Reviewerly/exHarmony huggingface dataset:
Pre-trained Language Models in Biomedical Domain: A Systematic Survey

Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing (NLP) tasks. This also benefits biomedical domain: researchers from informatics, medicine, and computer science (CS) communities propose various PLMs trained on biomedical datasets, e.g., biomedical text, electronic health records, protein, and DNA sequences for various biomedical tasks. However, the cross-discipline characteristics of biomedical PLMs hinder their spreading among communities; some existing works are isolated from each other without comprehensive comparison and discussions. It expects a survey that not only systematically reviews recent advances of biomedical PLMs and their applications but also standardizes terminology and benchmarks. In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks. Particularly, we discuss the motivations and propose a taxonomy of existing biomedical PLMs. Their applications in biomedical downstream tasks are exhaustively discussed. At last, we illustrate various limitations and future trends, which we hope can provide inspiration for the future research of the research community.
N-gram Based Croatian Language Network

In scope of natural language processing, language networks represent a method which enables observation of linguistic units and their interactions in different linguistic contexts. Here, we describe a language network constructed from an N-gram system collected by Croatian online academic spellchecking service Hascheck. Additionally, we present a service providing a set of functionalities which enable analysis of word transitions. The described approach can provide deeper insight into morphological, semantic, and pragmatic features of Croatian language. A use-case scenario related to application from the domain of natural language generation is provided.
Digital Tools for Managing Innovative Infrastructure

The digital transformation of the economy and the conceptual changes in developing new consumer value predetermined new types of interaction between participants in innovation activities. These changes affected the elements of innovative infrastructure, which ensures effective production of open innovations. The increasing communication of innovation process participants remains a problem of innovative infrastructure performance, which requires new forms and configurations based on digital platform solutions. Modern information and communication technologies and their implementation in the innovative infrastructure will reduce investment costs for creation and maintenance of physical objects and decrease the time spent on searching, exchanging, and processing information of innovation process participants. The article considers the innovative infrastructure as an interaction system of subsystems based on digital platforms. The system provides downloadable information and innovative solutions. The paper focuses on the information subsystem of innovative infrastructure, its components, digital platforms, and services. The authors define distributed and integrated technologies for managing innovative infrastructure that provide communications in a single ecosystem of heterogeneous information network services of innovation process participants. The authors introduce effective mathematical tools of modified GERT networks for modeling computational data processing in heterogeneous environments of digital services of innovative infrastructure.
A Low-Cost Video Analytics System with Velocity Based Configuration Adaptation in Edge Computing

In this paper, we propose a low-cost video analytics system analyzing multiple video streams efficiently under limited resource. The objective of our proposed system is to find the best configuration decision of frame sampling rate for multiple video streams in order to minimize the accuracy degradation in the shared limited resource, utilizing the velocity features extracted from video context in low cost. To evaluate the proposed algorithm, we use a subset of videos from VIRAT dataset. The results show that our video analytics system outperforms the existing video analytics systems on resource-accuracy trade-offs and reduces the high profiling cost of them.
Advances in Spatially Faithful (3D) Telepresence

Benefits of AR technologies have been well proven in collaborative industrial applications, for example in remote maintenance and consultancy. Benefits may also be high in telepresence applications, where virtual and mixed reality (nowadays often referred as extended reality, XR) technologies are used for sharing information or objects over network. Since the 90’s, technical enablers for advanced telepresence solutions have developed considerably. At the same time, the importance of remote technologies has grown immensely due to general disruption of work, demands for reducing travelling and CO2, and the need for preventing pandemics. An advanced 3D telepresence solution benefits from using XR technologies. Particularly interesting are solutions based on HMD or glasses type of near-eye-displays (NED). However, as AR/VR glasses supporting natural occlusions and accommodation are still missing from the market, a good alternative is to use screen displays in new ways, better supporting e.g. virtual meeting geometries and other important cues for 3D perception. In this article, researchers Seppo Valli, Mika Hakkarainen, and Pekka Siltanen from VTT Technical Research Centre of Finland describe the status, challenges, and opportunities in both glasses and screen based 3D telepresence. The writers also specify an affordable screen based solution with improved immersiveness, naturalness, and efficiency, enhanced by applying XR technologies.
Comparing Area-Based and Feature-Based Methods for Co-Registration of Multispectral Bands on GPU

