Reviewerly/exHarmony huggingface dataset:
Fundamentals Of Development Of Creative Abilities Of Students In Professional Training

This article describes the creative qualities of students and ways to develop creative skills in career guidance, the successful formation of creative thinking skills.
Universal hybrid quantum computing in trapped ions

Using discrete and continuous variable subsystems, hybrid approaches to quantum information could enable more quantum computational power for the same physical resources. Here, we propose a hybrid scheme that can be used to generate the necessary Gaussian and non-Gaussian operations for universal continuous variable quantum computing in trapped ions. This scheme utilizes two linear spin-motion interactions to generate a broad set of non-linear effective spin-motion interactions including one and two mode squeezing, beam splitter, and trisqueezing operations in trapped ion systems. We discuss possible experimental implementations using laser-based and laser-free approaches.
BIFNOM: Binary-Coded Features on Normal Maps

We propose a novel method for detecting features on normal maps and describing binary features, called BIFNOM, which is three-dimensionally rotation invariant and detects and matches interest points at high speed regardless of whether a target is textured or textureless and rigid or non-rigid. Conventional methods of detecting features on normal maps can also be applied to textureless targets, in contrast with features on luminance images; however, they cannot deal with three-dimensional rotation between each pair of corresponding interest points due to the definition of orientation, or they have difficulty achieving fast detection and matching due to a heavy-weight descriptor. We addressed these issues by introducing a three dimensional local coordinate system and converting a normal vector to a binary code, and achieved more than 750fps real-time feature detection and matching. Furthermore, we present an extended descriptor and criteria for real-time tracking, and evaluate the performance with both simulation and actual system.
Research of consensus algorithms for blockchain platforms

In this paper, several consensus algorithms are investigated, a comparative analysis of these algorithms is made, the results of which are important in the context of the active growth of the cryptocurrency market. In addition, in the context of pandemics, the creation of blockchain technologies for the distributed storage of reliable medical data is relevant. The key properties of consensus algorithms are highlighted, and a detailed analysis of the algorithm of the promising Istanbul BFT algorithm is carried out. Using multi-criterion analysis of alternatives, it is shown how you can select the appropriate consensus algorithm for the needs of specific tasks.
Ternary Primitive LCD BCH codes

Absolute coset leaders were first proposed by the authors which have advantages in constructing binary LCD BCH codes. As a continue work, in this paper we focus on ternary linear codes. Firstly, we find the largest, second largest, and third largest absolute coset leaders of ternary primitive BCH codes. Secondly, we present three classes of ternary primitive BCH codes and determine their weight distributions. Finally, we obtain some LCD BCH codes and calculate some weight distributions. However, the calculation of weight distributions of two of these codes is equivalent to that of Kloosterman sums.
On the group of unit-valued polynomial functions

Let $R$ be a finite commutative ring with $1\ne 0$. The set $\mathcal{F}(R)$ of polynomial functions on $R$ is a finite commutative ring with pointwise operations. Its group of units $\mathcal{F}(R)^\times$ is just the set of all unit-valued polynomial functions, that is the set of polynomial functions which map $R$ into its group of units. We show that $\mathcal{P}_R(R[x]/(x^2))$ the group of polynomial permutations on the ring $R[x]/(x^2)$, consisting of permutations represented by polynomials over $R$, is embedded in a semidirect product of $\mathcal{F}(R)^\times$ by $\mathcal{P}(R)$ the group of polynomial permutations on $R$. In particular, when $R=\mathbb{F}_q$, we prove that $\mathcal{P}_{\mathbb{F}_q}(\mathbb{F}_q[x]/(x^2))\cong \mathcal{P}(\mathbb{F}_q) \ltimes_\theta \mathcal{F}(\mathbb{F}_q)^\times$. Furthermore, we count unit-valued polynomial functions $\pmod{p^n}$ and obtain canonical representations for these functions.
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders

