Reviewerly/exHarmony huggingface dataset:
$k$-Forrelation Optimally Separates Quantum and Classical Query Complexity.

Aaronson and Ambainis (SICOMP ‘18) showed that any partial function on N bits that can be computed with an advantage δ over a random guess by making q quantum queries, can also be computed classically with an advantage δ/2 by a randomized decision tree making Oq(N1−1/2qδ−2) queries. Moreover, they conjectured the k-Forrelation problem — a partial function that can be computed with q = ⌈ k/2 ⌉ quantum queries — to be a suitable candidate for exhibiting such an extremal separation. We prove their conjecture by showing a tight lower bound of Ω(N1−1/k) for the randomized query complexity of k-Forrelation, where δ = 2−O(k). By standard amplification arguments, this gives an explicit partial function that exhibits an Oє(1) vs Ω(N1−є) separation between bounded-error quantum and randomized query complexities, where є>0 can be made arbitrarily small. Our proof also gives the same bound for the closely related but non-explicit k-Rorrelation function introduced by Tal (FOCS ‘20). Our techniques rely on classical Gaussian tools, in particular, Gaussian interpolation and Gaussian integration by parts, and in fact, give a more general statement. We show that to prove lower bounds for k-Forrelation against a family of functions, it suffices to bound the l1-weight of the Fourier coefficients between levels k and (k−1)k. We also prove new interpolation and integration by parts identities that might be of independent interest in the context of rounding high-dimensional Gaussian vectors.
BIDSonym: a BIDS App for the pseudo-anonymization of neuroimaging datasets

sharing is important and beneficial (Eickhoff et al., 2016;Nichols et al., 2017;Poldrack & Gorgolewski, 2014;Poline et al., 2012), the privacy of participant data must be protected (Bannier et al., 2020;Brakewood & Poldrack, 2013).To that end, Ethic Review Boards and data sharing platforms typically require that uploaded datasets are provided in anonymized or pseudoanonymized form, limiting participant reidentification.However, the (pseudo-)anonymization process is deceptively complex; attempts at ensuring data privacy must take into consideration all dataset components, including imaging modalities, as well as national legal and ethical frameworks.Several algorithms have been developed to (pseudo-)anonymize imaging datasets but they offer limited solutions.Some are attached to specific software and some are limited to specific computing environments; most miss an in-depth assessment and treatment of the metadata attached to the dataset or lack the capacity to automatize (pseudo-)anonymization across large datasets.BIDSonym was created to address these points in one simple, flexible, and general tool that offers users an array of automated (pseudo-)anonymization options to augment participant privacy in neuroimaging datasets.There are two components of neuroimaging datasets that arguably pose the largest risk to maintaining participant privacy: the structural images and accompanying metadata (e.g., metadata text files or information embedded in image file headers).Structural images contain visible identifiable participant information via facial features like the eyes, nose, and mouth, and privacy is usually addressed through a process called "defacing," within which all or a subset of these features are removed from the final structural data files.The metadata text files may additionally contain identifiable participant data through the recording of acquisition time and location, and personal details such as date of birth, height, and weight.Here, privacy is maintained by removing or blurring this information from the final dataset.BIDSonym addresses both vulnerabilities in neuroimaging datasets, obviating the need for multiple steps within a data sharing pipeline to ensure participant privacy.
Speciality 05.25.03 “Library science, bibliography and book science”. What are the prospects?

The material presents the views of the chairs and members of the dissertation councils at the KazGIK, MGIK, SPGIK and RSL on the specialty 05.25.03 indicated in the title on four issues: 1. Are you confident in the future of your dissertation council? What is your confidence or anxiety related to? 2. What do you think are the problems of modern library science, bibliography and book studies? 3. There are only 4 dissertation councils on the specialty – 03.25.03 – in Russia: in Kazan, Moscow, St. Petersburg, Khimki. Do you think this is sufficient for the development of our industry? 4. What question (s) on the dissertation council would you like to be asked? The answers showed concern for the future of not only of dissertation counsils as such, but confirmed serious concerns about the future of the specialty 05.25.03.
Activity Classification Using Backpropagation Neural Networks for the Daily Lives of the Elderly

Activity Analysis Systems or Activity Recognition Systems for the elderly is recently a part of the smart home systems design. This assisted system normally helps the senior people to live alone in a house, safely and improve a quality of life. Therefore, learning to recognize which activities are safe is necessary for classifying the activities of the elderly. This information will give the researchers in the assistive technology some insights to understand the basic daily lives of the elderly. Moreover, it is also help the caregivers to monitor activities of the senior people while they live alone in the house. In this paper, the novel method for detecting and recognizing the activities using Backpropagation Neural Networks has been proposed. The proposed model was tested on a set of basic daily activities (lie, stand, sit, walk and dine). The proposed model was trained to construct the Backpropagation Neural Networks model and used the trained model to classify basic daily activities of the elderly. The proposed model gives the results of 0.78, 0.72 and 0.74 of precision, recall and F1 score, respectively. The discussion and future extension are also given in this paper.
An Attribute-Aligned Strategy for Learning Speech Representation

