Reviewerly/exHarmony huggingface dataset:
High-quality strong lens candidates in the final Kilo Degree survey footprint

We present 97 new strong lensing candidates found in the final $\sim 350\,\rm deg^2$, that completed the full $\sim 1350\,\rm deg^2$ area of the Kilo-Degree Survey (KiDS). Together with our previous findings, the final list of candidates from KiDS sums up to 268 systems. The new sample is assembled using a new Convolutional Neural Network (CNN) classifier applied to $r$-band (best seeing) and $g,~r,~i$ color-composited images separately. This optimizes the complementarity of the morphology and color information on the identification of strong lensing candidates. We apply the new classifiers to a sample of luminous red galaxies (LRGs) and a sample of bright galaxies (BGs) and select candidates that received a high probability to be a lens from the CNN ($P_{\rm CNN}$). In particular, setting $P_{\rm CNN}>0.8$ for the LRGs, the $1$-band CNN predicts 1213 candidates, while the $3$-band classifier yields 1299 candidates, with only $\sim$30\% overlap. For the BGs, in order to minimize the false positives, we adopt a more conservative threshold, $P_{\rm CNN} >0.9$, for both CNN classifiers. This results in 3740 newly selected objects. The candidates from the two samples are visually inspected by 7 co-authors to finally select 97 high-quality lens candidates which received mean scores larger than 6 (on a scale from 0 to 10). We finally discuss the effect of the seeing on the accuracy of CNN classification and possible avenues to increase the efficiency of multi-band classifiers, in preparation of next-generation surveys from ground and space.
The Investigation of Using Out-Classroom Experiences to Enhance Indonesian EFL Learners’ Speaking Performance

The role of English in Indonesia can be said quite special because English is the only compulsory foreign language included in secondary curriculums. However, the special status of English in national education is not accompanied by satisfactory results. Out-classroom experience (OCE) is believed to overcome this issue and has been applied successfully. Therefore, this study investigates the use of out-classroom experience in enhancing Indonesian EFL learners’ ability in speaking English. By conducting a case study approach, the research used triangulation data collection techniques consisting of classroom observation, interview, and speaking performance test. The study involved 40 undergraduate students from one of the public universities in Banten province as its subjects. The result showed that the use of OCE positively contributes to the development of students’ speaking development. Out-classroom activities can facilitate the learners to access or even create language exposures by themselves. This study provided some implications about using OCE for language teachers and educational practitioners.&#x0D; Keywords: Out-Classroom experiences, speaking, college students, EFL context
AIT* and EIT*: Asymmetric bidirectional sampling-based path planning

Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their search with problem-specific knowledge expressed as heuristics and can be orders of magnitude more efficient than uninformed algorithms. Heuristics are most effective when they are both accurate and computationally inexpensive to evaluate, but these are often conflicting characteristics. This makes the selection of appropriate heuristics difficult for many problems. This paper presents two almost-surely asymptotically optimal sampling-based path planning algorithms to address this challenge, Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*). These algorithms use an asymmetric bidirectional search in which both searches continuously inform each other. This allows AIT* and EIT* to improve planning performance by simultaneously calculating and exploiting increasingly accurate, problem-specific heuristics. The benefits of AIT* and EIT* relative to other sampling-based algorithms are demonstrated on twelve problems in abstract, robotic, and biomedical domains optimizing path length and obstacle clearance. The experiments show that AIT* and EIT* outperform other algorithms on problems optimizing obstacle clearance, where a priori cost heuristics are often ineffective, and still perform well on problems minimizing path length, where such heuristics are often effective.
Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity

Speech processing systems currently do not support the vast majority of languages, in part due to the lack of data in low-resource languages. Cross-lingual transfer offers a compelling way to help bridge this digital divide by incorporating high-resource data into low-resource systems. Current cross-lingual algorithms have shown success in text-based tasks and speech-related tasks over some low-resource languages. However, scaling up speech systems to support hundreds of low-resource languages remains unsolved. To help bridge this gap, we propose a language similarity approach that can efficiently identify acoustic cross-lingual transfer pairs across hundreds of languages. We demonstrate the effectiveness of our approach in language family classification, speech recognition, and speech synthesis tasks.
Optimised Playout Implementations for the Ludii General Game System