Encoder pre-training is promising in end-to-end Speech Translation (ST), given the fact that speech-to-translation data is scarce. But ST encoders are not simple instances of Automatic Speech Recognition (ASR) or Machine Translation (MT) encoders. For example, we find that ASR encoders lack the global context representation, which is necessary for translation, whereas MT encoders are not designed to deal with long but locally attentive acoustic sequences. In this work, we propose a Stacked Acoustic-and-Textual Encoding (SATE) method for speech translation. Our encoder begins with processing the acoustic sequence as usual, but later behaves more like an MT encoder for a global representation of the input sequence. In this way, it is straightforward to incorporate the pre-trained models into the system. Also, we develop an adaptor module to alleviate the representation inconsistency between the pre-trained ASR encoder and MT encoder, and develop a multi-teacher knowledge distillation method to preserve the pre-training knowledge. Experimental results on the LibriSpeech En-Fr and MuST-C En-De ST tasks show that our method achieves state-of-the-art BLEU scores of 18.3 and 25.2. To our knowledge, we are the first to develop an end-to-end ST system that achieves comparable or even better BLEU performance than the cascaded ST counterpart when large-scale ASR and MT data is available.
Optimizing Deep Learning Based Intrusion Detection Systems Defense Against White-Box and Backdoor Adversarial Attacks Through a Genetic Algorithm

Recent years have witnessed rapid progress and significant success in the use of deep learning neural networks (DLNNs) in a wide range of applications. Recently, DLNN has been integrated with intrusion detection system (IDS) to enhance network security to detect zero-day attacks. However, DLNNs themselves have been recently found vulnerable to attacks called adversarial examples and backdoor attacks for image recognition applications. In this work, we present an effective defense method for DLNN based IDS by using Genetic Algorithm (GA) to optimize the generation of triggers neurons selected based on their response to the features to produce the output. We embed the GA-Trigger-Detection neurons within the model to detect and prevent white-box advertorial examples and backdoor attacks against two DLNNs based IDS: Deep Belief Network (DBN) and Stacked Sparse AutoEncoder Based Extreme Learning Machine (SSAELM). We implement two white-box adversarial examples and backdoor attacks from prior work and use them to investigate the proposed defense method. We show that the defense method is sufficient to defend against sophisticated attackers with 99% success rate and only 1% degrade in accuracy. We then show that it successfully weakens backdoor attacks on the two DNN architectures using two benchmark datasets: KDDCUP'99 and Kyoto. Our work provides an important step toward defenses against white-box advertorial examples and backdoor attacks in DLNNs based IDS.
Classification of Groundwater by Applying Simplified Fuzzy Adaptive Resonance Theory

Groundwater quality assessment is primarily intended to determine whether the water in a particular area can be used for aquatic purpose or not.The assessment comprises analysis of physical, chemical and microbiological characteristics of groundwater samples.The quality of groundwater can be evaluated through few standard conventional methods viz.Water Quality Index, Canadian Council of Ministries Environmental Water Quality Index and Weighted Arithmetic Water Quality Index etc.In addition to the conventional methods, multivariate statistical methods like Principal Component Analysis, Factor Analysis and Cluster Analysis can also be used to assess the groundwater quality.As these methods are descriptive models, they are inadequate to predict the quality of unknown groundwater sample.Hence, an efficient predictive model is desirable to analyze the characteristic parameters of groundwater samples and predict the quality of an unknown sample.The sample may have both crisp and fuzzy values.Conventional supervised learning methods may not be suitable for constructing the required prediction model as they are not suitable for handling fuzzy input data.Therefore, simplified fuzzy adaptive resonance theory model is an appropriate choice for accomplishing the task of building the prediction model.The present work proposes to assess the quality of groundwater by applying the Weighted Arithmetic Water Quality Index method and Simplified Fuzzy Adaptive Resonance Theory model by considering 7 groundwater quality parameters.The accuracy of afore mentioned approach seems to be pleasing when compared to counter parts like Back propagation and Random Forests classifiers.
Deep Convolutional Neural Networks models for the binary morphological classification of SDSS-galaxies