Advancement in speech technology has brought convenience to our life. However, the concern is on the rise as speech signal contains multiple personal attributes, which would lead to either sensitive information leakage or bias toward decision. In this work, we propose an attribute-aligned learning strategy to derive speech representation that can flexibly address these issues by attribute-selection mechanism. Specifically, we propose a layered-representation variational autoencoder (LR-VAE), which factorizes speech representation into attribute-sensitive nodes, to derive an identity-free representation for speech emotion recognition (SER), and an emotionless representation for speaker verification (SV). Our proposed method achieves competitive performances on identity-free SER and a better performance on emotionless SV, comparing to the current state-of-the-art method of using adversarial learning applied on a large emotion corpora, the MSP-Podcast. Also, our proposed learning strategy reduces the model and training process needed to achieve multiple privacy-preserving tasks.
Research on the Construction of the University Innovation and Entrepreneurship Education Ecosystem Based on Combination of Computer and Engineering

Abstract Computer technology develops rapidly, computer management systems have brought better development to college education. In the era that students are encouraged to innovate and start businesses, computer systems have greatly promoted the development of college innovation and entrepreneurship education. This article mainly analyzes and studies the construction of the ecosystem and its application in college students’ innovation and entrepreneurship education.
INTELLIGENT DATA ANALYSIS IN BIOMEDICAL RESEARCH: ARTIFICIAL NEURAL NETWORKS

Substantial amounts of biomedical data are being accumulated every year. Large datasets are accumulated in specialized repositories, electronic document management systems, medical information systems, and other repositories. Classical statistical analysis does not always provide opportunities for analysis of these large datasets; therefore, intelligent data analysis (IDA) is becoming more popular in biomedical research. This paper is an introduction to artificial neural networks - one of the most popular methods of IDA. An artificial neural network is an attempt to build a mathematical analog of the brain and mathematically simulate the transmission of a nerve impulse between neurons. We present an example of the application of artificial neural networks in medical research using SPSS and Statistica software packages. The article describes a medical research question, an example of a dataset and a guide on construction and training of an artificial neural network as well as interpretation of the results.
Quantum key distribution with two-way authentication

Abstract Quantum key distribution uses the principle of quantum physics to realize unconditionally secure key distribution protocol. But this kind of security needs to be based on the authenticated classical channel. Although there are quantum key distribution protocols without classical channel, authentication is still needed. In the process of key distribution, authentication is not considered, which is also a problem of quantum key distribution protocol. In this paper, a quantum key distribution protocol with two-way authentication is proposed. Identity authentication is carried out at the same time of key distribution. If the identity authentication fails, the key distribution protocol cannot be carried out. If the key distribution protocol is aborted, the identity authentication is not successful. The conclusion of this paper is based on a central authentication system supported by symmetric encryption theory, which uses pseudo-random functions, multiple sets of quantum conjugate bases and Measurement-device-independent technology to simultaneously achieve two-way authentication and key distribution.
Speed Bump Recognition for Autonomous Vehicles Based on Semantic Segmentation

In this paper, we combine 3Dmax and PreScan to make data sets and use the recognition algorithm to get the region of interest (ROI) of the speed bumps area. Then, we realize the speed bump recognition for autonomous vehicles. Besides, we do the post-processing operations on the area corresponding to the disparity map. Finally, we provide an innovative method to improve the accuracy of the distance measurement of the speed bumps. The results show that the method in this paper can accurately recognize the speed bumps and measure the distance under the requirements of the automotive embedded system.
Implementasi Algoritma Synthetic Minority Over-Sampling Technique untuk Menangani Ketidakseimbangan Kelas pada Dataset Klasifikasi

