Reviewerly/exHarmony huggingface dataset:
The Intelligentization of Second Language Acquisition Assisted Cognitive Linguistics Based on Internet Dynamic Information Network

This paper combs the basic concepts of dynamic system theory in detail, including the overall connectivity, nonlinearity, self-organization and attractive state of the system, and the sensitivity and difference of the initial state. And from the neurophysiological level, the hot issues of second language acquisition are discussed briefly, so as to provide a new idea for the research of second language acquisition. It is proposed that cognitive linguistics can provide coherent and appropriate theoretical guidance and method reference for second language acquisition. The deepening of second language acquisition research can enrich and test the theoretical assumptions of cognitive linguistics, and promote the continuous maturity and improvement of the theoretical system. Through the statistical analysis of the pre-test and post-test of the CET4 simulated test scores of the two groups of students, it is found that this training mode is helpful to improve the students' English ability.
Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler

In services such as metaverse, which should provide a constant quality of service (QoS) regardless of the user’s physical location, the end-to-end (E2E) latency must be fairly distributed over any flow in the network. To this end, we propose a reinforcement learning (RL)-based scheduler for minimizing the maximum network E2E latency. The RL model used the double deep Q-network (DDQN) with the prioritized experience replay (PER). In order to see the performance change according to the type of RL agent, we implemented a single-agent environment where the controller is an agent and a multi-agent environment where each node is an agent. Since the agents were unable to identify E2E latencies in the multi-agent environment, the state and reward were formulated using the estimated E2E latencies. To precisely evaluate the RL-based scheduler, we set out benchmark algorithms to compare with which a network-arrival-time-based heuristic algorithm (NAT-HA) and a maximum-estimated-delay-based heuristic algorithm (MED-HA). The RL-based scheduler, first-in-first-out (FIFO), round-robin (RR), NAT-HA, and MED-HA were compared through large-scale simulations on four network topologies. The simulation results in fixed-packet generation scenarios showed that our proposal, the RL-based scheduler, achieved the minimization of maximum E2E latency in all the topologies. In other scenarios with random flow generation, the RL-based scheduler and MED-HA showed the lowest maximum E2E latency for all topologies. Depending on the topology, the maximum E2E latency of NAT-HA was equal to or larger than that of the RL-based scheduler. In terms of fairness, the RL-based scheduler showed a higher level of fairness than that of FIFO and RR. NAT-HA had similar or lower fairness than the RL-based scheduler depending on the topology, and MED-HA had the same level of fairness as the RL-based scheduler.
Robust control and recursive modelling approach for hexarotor manipulator

This article presents the design, realization, modelling, and control of an aerial manipulator robot capable of performing tasks considered difficult and dangerous for humans. We propose a manipulation system that consists of two subsystems: an aerial platform represented by a hexacopter and a 3 degree-of-freedom manipulator arm that is light and easy to integrate into our hexacopter. The modelling approach of our aerial manipulation system is inspired by the dynamics of floating base systems with tree structures. This type of system presents many control challenges due to system asymmetry, continuous change of inertia, the centre of gravity, manipulator arm effects, and so on. For these reasons, a robust control technique based on a sliding mode controller is proposed to achieve a three-dimensional trajectory-tracking objective. In order to show the effectiveness of the proposed strategy, numerical simulations have been performed in many scenarios.
COVID-19, Didactic Practices, and Representations Assumed by Preservice Teachers at Universidad Técnica del Norte-Ecuador

As an alternative for university students to continue their professional training during the COVID-19 pandemic, higher education institutions implemented virtual learning modalities. In this context, it was proposed to determine the social representations assumed by university students who are pursuing their studies as future educators. It is presumed that representations related to didactic practices are composed of content (knowledge, skills, and attitudes) and organization (central core and representational system). This is an ethnographic study, with an available nonprobabilistic sample of 227 students from the primary education major at Universidad Técnica del Norte. Verbal association techniques and documentary research were used for information collection. To analyze the data, the IRaMuTeQ software (R interface for texts and questionnaire multidimensional analysis) was used. Two types of analyses were conducted: hierarchical classification and factorial correspondence. In conclusion, a virtuality with difficulties and a careful and responsible face-to-face modality are expressed as meanings associated to the representations, both of which require qualitative changes. Regarding the organization, didactic practice complementarity is assumed to be integrated in a hybrid learning modality.
Sensing Magnetic Field with Single-Spin Dynamical Probe State: Control over Sensing Precision via Quantum Fisher Information

Quantum sensors play an important role in many branches of modern science, and they occupy a huge segment of the growing market for quantum devices. Quantum sensors use qubits and their analogs as detecting and analyzing quantum elements. Some sensors can be based on a single qubit, which is often presented as a system making its evolution on the so-called Bloch sphere. Different criteria are used to evaluate the efficiency of the sensing process. One of the most popular is the Quantum Fisher Information Matrix (QFIM) based on Fisher information. The magnitudes of the QFIM elements are strongly related to the precision of the sensing. As an analog of the classical Cramér theorem, one can define the quantum Cramér-Rao bound for the variance V, which is equal to V = 1/NF where F is the corresponding quantum Fisher information element, and N stands for the number of repeated sensory measurements. In this work, we develop our quantum Fisher information-based approach for a single feedback-driven qubit-type element for sensing external magnetic fields. We demonstrate the efficiency of our algorithm and discuss its further possible improvement. The approach developed here can be easily extended to other sensing schemes: collective spin systems and multi-qubit-based sensors. Alternative control algorithms can be applied to drive the probe state vector for maximization of the QFIM components. The particular choice of the control algorithm is defined by the specific experimental set-up.
Artificial awareness, as an innovative learning method and its application in science and technology