We present the deep learning approach for the determination of morphological types of galaxies. We demonstrate the method's performance with the redshift-limited (z &lt; 0.1) training sample of 6 163 galaxies from the SDSS DR9. We exploited the deep convolutional neural network classifiers such as InceptionV3, DenseNet121, and MobileNetV2 to process images of SDSS-galaxies (100x100 pixels, 25 arcsec in each axis in size) using g, r, i filters as R - G - B channels to create images. We provided the data augmentation (horizontal and vertical flips, random shifts on ±10 pixels, and rotations) randomly applied to the set of images during learning, which helped increase the classifier's generalization ability. Also, two different loss functions, MAE and Lovasz-Softmax, were applied to each classifier. The target sample galaxies were classified into two morphological types (late and early) trained on the images of galaxies from the sample. It turned out that the deep convolutional neural networks InceptionV3 and DenseNet121 with MAE-loss function show the best result attaining 93.3% accuracy.
Quantifying entanglement of two-qubit Werner states

In this article, we introduce a framework for entanglement quantification of photon pairs represented by two-qubit Werner states. The measurement scheme is based on the symmetric informationally complete POVM. To make the framework realistic, we impose the Poisson noise on the measured two-photon coincidences. For various settings, numerical simulations were performed to evaluate the efficiency of the framework.
Canine Semen Evaluation Using Transfer Learning Models

This paper has experimented different architectures of transfer learning models (TLM) for canine semen evaluation and improving the performance of Deep Learning algorithms. There are 332 total datasets used for training and testing of different convolutional network models. These models are: MobileNetV1, InceptionV2, ResNet50, ResNet101 and Inception_ResNetV2. The canine sperm classification is successfully executed. In the evaluation of these models, a comparison of the manual method to the systems accuracy and error was made. Through these evaluations, the highest accuracy of 87% is obtained by Inception_ResNetV2 and the lowest accuracy of 40% is the MobileNetv1.
Efficient Single Image Dehazing Model Using Metaheuristics-Based Brightness Channel Prior

Haze degrades the spatial and spectral information of outdoor images. It may reduce the performance of the existing imaging models. Therefore, various visibility restoration models approaches have been designed to restore haze from still images. But restoring the haze is an open area of research. Although the existing approaches perform significantly better, they are not so effective against a large haze gradient. Also, the effect of hyperparameters tuning issue is also ignored. Therefore, a brightness channel prior (BCP) based dehazing model is proposed. The gradient filter is utilized to improve the transmission map computed using the gradient filter. Nondominated Sorting Genetic Algorithm is also used to optimize the initial parameters of the BCP approach. The comparative analysis shows that BCP performs effectively across a wide range of haze degradation levels without causing any visible artifacts.
Algorithm and model for improve the avoiding of deadlock with increasing efficiency of resource allocation in cloud environment

Abstract Recently, cloud computing has been the most popular method among all other promising technologies that provides the service for many users at the same time. all users can reach and use the resource that is provided by the Virtual Machines (VMs). Hence users process, data must arrive to the resources to perform as a disturbed system, which provides dynamical scale services and virtualized resources. As a result of limitation of resources, the deadlock status may occur in such type of systems. In this paper, we have designed an algorithm that could increase the efficiency of deadlock avoidance. Our technique will use the attribute of execution time in the process to get better resource allocation in additional to the checking system to stay in safe state and free of deadlock.
A Review of Accessing Big Data with Significant Ontologies