In this research, the subscriber of class imbalance to the minority class was carried out using a resampling technique, namely oversampling. The oversampling algorithm used is Synthetic Minority Over-sampling Technique (SMOTE). The results of this study were compared with the results of the classification without resampling. The evaluation tests used are accuracy, Geometric Mean (g-mean), and Confusion Matrix (CM). Handling the unbalanced class distribution on the dataset using the SMOTE algorithm can increase the accuracy and g-mean values of the Naïve Bayes, SVM, KNN and Decision Tree algorithms. This shows that the handling process of the unbalanced class distribution at the pre-processing stage has an effect on the accuracy and g-mean values of the Naïve Bayes, SVM, KNN and Decision Tree algorithms. In the experimental scenario that has been carried out the NaïveBayes algorithm has the best accuracy of 96.43%, SVM with 99.02%, KNN with 97.29% and Decision Tree with a value of 97.29% on the ecoli dataset of 15.8 after SMOTE with 10 fold cross validation.Meanwhile, it has the best G-mean value of 96.42% for the Naïve Bayes algorithm, SVM with 99.37%, KNN with 99.53% and Decision Tree with a value of 96.29% in the ecoli dataset of 15.8 after SMOTE with 10 fold cross validation.
Content and Types of Safety of the Educational Environment

Aim. To clarify the basic componentof the content of the educational environment, as well as to highlight its components and types. Procedure and methods. The article identifies the main components of the educational environment, from the point of view of the philosophical approach to understanding this phenomenon, as well as analyzed and evaluated the reasons that make it unsafe, the allocation of levels of expansion, the classification of the types of safety of the educational environment. In the study methods are used: analogies, comparative, generalization, system, interpretation of the results, thought experiment. The results. The work clarifies the content of the educational environment, reveals its main components, on the basis of a philosophical approach to this phenomenon, has been identified the impact of various types of expansion on its safety, identified and justified the basic safety of the educational environment, taking into account the current state of public practice in the country and the world, as well as the requirements that are imposed on educational institutions on the education and education of trainees. Theoretical and/or practical significance. The results of the study contribute to the palette of research of the educational environment in school and university, to the methodology of organizing the safety of the educational environment, as well as to the practice of educational activities of schools and universities.
Tourism Souvenir Ceramics Craft Creation: Design Inspiration of Malang Local Artefact Culture

Crafts should be developed as part of the tourism sector, because when this pandemic ends, the tourism sector will need to rise up and be ready to answer the challenge. The tourism industry should be supported by souvenir crafts, but the products lack the design variance that is reflected in the local cultural character. This research aimed to develop designs based on local culture. The methods included studying local culture, formulating local culture concepts as Malang souvenir ceramic creation ideas, doing creative shape exploration and technique exploration, creating art and product prototypes, testing and validating the product diversification in terms of concept and prototype, and lastly, improving the product and analyzing the creative results.&#x0D; Keywords: Malang souvenir ceramics, design diversification
COMPARISON OF TABLETOP DISASTER EXERCISE (TDE) AND EDUCATIONAL FILM ON DISASTER PREPAREDNESS AMONG INDONESIAN VOCATIONAL HIGH SCHOOL STUDENT: A QUASI-EXPERIMENTAL STUDY

Background: Teenagers play an essential role in the Disaster Risk Reduction program or DRR. Teenagers need to get adequate knowledge related to disasters through disaster education. Various studies have been conducted to obtain effective media in disaster education, some of them are Tabletop Disaster Education (TDE) and educational film. However, there are no studies yet comparing the effectiveness of these media in increasing disaster preparedness. Aim: The purpose of this study is to compare TDE and educational film in increasing disaster preparedness in flash floods for vocational students. Methods: A quasi-experimental with a pretest-posttest nonequivalent control group was used in this study. One hundred and four students in tenth-grade vocational high school participated in this study and were divided into two groups, TDE group, and the educational film group. Samples were taken using a simple random sampling technique. Data were analyzed using paired t-test and independent t-test with SPSS software version 22.0. Result:There was a significant difference in disaster preparedness between the two groups before and after treatment (p=0.000), and there was a significant difference in disaster preparedness between the TDE and educational film (p=0.000), with a higher variance of TDE of 6.94. Conclusion:This study demonstrated that there was a significant difference in disaster preparedness between two groups, and TDE showed better results in increasing disaster preparedness among students compared to the educational film. Recommendation. Further research is needed that involves confounding factors of academic intelligence and knowledge retention in both media TDE and educational film.
L’uso degli articoli e la coesione in testi narrativi scritti di studenti polacchi

Use of articles and cohesion in narrative texts written by Polish students The present article aims to present which anaphoric means use students to create a cohesive narrative story. A particular attention is focused on pronouns and on articles as elements absent in Polish language. Three groups of students representing respectively A2, B1 and B2 levels were examined to find out if they use different means while improving their competences in Italian. Another aim of the study was to analyse which parts of narration are susceptible to be bad constructed from the cohesive point of view. The results could have a positive impact on didactic methods applied during courses of writing competences.
The Effects of Virtual Reality and Animation Supported Science Education on Students’ Some Learning Products