This paper describes three different optimised implementations of playouts, as commonly used by game-playing algorithms such as Monte-Carlo Tree Search. Each of the optimised implementations is applicable only to specific sets of games, based on their rules. The Ludii general game system can automatically infer, based on a game’s description in its general game description language, whether any optimised implementations are applicable. An empirical evaluation demonstrates major speedups over a standard implementation, with a median result of running playouts 5.08 times as fast, over 145 different games in Ludii for which one of the optimised implementations is applicable.
Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective

Modern video object segmentation (VOS) algorithms have achieved remarkably high performance in a sequential processing order, while most of currently prevailing pipelines still show some obvious inadequacy like accumulative error, unknown robustness or lack of proper interpretation tools. In this paper, we place the semi-supervised video object segmentation problem into a cyclic workflow and find the defects above can be collectively addressed via the inherent cyclic property of semi-supervised VOS systems. Firstly, a cyclic mechanism incorporated to the standard sequential flow can produce more consistent representations for pixel-wise correspondance. Relying on the accurate reference mask in the starting frame, we show that the error propagation problem can be mitigated. Next, a simple gradient correction module, which naturally extends the offline cyclic pipeline to an online manner, can highlight the high-frequent and detailed part of results to further improve the segmentation quality while keeping feasible computation cost. Meanwhile such correction can protect the network from severe performance degration resulted from interference signals. Finally we develop cycle effective receptive field (cycle-ERF) based on gradient correction process to provide a new perspective into analyzing object-specific regions of interests. We conduct comprehensive comparison and detailed analysis on challenging benchmarks of DAVIS16, DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is helpful to enhance segmentation quality, improve the robustness of VOS systems, and further provide qualitative comparison and interpretation on how different VOS algorithms work. The code of this project can be found at https://github.com/lyxok1/STM-Training
Meningkatkan Benefits melalui Kepuasan Pengguna berdasarkan Kualitas Sistem Informasi Akademik

The purpose of this research is to determine the influence of academic information system to benefits through user satisfaction of academic information system in Institut Manajemen Koperasi Indonesia. The research method used is a quantitative method with descriptive approach and verification approach. The sample of this research is 184 students who used academic information system. The measuring instrument used in this research is questionaire with 21 statement items. Data analysis techique using path analysis using software IBM SPS 22.0. Based on the results, (1) user satisfaction is influenced by quality of academic information system, (2) benefits is influenced by user satisfaction, and (3) benefits is influenced by quality of academic information system through user satisfaction.
Cybercrime and mental health greatest threats to small firms

Experts say cyber criminals are attacking small law firms and taking advantage of their vulnerabilities, as lawyers increasingly shift their practices online during the pandemic.
MHAttnSurv: Multi-Head Attention for Survival Prediction Using Whole-Slide Pathology Images

In pathology, whole-slide images (WSI) based survival prediction has attracted increasing interest. However, given the large size of WSIs and the lack of pathologist annotations, extracting the prognostic information from WSIs remains a challenging task. Previous studies have used multiple instance learning approaches to combine the information from multiple randomly sampled patches, but different visual patterns may contribute differently to prognosis prediction. In this study, we developed a multi-head attention approach to focus on various parts of a tumor slide, for more comprehensive information extraction from WSIs. We evaluated our approach on four cancer types from The Cancer Genome Atlas database. Our model achieved an average c-index of 0.640, outperforming two existing state-of-the-art approaches for WSI-based survival prediction, which have an average c-index of 0.603 and 0.619 on these datasets. Visualization of our attention maps reveals each attention head focuses synergistically on different morphological patterns.
QDCNN: Quantum Dilated Convolutional Neural Network

In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements, has been massively explored for the purpose of improving the performance of classical neural networks. In this paper, we propose a novel hybrid quantum-classical algorithm called quantum dilated convolutional neural networks (QDCNNs). Our method extends the concept of dilated convolution, which has been widely applied in modern deep learning algorithms, to the context of hybrid neural networks. The proposed QDCNNs are able to capture larger context during the quantum convolution process while reducing the computational cost. We perform empirical experiments on MNIST and Fashion-MNIST datasets for the task of image recognition and demonstrate that QDCNN models generally enjoy better performances in terms of both accuracy and computation efficiency compared to existing quantum convolutional neural networks (QCNNs).
ANALYSIS OF FEATURES AND DEVELOPMENT POTENTIAL OF INTERACTIVE SYSTEMS SUPPORTED BY AUXILIARY REALITY TECHNOLOGIES FOR EDUCATION FIELD