Ontology Based Data Access (OBDA) is a recently proposed approach which is able to provide a conceptual view on relational data sources. It addresses the problem of the direct access to big data through providing end-users with an ontology that goes between users and sources in which the ontology is connected to the data via mappings. We introduced the languages used to represent the ontologies and the mapping assertions technique that derived the query answering from sources. Query answering is divided into two steps: (i) Ontology rewriting, in which the query is rewritten with respect to the ontology into new query; (ii) mapping rewriting the query that obtained from previous step reformulating it over the data sources using mapping assertions. In this survey, we aim to study the earlier works done by other researchers in the fields of ontology, mapping and query answering over data sources.
Swarm: A federated cloud framework for large-scale variant analysis

Genomic data analysis across multiple cloud platforms is an ongoing challenge, especially when large amounts of data are involved. Here, we present Swarm, a framework for federated computation that promotes minimal data motion and facilitates crosstalk between genomic datasets stored on various cloud platforms. We demonstrate its utility via common inquiries of genomic variants across BigQuery in the Google Cloud Platform (GCP), Athena in the Amazon Web Services (AWS), Apache Presto and MySQL. Compared to single-cloud platforms, the Swarm framework significantly reduced computational costs, run-time delays and risks of security breach and privacy violation.
Do your eye movements reveal your performance on an IQ test? A study linking eye movements and socio-demographic information to fluid intelligence.

Understanding the main factors contributing to individual differences in fluid intelligence is one of the main challenges of psychology. A vast body of research and numerous empirical findings are based on the framework of the Cultural-Fair IQ test 20-R (CFT 20-R). In this work, we extend and complement the current state of research by analysing the relationship of socio-demographic and eye-movement-related information and the ability of a participant to correctly solve a CFT test item. Our work shows that a participant's eye movements while solving a CFT test item contain discriminative information and can be used to predict whether the participant will succeed in the current task. Moreover, the information related to eye movements complements the information provided by socio-demographic data when it comes to success prediction. In combination, both types of information yield a significantly higher predictive performance than each information type individually. To better understand the influence of features related to eye movements and socio-demographic information for the prediction of a participant's performance on a CFT item, we employ state-of-the-art explainability techniques and show that, along with socio-demographic variables, eye-movement data, especially the number of saccades and the mean pupil diameter, adds highly discriminating power. This ultimately suggests more efficient processing and more invested mental effort. Beyond the specific contribution to research on the relationship between eye movements and fluid intelligence, the findings presented in this work pave the way for further in-depth investigations of factors predicting fluid intelligence.
Spectral characterization of SPDC-based single-photon sources for quantum key distribution

Abstract In our laboratory, we employ two biphoton sources for quantum key distribution. The first is based on cw parametric down-conversion of photons at 404 nm in PPKTP waveguide chips, while the second is based on the pulsed parametric down-conversion of 775 nm photons in PPLN waveguides. The spectral characterization is important for the determination of certain side-channel attacks. A Hong-Ou-Mandel experiment employing the first photon source revealed a complex structure of the common Hong-Ou-Mandel dip. By measuring the spectra of the single photons at 808 nm, we were able to associate these structures to the superposition of different transverse modes of the pump photons in our waveguide chips. The pulsed source was characterized by means of single-photon spectra measured by a sensitive spectrum analyzer as well as dispersion-based measurements. Finally, we also describe Hong-Ou-Mandel experiments using the photons from the second source.
Benchmarking near-term quantum computers via random circuit sampling

The increasing scale of near-term quantum hardware motivates the need for efficient noise characterization methods, since qubit and gate level techniques cannot capture crosstalk and correlated noise in many qubit systems. While scalable approaches, such as cycle benchmarking, are known for special classes of quantum circuits, the characterization of noise in general circuits with non-Clifford gates has been an unreachable task. We develop an algorithm that can sample-efficiently estimate the total amount of noise induced by a layer of arbitrary non-Clifford gates, including all crosstalks, and experimentally demonstrate the method on IBM Quantum hardware. Our algorithm is inspired by Google's quantum supremacy experiment and is based on random circuit sampling. In their paper, Google observed that their experimental linear cross entropy was consistent with a simple uncorrelated noise model, and claimed this coincidence indicated that the noise in their device was uncorrelated -- a key step in hardware development towards fault tolerance. As an application, we show that our result provides formal evidence to support such a conclusion.
The Formal, Financial and Fraught Route to Global Digital Identity Governance

