ontology and the theory of knowledge, and axiology, ethics, philosophy of science and technology.
Towards Natural Language Processing with Figures of Speech in Hindi Poetry

Poems have always been an excellent way of expressing emotions in any language.In particular, Hindi poetry is having versatile popularity among native and non-native speakers all over the world.A typical poem in Hindi is characterized by meter ("Chhand"), emotion ("Rasa"), and figure of speech ("Alankaar").The present research work is the first of its kind in Hindi Natural Language Processing (NLP), which touches on the area of Hindi figure of speech.The authors have created a systematic hierarchical structure of Hindi "Alankaar" types and sub-types and attempted and extended the work to identify a few.A taxonomical list of 58 Hindi figures of speech is presented along with their nearest mapping to English equivalents.On the sidelines, the paper also presents the distinct rules for each type and sub-type needed for the classification task of NLP.The authors achieved 97% efficiency in reporting the first results with an average execution time of 0.002 seconds.
Parallelization of the K-Means++ Clustering Algorithm

K-means++ is the clustering algorithm that is created to improve the process of getting initial clusters in the K-means algorithm. The k-means++ algorithm selects initial k-centroids arbitrarily dependent on a probability that is proportional to each data-point distance to the existing centroids. The most noteworthy problem of this algorithm is when running happens in sequential mode, as this reduces the speed of clustering. In this paper, we develop a new parallel k-means++ algorithm using the graphics processing units (GPU) where the Open Computing Language (OpenCL) platform is used as the programming environment to perform the data assignment phase in parallel while the Streaming SIMD Extension (SSE) technology is used to perform the initialization step to select the initial centroids in parallel on CPU. The focus is on optimizations directly targeted to this architecture to exploit the most of the available computing capabilities. Our objective is to minimize runtime while keeping the quality of the serial implementation. Our outcomes demonstrate that the implementation of targeting hybrid parallel architectures (CPU &amp; GPU) is the most appropriate for large data. We have been able to achieve a 152 times higher throughput than that of the sequential implementation of k-means ++.
Translation Mechanism of Neural Machine Algorithm for Online English Resources

At the level of English resource vocabulary, due to the lack of vocabulary alignment structure, the translation of neural machine translation has the problem of unfaithfulness. This paper proposes a framework that integrates vocabulary alignment structure for neural machine translation at the vocabulary level. Under the proposed framework, the neural machine translation decoder receives external vocabulary alignment information during each step of the decoding process to further alleviate the problem of missing vocabulary alignment structure. Specifically, this article uses the word alignment structure of statistical machine translation as the external vocabulary alignment information and introduces it into the decoding step of neural machine translation. The model is mainly based on neural machine translation, and the statistical machine translation vocabulary alignment structure is integrated on the basis of neural networks and continuous expression of words. In the model decoding stage, the statistical machine translation system provides appropriate vocabulary alignment information based on the decoding information of the neural machine translation and recommends vocabulary based on the vocabulary alignment information to guide the neural machine translation decoder to more accurately estimate its vocabulary in the target language. From the aspects of data processing methods and machine translation technology, experiments are carried out to compare the data processing methods based on language model and sentence similarity and the effectiveness of machine translation models based on fusion principles. Comparative experiment results show that the data processing method based on language model and sentence similarity effectively guarantees data quality and indirectly improves the algorithm performance of machine translation model; the translation effect of neural machine translation model integrated with statistical machine translation vocabulary alignment structure is compared with other models.
Harmony Search Algorithm for Structural Engineering Problems

Nowadays, several new metaheuristic methods have been developed by imitating a process in life. These algorithms may be practically effective on optimization of engineering problem, and these algorithms may disappear in time. Despite that, classical and frequently proved algorithm by the success of optimization will live forever, and these algorithms are modified up to date to provide the needs of the future. The harmony search (HS) algorithm is one of these algorithms, and the initial born of this algorithm was provided by the observation of musical performances. By the start of development of HS, several optimization methodologies have been developed for structural engineering problems. In this chapter, a state-of-the-art study is presented for structural optimization problems employing HS or variants of it. It is possible to find the modification period HS on structural engineering problems. The process of several variants is also briefly explained for a structural engineering benchmark problem with computer codes, and the performance of different variants is compared.
Robust smoothing of one‐dimensional data with missing and/or outlier values

Penalized least squares (PLS) is a popular data smoothing technique. However, existing PLS smoothing algorithms behave as low-pass filters (LPF), and, hence, they may introduce distortions to bandpass signals. These algorithms also have difficulties associated with the selection of the smoothing parameter and the order of the difference matrix they utilize. A new PLS smoothing algorithm is developed to overcome these limitations. In the proposed algorithm, two difference matrices and two regularisation parameters are utilized. As a result, the proposed algorithm can act as either an LPF or a bandpass filter. The particular shape of the filter is determined by the values of the regularisation parameters and the orders of the two difference matrices. The spectral characteristics of the smooth signal are utilized to derive closed-form expressions for the optimum values of these four parameters. The derived algorithm is further developed to handle the case of having missing and/or outlier samples in the measured data. The proposed algorithms are completely data driven, and they do not require any special optimisation tools. In addition, the proposed algorithms are also shown to produce significantly improved results compared to some existing smoothing techniques.
Recent Advancement in Speech Recognition for Bangla: A Survey