Currently, the market of modern software systems and information technology is developing a special direction of virtual reality as one of the convenient approaches to improve the process of interactive learning and visualization of complex systems, devices and individual elements, an important area of which is augmented reality technology. The aim of the article is to analyze the features and means of developing interactive systems with the support of augmented reality technologies for education to form the concept of further development of a full-featured information system to support the study of academic disciplines. Realization of the set purpose provides the decision of a number of tasks: 1) the analysis of features of application of multimedia technologies in the field of science and education; 2) analysis of technologies and tools for developing systems with elements of virtual and augmented reality; 3) development of the concept of the project of system of support of processes of education. Scientific novelty. The article considers an approach to the use of augmented reality technologies on mobile devices, using the capabilities of the camera module during its activation, to acquire the process of studying theoretical positions in academic disciplines of a higher level of interactivity by means of Unity. In conclusion, the article emphasizes that virtual and augmented reality technologies can greatly simplify the solution of complex problems of visual and dynamic visualization of individual objects or systems. The flexibility and versatility of the Unity system allows you to implement full-fledged clientserver software applications that have a thin client in the form of a cross-platform mobile application and a server part that is responsible for data processing to ensure the efficiency of the entire system.
Design and Implementation of UAV Architecture for Delivering Medical Products

Unmanned Aerial vehicles (UAVs) likewise regularly alluded to as automatons, and are increasing incredible research intrigue, consideration as a significant future innovation. These vehicles have almost boundless potential essentially because of their high mobility and small scale size, that permits their utilization in different applications. Among numerous kinds of UAVs, the multi-copter UAVs with 4 rotors (quadcopter) are being utilized and used regularly. Automatons are semi-self-governing airplanes that can be controlled and worked remotely. Economically accessible automatons are progressively being utilized in an assortment of uses. It is being used in a variety of applications such as,3-D mapping, disaster monitoring, aerial packet delivery, photography and filming, and very useful in the field of monitoring and surveillance also in unmanned cargo system .Recent innovations made huge growth in this field that made the researchers to develop applications which are useful in the field of medicine to deliver drugs ,blood products in remote areas. A medical drugs is delivered to the person based on the location which was identified through GPS module .This system offers the medicine to be delivered to the right person and at right time.
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup

In this paper, we describe our method for tackling the automated hyperparameter optimization challenge in QQ Browser 2021 AI Algorithm Competiton (ACM CIKM 2021 AnalyticCup Track 2). The competition organizers provide anonymized realistic industrial tasks and datasets for black-box optimization. Based on our open-sourced package OpenBox, we adopt the Bayesian optimization framework for configuration sampling and a heuristic early stopping strategy. We won first place in both the preliminary and final contests with the results of 0.938291 and 0.918753, respectively.
Accelerating RNNs on FPGA with HBM

Aiming at the problem that the algorithm of the recurrent neural network is limited by bandwidth, an accelerated SoC based on HBM is designed, which can universally support the RNN and its variants. First, the structure of RNN and its variants, and the calculation requirements and storage requirements of the algorithms., a high-bandwidth accelerator design based on HBM was proposed and deployed on the Xilinx VCU128 development board. Finally, according to the Roofline model analysis method, the bandwidth and calculation density are imprved. The average inference performance of testing DeepSpeech2 and GNMT algorithms are 61.74 GFLOPs/sec and 20GFLOPs/sec respectively. Compared with the design based on DDR memory, the performance is improved by 3.68 times. Compared with the accelerated design of other floating-point 32-bit FPGA-based recurrent neural networks, the performance is improved by 8.5 times. This design proposes a data scheduling method for multi-channel memory and can adapt to different recurrent neural network applications.
Digital archive of the literary magazine with the pre-reform spelling «Otechestvennye Zapiski» (1839-1884)

The paper describes an initial version of the digital archive of the literary magazine with the pre-reform orthography «Otechestvennye Zapiski».Today, the corpus contains 10 XML-volumes of the literary magazine (~ 2 mil.words).The web-application of the digital archive allows users to search for words and lemmas in corpus and to edit magazine's texts online.The future optimization of the digital archive includes expanding the composition of corpus and switching modes between pre-reform and modern orthography.
A New Algorithm Based on Bernstein Polynomials Multiwavelets for the Solution of Differential Equations Governing AC Circuits