How can we understand the progressive, piecemeal emergence of global digital identity governance? Examining the activities of the Financial Action Task Force (FATF) - an intergovernmental organization at the center of global anti-money laundering and counter-the-financing of terrorism governance-this paper advances a two-fold argument. First, the FATF shapes how, where and who is involved in developing key standards of acceptability underpinning digital identity governance in blockchain activities. While not itself directly involved in the actual coding of blockchain protocols, the FATF influences the location and type of centralized modes of control over digital identity governance. Drawing on the notion of protocological control from media studies, we illustrate how centralized control emerging in global digital identity governance emanates from the global governance of financial flows long considered by international organizations like the FATF. Second, we suggest that governance by blockchains persistently shapes the ability of the FATF to stem illicit international financial flows. In highlighting both the influence of FATF on blockchain governance and blockchain governance on the FATF, we draw together two strands of literature that have been considered separately in an analysis of the formal, financial and fraught route to global digital identity governance.
Electronics and DAQ for the Magnetized mini-ICAL Detector at IICHEP

Magnetized iron calorimeter (ICAL) detectorSaraf, M. N. built using Resistive Plate Chambers (RPCs), which are interleaved between the iron plates, isPathaleswar, proposedNagaraj, P. by India-based Neutrino Observatory (INO), toRavindran, K. C. study atmospheric neutrinos [1]. Ten layersSatyanarayana, B. of RPCs are deployed in the mini-ICAL which is an 85-ton magnetized detector stack with 20 RPCs in 10 layers. The mini-ICAL is being used for prototyping data acquisition electronics, its integration with the magnetized detector, back-end software, etc. The analog front-end Amplifier-Discriminator, digital front-end RPC-DAQ and bipolar HV unit to bias RPC are embedded with the RPCShinde, R. R.. The RPC-DAQ mainlyKaur, P. K. generates pre-triggers, acquires event data on a final triggerChandratre, V. B. and monitors the health of RPCSukhwani, M.. A central trigger systemKolla, H. generatesThomas, M. the final trigger by processingShas, Umesh the pre-trigger signalsSil, D.. The relative arrival time offsetsUpadhya, S. S. of the final triggerLokapure, A. at each of theGokhale, U. RPC-DAQs are measuredBharathi, Rajkumar and event timer stampYuvaraj, E. clocks are synchronizedBehere, A. by the CalibrationMoitra, S. and Auxiliary Unit (CAU) housedAyyagiri, N. next to the trigger systemSikder, S.. All the RPC-DAQ nodesJoshi, S.R. are connected to back-end serversManna, A. over LAN for control, data collectionPadmini, A. and monitoringPunna, M.. The back-end server manages the overall run control and detector health monitoringMajunder, G.. The data concentrator server collects data from all the digital front-ends (DFEs) and the eventDatar, V.M. builder server builds events based on event time stamps. The overall design is motivated by modularity, flexibility, reduced cost and power, and the use of open-source software. This paper will highlight the design details and performance of prototype electronics.
HoloBoard: an Immersive Teaching Board System

We present HoloBoard, a next generation teaching board based on large-format, semi-transparent, interactive display technology that supports immersive teaching and learning. It can present immersive media curriculum materials including large-format videos, interactive 3D objects, AR effects, etc. Based on a multi-stage mixed-methods teacher-centered design thinking approach and a series of user studies with 6-9 years old primary students, the concept design of HoloBoard was defined and refined. The results showed that HoloBoard can be deployed in the wild and bring more engagement and interactivity in the primary school classroom, promoting students' spontaneous exploration and active learning.