This paper presents a brief study of remarkable works done for the development of Automatic Speech Recognition (ASR) system for Bangla language.It discusses the information of available speech corpora for this language and reports major contributions made in this research paradigm in the last decade.Some important design issues to develop a speech recognizer are: levels of recognition, vocabulary size, speaker dependency, and approaches for classifications; these have been defined in this paper in the order of complexity of speech recognition.It also highlights some challenges which are very important to resolve in this exciting research field.Different studies carried out on the last decade for Bangla speech recognition have been shortly reviewed in chronological order.It was found that the selection of classification model and training dataset play important roles in speech recognition.
Identity of Things: Applying concepts from Self Sovereign Identity to IoT devices

Identity is a crucial property of Internet of Things (IoT) devices.Due to rapid growth and high numbers of similar devices, reliable identification of those devices is a problem.The origin and history of an IoT device is especially important in security-relevant environments.Our research addresses this issue by proposing an approach based on blockchain and decentralised identifiers (DID).It is inspired by the concepts of self-sovereign identity (SSI) and bootstrapping of remote secure key infrastructures (BRSKI).Devices are equipped by the manufacturer with an identity stored in a trusted execution environment (TEE) and secured by a blockchain.This identity can be used to trace back the origin of the device.During the bootstrapping process on the customer side, the identity registration of the device is updated in the blockchain.This process is performed by a so-called registrar.Smart contracts prevent unsolicited transfer of ownership and track the history of the device.Besides proof of origin and device security our concept can be used for device inventory and firmware upgrade.A prototype implementation was realised to validate the concept.All six use cases have been implemented and tested using an Ethereum blockchain infrastructure.JSON Web Tokens (JWT) have been used as signed artefacts to transfer information between the stakeholders.This enables an asynchronous communication needed for example in an environment with no direct internet access.Such an infrastructure can be provided by an independent association and can be used by all manufacturers.Depending on the environment a registration of devices can be optional or mandatory.
A Hybrid Modified Deep Learning Data Imputation Method for Numeric Datasets

Missing data is a major problem in terms of both machine learning and data mining methods.Like most of these methods do not work with missing data, negative results may occur on the performance of the working ones, also.Imputation is a data preprocessing method used to replace missing data with appropriate values.This study aims at developing a hybrid modified imputation method based on deep learning approach.For this purpose, we use Random Forest and Datawig deep learning imputation (called RF-DLI) methods together.Datawig is a deep learning-based library that supports missing value imputation for all types of data.RF-DLI approach includes the following steps to impute missing data.First, the importance of each attribute of the data set is determined with Random Forest (RF).Second, the most important 50% of the attributes are selected.Finally, each missing value is imputed with datawig (DLI) using these most important attributes.The study uses six real-world data sets from different fields with 30% missing data.The imputation performance of RF-DLI is compared to K-Nearest Neighbors (KNN), Multiple Imputation by Chained Equations (MICE), MEAN imputation, and Principle Component Analysis (PCA) imputation approaches in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-square (R 2 ) evaluation metrics.The results show that in most cases, the RF-DLI approach has better imputation performance than the other techniques mentioned.
Robust image hashing based on structural and perceptual features for authentication of color images

Image hashing is one of the most celebrated techniques regarding the discipline of image forensics, imageretrieval, image indexing, content verification, and zero watermarking. For such sensitive and complex problems,generation of a unique and robust image hash is an utmost prerequisite for an image identifier driven from the perceptualcontents of an image. As a design perspective, it is essential for an image hash to have robustness and optimizeddiscriminative capability. We propose a robust image hashing technique by acquiring perceptual features based on anovel distance magnitude profile utilizing color pixel incongruity among the contiguous pixels, as well as producing astructural image for discrimination. The combination of both results provide a state-of-the-art robust profile that hase?icient discriminative capacity and is impervious to usual signal processing violations.
Fruit Classification Model Based on Residual Filtering Network for Smart Community Robot

With the rapid development of computer vision and robot technology, smart community robots based on artificial intelligence technology have been widely used in smart cities. Considering the process of feature extraction in fruit classification is very complicated. And manual feature extraction has low reliability and high randomness. Therefore, a method of residual filtering network (RFN) and support vector machine (SVM) for fruit classification is proposed in this paper. The classification of fruits includes two stages. In the first stage, RFN is used to extract features. The network consists of Gabor filter and residual block. In the second stage, SVM is used to classify fruit features extracted by RFN. In addition, a performance estimate for the training process carried out by the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -fold cross-validation method. The performance of this method is assessed with the accuracy, recall, F1 score, and precision. The accuracy of this method on the Fruits-360 dataset is 99.955%. The experimental results and comparative analyses with similar methods testify the efficacy of the proposed method over existing systems on fruit classification.