In this study, it is aimed to examine the effects of virtual reality supported science education on the subject of “Nervous System” from the unit of “Controller and Regulatory System” on 6th grade the students’ cognitive levels and meta cognitive awareness. The sample of the study is 108 6th grade students (1st experimental group 36, 2nd experimental group 36, control group 36) from a public school in Altınordu district from city of Ordu in 2018-2019 educational year. While virtual reality method was applied for the first experimental group, animation supported teaching applications were applied for the second experimental group. For the control group, the students were taught with science education curriculum. As a result, it is determined that the students from virtual reality experimental group had significantly higher points than the other two groups. It is also observed that the students who were taken virtual reality based science lessons had higher metacognitive awareness.
The Challenges of Developing a Living Arabic Phonetic Dictionary for Speech Recognition System: A Literature Review

Phonetic dictionaries are regarded as pivotal components of speech recognition systems. The function of speech recognition research is to generate a machine which will accurately identify and distinguish the normal human speech from any other speaker. Literature affirmed that Arabic phonetics is one of the major problems in Arabic speech recognition. Therefore, this paper reviews previous studies tackling the challenges faced by initiating an Arabic phonetic dictionary with respect to Arabic speech recognition. It has been found that the system of speech recognition investigated areas of differences concerning Arabic phonetics. In addition, an Arabic phonetic dictionary should be initiated where the Arabic vowels’ phonemes should be considered as a component of the consonants’ phonemes. Thus, the incorporation of developed machine translation systems may enhance the quality of the system. The current paper concludes with the existing challenges faced by Arabic phonetic dictionary.
An Interactive Evolutionary Environment for Creative Design

A creative and/or innovative computer aided design environment is developed around the concept of an evolutionary optimization algorithm.Designs are generated within a set of prescribed design frameworks using a problem specific encoding and modified through operations including recombination, crossover and mutation.Evaluation of all candidate designs is performed by a user through a graphical user interface.A set of problems involving the creation of graphic images is presented.The examples include the generation of a set of two dimensional polygonal shapes, fractal images, path generation from a multi-degree freedom mechanical planar linkage and a mathematically prescribed pattern generation from a graphic design application utilized in quilting.Post design evaluation of the user input to the process provides insight into the individuals design strategy as well as determination of common user attributes in the creative design process.
Intellectual capital and Tobin’s Q as measures of bank performance

This paper aims at examining the effect of the Value Added by Intellectual Capital (VAIC) in terms of its three components: capital employed efficiency, human capital efficiency, and structure capital efficiency on the financial performance of commercial banks listed on the Amman Stock Exchange for the period 2010–2018.Value Added of Intellectual Capital (VAIC) model was used to measure the intellectual capital while Tobin’s Q ratio was used as an indicator of bank financial performance. The study has used parametric techniques like multiple linear regression and correlation coefficient, and other statistical methods to investigate its hypothesis. It was found that only human capital efficiency and capital employed efficiency had impacts on the banks’ financial performance. These results emphasize the importance of using the VAIC model to evaluate the financial performance of these banks, as well as encourage banks to make further investments in intellectual capital’s components, and concentrate on human resources to build up their knowledge, skills and capabilities, because of their greatest role in value creation.
Improving Indoor Semantic Segmentation with Boundary-Level Objectives

While most of the recent literature on semantic segmentation has focused on outdoor scenarios, the generation of accurate indoor segmentation maps has been partially under-investigated, although being a relevant task with applications in augmented reality, image retrieval, and personalized robotics. With the goal of increasing the accuracy of semantic segmentation in indoor scenarios, we develop and propose two novel boundary-level training objectives, which foster the generation of accurate boundaries between different semantic classes. In particular, we take inspiration from the Boundary and Active Boundary losses, two recent proposals which deal with the prediction of semantic boundaries, and propose modified geometric distance functions that improve predictions at the boundary level. Through experiments on the NYUDv2 dataset, we assess the appropriateness of our proposal in terms of accuracy and quality of boundary prediction and demonstrate its accuracy gain.
Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm

This paper studies the basketball goal recognition method based on image processing and improved algorithm to improve the accuracy of automatic recognition of basketball goal. The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by using the adaptive filtering algorithm, the wavelet analysis method is used to extract the features of basketball goal signal, which are input into the optimized deformable convolution neural network. Through the weighted sum of the values of each sampling point and the corresponding position authority of the block convolution core, the results are output as convolution operation. Combined with the depth feature of the same dimension, the full connection feature of the candidate target area is obtained to realize the basketball goal recognition. The experimental results show the following: the method can effectively identify basketball goals and the recognition error rate is low; the average accuracy of the automatic recognition results of basketball goals is as high as 98.4%; under the influence of different degrees of noise, the method is less affected by noise and has strong anti-interference ability.