We extend the application of multiwavelet-based Bernstein polynomials for the numerical solution of differential equations governing AC circuits (LCR and LC). The operational matrix of integration is obtained from the orthonormal Bernstein polynomial wavelet bases, which diminishes differential equations into the system of linear algebraic equations for easy computation. It appeared that fewer wavelet bases gave better results. The convergence and exactness were examined by comparing the calculated numerical solution and the known analytical solution. The error function was calculated and illustrated graphically for the reliability and accuracy of the proposed method. The proposed method examined several physical issues that lead to differential equations. HIGHLIGHTS Differential equations governing AC circuits are converted into the system of linear algebraic equations using Bernstein polynomial multiwavelets operational matrix of integration for easy computation The convergence and exactness were examined by comparing the calculated numerical solution and the known analytical solution The error function is calculated and shown graphically GRAPHICAL ABSTRACT
QuaSiMo: A composable library to program hybrid workflows for quantum simulation

We present a composable design scheme for the development of hybrid quantum/classical algorithms and workflows for applications of quantum simulation. Our object-oriented approach is based on constructing an expressive set of common data structures and methods that enable programming of a broad variety of complex hybrid quantum simulation applications. The abstract core of our scheme is distilled from the analysis of the current quantum simulation algorithms. Subsequently, it allows a synthesis of new hybrid algorithms and workflows via the extension, specialization, and dynamic customization of the abstract core classes defined by our design. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library. To validate our implementation, we test and show its utility on commercial quantum processors from IBM and Rigetti, running some prototypical quantum simulations.
Online Handwritten Telugu Character Recognition Using Normalized Differential Chain Code Feature

In Character Recognition, the Feature extraction has encompassed a well-known role. Here, Feature Extraction centered on Chain code (CC) is implemented. CC encodes every stroke with a string of numbers, in which every number signifies a specific direction wherein the subsequent point on the stroke is present. CC centered feature safeguard information and permits reasonable data to decrease. Disparate CC can signify the same shape since the CC is reliant on starting point. So here, Starting Point and rotation invariant feature extraction technique using Normalized Differential Chain Code (NDCC) is proposed. A two-stage classifier is employed for classification. Here, the NDCC feature is utilized in the pre-classifier and pre-processed (x,y) coordinates are used in the post classifier. In both stages K-NN classifier is used. This feature is verified in HP-Lab data that is present in the UNIPEN format. Investigational outcomes proved that the proposed feature enhances recognition accuracy over the selected dataset.
Universidades públicas federais brasileiras: Ações e estratégias para a abertura da ciência

This research proposal aims to identify and reflect on the actions of Brazilian federal public universities regarding the dissemination of scientific research, appropriating the importance that Open Science has been gaining. The five schools of thought that represent the perspectives of the authors Fecher and Friesike (2013) will be the focus of this study. It is proposed to address in the literature review the developments involving Open Science such as: open access, scientific publications, knowledge management and analysis and data use. It is intended to identify public policies adopted in Brazilian federal public universities; identify how federal public universities are defining their guidelines related to new ways of sharing data and information, through the perception of researchers; analyze web attention regarding Open Science, using altmetric indices. It is a research of mixed characteristics conducted by the Quadripolar Method, by Bruyne, Herman and Schoutheete (1974).
Explainable Neural Network Ensembles

Abstract Neural networks are known for providing impressive classification performance, and the ensemble learning technique is further acting as a catalyst to enhance this performance by integrating multiple networks. But like neural networks, neural network ensembles are also considered as a black-box because they cannot explain their decision making process. So, despite having high classification performance, neural networks and their ensembles are not suited for some applications which require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks with the power to justify their classification responses through explainable classification rules. Several rule extraction algorithms exist to extract classification rules from neural networks, but only a few of them generates rules using neural network ensembles. So this paper proposes an algorithm named Rule Extraction using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles through rule extraction. RE-E-NNES extracts classification rules by ensembling several neural network ensembles. Results show the efficacy of the proposed RE-E-NNES algorithm compared to different existing rule extraction algorithms.
La investigación acción en la escritura de artículos científicos deportivos y su evaluación en contexto virtual

The objective of the research is to generate an strategy for the evaluation in pairs, since the pedagogical mediation, to elaborate a scientific article and its validation. The problem is the  insufficiency of university students in the argumentative redaction. The methodology is qualitative, and we consider different ideas about the redaction of sportive articles, orientated tasks and the dialogue of the teacher using the virtual space of the synchronical interaction in the class. Between proposals there are the improvement to the process of knowledge’s construction, the searching actions, the individual and group activities, the method of research-action and the systematization of experiences. This components are used in the analysis to arrive to interpretation, and also allow to find significance and sense in the practice of learning at the curricular subjects.