Registration is required as a previous step for processing multispectral images. The different bands captured by each sensor for each image, as well as the different images corresponding to the same area, need to be aligned. In this paper, a 2-level registration scheme comparing the results obtained by the hyperspectral Fourier-Mellin (HYFM) and hyperspectral KAZE (HSI-KAZE) registration methods is proposed. It is designed for efficient implementation in a multi-GPU system in which different scenes are registered in parallel on different GPUs.
An affine moment invariant for multi-component shapes

Abstract We introduce an image based algorithmic tool for analyzing multi-component shapes. The method allocates a number to a shape, herein called a multi-component shape measure. This number/measure is invariant with respect to affine transformations and is established based on the theoretical framework developed here. In addition, the method is easy to implement and fast to compute. We provide two small but illustrative examples related to the analysis of both aerial and celestial galaxy imagery. We also provide some synthetic examples for a better understanding of the behavior of the measure.
Implementation of the principle of integration in teaching mathematics to primary school children

The article discusses the features of the implementation of the integration principle in teaching mathematics to primary school children. Integration in the article refers to the natural relationship of Sciences, subject areas, individual sections and topics based on the unifying idea of a consistent, comprehensive disclosure of the studied processes and phenomena. It is noted that due to the spread of the ideas of integration as a didactic principle of learning among the pedagogical community, the concepts of "integral", "integration", "integrative" and "integrated" have been mixed, so the article clarifies the definitions of these concepts and defines the areas of their application. The authors introduce the concept of "integrated task", which is understood as a study task, for which students need to apply knowledge and skills from various subject areas. The result of such a task may be an awareness of the connection between the studied objects, Sciences, or a universal method of action, the application of which does not depend on the subject area. The article notes that an integrated task can be designed in a vertical and horizontal structure. The basis for building an integrated task in a vertical structure is the proximity of the content of subject areas. To build an integrated task in a horizontal structurethe basis is the methods of activity that are characteristic of one subject area and can be applied when studying material in another subject area. The article provides examples of integrated tasks based on the example of mathematics, designed in both vertical and horizontal structures.
A Practical Framework for Quantum Error Mitigation

Quantum error mitigation is expected to play a crucial role in the practical applications of quantum machines for the foreseeable future. Thus it is important to put the numerous quantum error mitigation schemes proposed under a coherent framework that can highlight their underlying connections while providing guidance for their practical performance. In this article, we construct a general framework named linear quantum error mitigation that includes most of the state-of-the-art quantum error mitigation schemes. Within the framework, quantum error mitigation can be effectively viewed as extracting the error-mitigated state out of the noisy state, which introduces a new metric called extraction rate for indicating the cost-effectiveness of a given mitigation scheme. Using the framework, we have derived and compared the extraction rate, improvement in the fidelity and sampling overhead across various mitigation schemes under practical assumptions. The structure, insights and intuitions provided by the framework can serve as a basis for the further development of new schemes.
Using Natural Language Processing to Understand Reasons and Motivators Behind Customer Calls in Financial Domain

In this era of abundant digital information, customer satisfaction has become one of the prominent factors in the success of any business. Customers want a one-click solution for almost everything. They tend to get unsatisfied if they have to call about something which they could have done online. Moreover, incoming calls are a high-cost component for any business. Thus, it is essential to develop a framework capable of mining the reasons and motivators behind customer calls. This paper proposes two models. Firstly, an attention-based stacked bidirectional Long Short Term Memory Network followed by Hierarchical Clustering for extracting these reasons from transcripts of inbound calls. Secondly, a set of ensemble models based on probabilities from Support Vector Machines and Logistic Regression. It is capable of detecting factors that led to these calls. Extensive evaluation proves the effectiveness of these models.
Skull stripping using traditional and soft-computing approaches for magnetic resonance images : a semi-systematic meta-analysis

MRI scanner captures the skull along with the brain and the skull needs to be removed for enhanced reliability and validity of medical diagnostic practices. Skull Stripping from Brain MR Images is significantly a core area in medical applications. It is a complicated task to segment an image for skull stripping manually. It is not only time consuming but expensive as well. An automated skull stripping method with good efficiency and effectiveness is required. Currently, a number of skull stripping methods are used in practice. In this review paper, many soft-computing segmentation techniques have been discussed. The purpose of this research study is to review the existing literature to compare the existing traditional and modern methods used for skull stripping from Brain MR images along with their merits and demerits. The semi-systematic review of existing literature has been carried out using the meta-synthesis approach. Broadly, analyses are bifurcated into traditional and modern, i.e. soft-computing methods proposed, experimented with, or applied in practice for effective skull stripping. Popular databases with desired data of Brain MR Images have also been identified, categorized and discussed. Moreover, CPU and GPU based computer systems and their specifications used by different researchers for skull stripping have also been discussed. In the end, the research gap has been identified along with the proposed lead for future research work.
A COMPREHENSIVE SURVEY OF VARIOUS CLUSTERING PARADIGMS

Clustering is one of the most significant tasks in data mining. It is highly significant in the entire procedure of knowledge discovery. Clustering is an unsupervised learning task that is utilized to discover hidden patterns in the dataset that can't be classified appropriately and plainly. It groups the dataset in the form of clusters having similar characteristics. There are various clustering paradigms which are applied on the dataset depending on expected cluster characteristics. These algorithms are applied based on the data type of attributes, the algorithm’s complexity, and aptness for a specific grouping. Clustering is performed for various purposes. This paper attempts to study different clustering algorithms that are categorized into various clustering paradigms. The algorithms are reviewed and analyzed on various parameters. It also presents a comparison table based on efficiency to handle high dimensional data, handle outliers and noise, scalability, sensitiveness to the input sequence, space and time complexities.
Preliminary Fluid Geochemical Survey in Tete Province and Prospective Development of Geothermics in Mozambique

Abstract An evaluation of the feasible development of geothermal energy in Mozambique is proposed based on some thermal springs geochemical characterization in the Tete region. Chemical and isotopic data suggest that the springs have a meteoric origin and do not show connection with any active magmatic system. The proposed circulation model suggests high depths infiltration of meteoric waters along faults and fractures in a system characterised by discrete permeability and reservoir temperature between 90 and 120°C. These results, jointly with low salinity fluids and corrosive components absence suggest that the geothermal system may be conveniently exploited for direct and indirect uses.
Learning Realtime One-Counter Automata

Abstract We present a new learning algorithm for realtime one-counter automata. Our algorithm uses membership and equivalence queries as in Angluin’s $${L}^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>L</mml:mi> </mml:mrow> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> algorithm, as well as counter value queries and partial equivalence queries. In a partial equivalence query, we ask the teacher whether the language of a given finite-state automaton coincides with a counter-bounded subset of the target language. We evaluate an implementation of our algorithm on a number of random benchmarks and on a use case regarding efficient JSON-stream validation.
ViDT: An Efficient and Effective Fully Transformer-based Object Detector

Transformers are transforming the landscape of computer vision, especially for recognition tasks. Detection transformers are the first fully end-to-end learning systems for object detection, while vision transformers are the first fully transformer-based architecture for image classification. In this paper, we integrate Vision and Detection Transformers (ViDT) to build an effective and efficient object detector. ViDT introduces a reconfigured attention module to extend the recent Swin Transformer to be a standalone object detector, followed by a computationally efficient transformer decoder that exploits multi-scale features and auxiliary techniques essential to boost the detection performance without much increase in computational load. Extensive evaluation results on the Microsoft COCO benchmark dataset demonstrate that ViDT obtains the best AP and latency trade-off among existing fully transformer-based object detectors, and achieves 49.2AP owing to its high scalability for large models. We will release the code and trained models at https://github.com/naver-ai/vidt
SurroundNet: Towards Effective Low-Light Image Enhancement

Although Convolution Neural Networks (CNNs) has made substantial progress in the low-light image enhancement task, one critical problem of CNNs is the paradox of model complexity and performance. This paper presents a novel SurroundNet which only involves less than 150$K$ parameters (about 80-98 percent size reduction compared to SOTAs) and achieves very competitive performance. The proposed network comprises several Adaptive Retinex Blocks (ARBlock), which can be viewed as a novel extension of Single Scale Retinex in feature space. The core of our ARBlock is an efficient illumination estimation function called Adaptive Surround Function (ASF). It can be regarded as a general form of surround functions and be implemented by convolution layers. In addition, we also introduce a Low-Exposure Denoiser (LED) to smooth the low-light image before the enhancement. We evaluate the proposed method on the real-world low-light dataset. Experimental results demonstrate that the superiority of our submitted SurroundNet in both performance and network parameters against State-of-the-Art low-light image enhancement methods. Code is available at https: github.com/ouc-ocean-group/SurroundNet.
Pengembangan Media Pembelajaran Interaktif Berbasis Problem Based Learning (PBL) Berbantuan Android Untuk Meningkatkan Kemampuan Pemecahan Masalah Matematis Peserta Didik Kelas VII SMP Negeri 1 Sosopan

This research is motivated by the low mathematical problem solving ability of junior high school students. This study aims to produce a valid, practical, and effective Android-assisted problem-based interactive learning media to facilitate mathematical problem solving abilities. This research is a development research carried out with the Plomp development model. The Plomp development model consists of three stages, namely preliminary investigation, prototyping stage, and assessment phase. The research was conducted at SMPN 1 Sosopan with the research subjects being material experts, linguists and educational technology experts as well as seventh grade students at SMPN 1 Sosopan. Data collection instruments in the form of interviews, documentation, questionnaires and test questions. The data obtained were analyzed using qualitative and quantitative data analysis techniques. The results showed that the Android-assisted problem-based interactive mathematics learning media developed was classified as very valid (89,11% for lesson plans and 87,73% for media) and very practical, respectively 87% and 86,85%. (for lesson plans and small group media) 88,1% and 88,02% (for lesson plans and large group media). Meanwhile, based on the test results of the mathematical problem solving ability test, it was found that 81,5% of students met the criteria for the success of the mathematical problem solving ability test > 80%, meaning that the interactive learning media based on Android-assisted problem-based learning was very effective on mathematical problem solving abilities.
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression

Stochastic gradient descent (SGD) has been shown to generalize well in many deep learning applications. In practice, one often runs SGD with a geometrically decaying stepsize, i.e., a constant initial stepsize followed by multiple geometric stepsize decay, and uses the last iterate as the output. This kind of SGD is known to be nearly minimax optimal for classical finite-dimensional linear regression problems (Ge et al., 2019). However, a sharp analysis for the last iterate of SGD in the overparameterized setting is still open. In this paper, we provide a problem-dependent analysis on the last iterate risk bounds of SGD with decaying stepsize, for (overparameterized) linear regression problems. In particular, for last iterate SGD with (tail) geometrically decaying stepsize, we prove nearly matching upper and lower bounds on the excess risk. Moreover, we provide an excess risk lower bound for last iterate SGD with polynomially decaying stepsize and demonstrate the advantage of geometrically decaying stepsize in an instance-wise manner, which complements the minimax rate comparison made in prior works.
Enhanced Parameter Estimation with Periodically Driven Quantum Probe

I propose a quantum metrology protocol for measuring frequencies and weak forces based on a periodic modulating quantum Jahn-Teller system composed of a single spin and two bosonic modes. I show that, in the first order of the frequency drive, the time-independent effective Hamiltonian describes spin-dependent interaction between the two bosonic modes. In the limit of high-frequency drive and low bosonic frequency, the quantum Jahn-Teller system exhibits critical behavior which can be used for high-precision quantum estimation. A major advantage of the scheme is the robustness of the system against spin decoherence, which allows it to perform parameter estimation with measurement time not limited by spin dephasing.
A Novel Hybrid Deep Multi-Criteria Model for Recommender System

Abstract Recommender systems are everywhere books, products, movies, and more. Traditional recommender systems typically use a single criterion in the recommendation, while studies have shown that multi-criteria recommending is more accurate. Novel deep learning techniques have produced remarkable achievements in many fields. The use of such techniques in recommendation systems has started to get attention recently, and several models of recommendation have been proposed based on deep learning. However, there is still no work for using deep learning in hybrid multi-criteria recommender systems. In this work, a model for a hybrid deep multi-criteria recommender system was presented. The model mainly includes two major parts: In the first one, the model obtains the user ID, item ID, and the item metadata to be used as input to a deep neural network in order to predict the criteria ratings. In the second part, the obtained ratings act as an input to another deep neural network, where the overall rating is predicted. Our experiments were conducted on a real-world dataset. They demonstrated the superiority of the proposed novel model over the other models in all measures used to evaluate performance. This indicates the successful use of hybrid deep multi-criteria in the recommendation systems.
Loss-tolerant prepare and measure quantum key distribution protocol

We propose a modified version of the Bennett–Brassard 1984 quantum key distribution protocol intended to tolerate losses, certain forms of noise, and the so-called photon-number splitting attack. These are the issues facing the realization of practical quantum key distribution. The modified protocol is based on quantum non-demolition measurements for systems using weak coherent pulses. Our scheme ensures that emissions corresponding to zero photon pulses, multi-photon pulses, and detector double-clicks are discarded before sifting to improve sifting efficiency and increase the secret key rate. Moreover, we perform the finite key analysis to obtain the maximal achievable secret-key fraction and the corresponding optimal number of signals. Also, we compare our proposed protocol to the decoy-state quantum key distribution protocol. We observe that our proposed quantum key distribution scheme enables a more extended transmission distance than the decoy-state quantum key distribution protocol. Thus, this an advance in quantum communication because current limitations on quantum key distribution involve transmitting secret keys over more considerable distances in the presence of noise or losses in optical fibres.