The creation of the information society is associated with the creation of new intellectual, cultural, spiritual and material values, as well as with new principles and methods of social and interpersonal communication. Achieving this goal is impossible without changes in teaching methodology, teaching technologies and teacher’s work. The article is an overview and focuses on the following issues. In the information society, the era of biocomputers and quantum computers is coming, which will use not only artificial intelligence, but also artificial consciousness for simulation. Artificial awareness builds the foundations for the development of robots that will be widely used in various fields of industry and science. - Artificial awareness combined with artificial intelligence can be an innovative method in education and communication; - Quantum computers and biocomputers will find wide application in human education and social life;
The coexistence of blockchains and business models (innovation): a systematic review

Purpose Blockchain (BC) is a technological innovation that emphasizes the opposite paradigm compared to the available technology. This paradigm enables changing the firm’s business models (BMs) and has been elaborated by many experts. However, the discussion is scattered in various sources, particularly academic journals. This study aims to investigate the literature on the coexistence of BCs and BMs and depict the currently available situation that has not been discussed. Design/methodology/approach This study investigated articles focusing on the coexistence of BCs and BMs through heterogeneous academic databases, namely, Emerald, ProQuest, Taylor &amp; Francis, ScienceDirect and Scopus. The systematic approach and development of inclusion criteria used in this study resulted in 52 key articles for further review. This systematic review followed the PRISMA framework and a timeframe between 2012 and 2022. Findings This study classifies literature based on specific themes, the integration of BC (interaction and evolution) and BM innovation (innovativeness, new value system and system logic), including the research design. As expected, the literature on BCs and BMs appears to be focused on particular themes since this topic appears to have grown. This study identifies gaps in the literature and describes future research to accommodate the study discrepancy. Research limitations/implications The major limitation of this study is the research bias. Such a bias might occur due to the misinterpretations of researchers in this study. In the process of devising databases and keywords, this study identified the potential for misinterpretation. This study sought to use rigid protocols through a manual approach to mitigate the potential bias. A research bias also has the potential to arise in the literature classification. A literature categorization is performed back and forth, by referring to the theory or concept of a particular topic. The next limitation is limited access to scientific databases. This study drew upon several reputable scientific journal databases. However, the researcher considered the journal selection to be built upon a journal’s accessibility, multi-disciplinary nature and data size compared to other journals. It allows the analysis results to be biased, as they do not represent all available databases. However, the study used the available formal access to maintain the integrity of this research. Originality/value This study conducts a systematic review that discusses the coexistence of BCs and BMs. Furthermore, it provides a profound understanding of the discussion carried out through certain themes and the outlook for the future.
HighLight

With smartphones becoming more commonplace in our daily lives, they often take up more time and space than we would like them to. Research shows that using smartphones during social interactions does more harm than good. With this in mind, we set out to create the first prototype of an ambient robotic table that will support social interactions and discourage digital distractions. Through a rapid prototyping process, we present HighLight, a prototype of a socially enabling robotic table that has a smartphone compartment in its center and ambient features reacting in real-time to conversations taking place around the table. We report on our contributions to the research community by investigating the design of an ambient robotic table as a social enabler that encourages social interactions through ambiance, thus exploring future directions of non-disruptive technologies that support social interactions.
Book Review

Good representative dictionaries is the most critical part of the BoVW: Bag of Visual Words scheme, used for such tasks as category identification. The paradigm of learning dictionaries from datasets is by far the most widely used approach and there exists a plethora of methods to this effect. Dictionary learning methods demand abundant data, and when the amount of training data is limited, the quality of dictionaries and consequently the performance of BoVW methods suffer. A much less explored path for creating visual dictionaries starts from the knowledge of primitives in appearance models and creates families of parametric shape models. In this work, we develop shape models starting from a small number of primitives and develop a visual dictionary using various nonlinear operations and nonlinear combinations. Compared with the existing model-driven schemes, our method is able to represent and characterize images in various image understanding applications with competitive, and often better performance.
Computer-dependent or computer-assisted? The social context of online crime in Lithuanian court judgements

This paper addresses the relative lack of studies concerning characterisation of online crime in court judgements. The goal was to outline the social context of computer-dependent online offences in Lithuania as enacted in court practice. It is an internationally relevant example of the applied outcomes of local legislation enacting the Budapest Convention on Cybercrime. We focused on four key themes to explore how computer-dependent online crime was defined in court practice: offences, offenders, victims, and sentences, bound by the concept of selectivity in criminal justice. A mixed-methods approach was utilised to analyse 265 court judgements in online offence cases in Lithuania from the years 2017–2021. The results revealed that the majority of the offences were computer-enabled rather than computer-dependent, carried out by persons with previous experience in the criminal justice system seeking financial gain.
Effect of Super Resolution on Low-Resolution MRI Segmentation

Recently, numerous attempts have been devoted to applying deep-learning-based super resolution to medical images. However, discussions on its usefulness have been limited to the use of indices such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and the significance of its application has not been widely discussed. This study aimed to compare several deep learning (DL) —based super-resolution methods using publicly available brain magnetic resonance imaging datasets. The impact of training the segmentation models on super-resolved images was also investigated. The results demonstrated the superiority of the DL-based model over traditional image interpolation methods and the limitations of its application in medical imaging. Additionally, the results indicated that PSNR and SSIM might not always be suitable as evaluation indices.
Cooperative Coevolutionary NSGA-II with Linkage Measurement Minimization for Large-Scale Multi-objective Optimization

In this paper, we propose a novel decomposition method based on cooperative coevolution (CC) to deal with large-scale multi-objective optimization problems (LSMOPs) named Linkage Measurement Minimization (LMM), and after decomposition, NSGA-II is employed to optimize the subcomponents separately. CC is a mature and efficient framework for solving large-scale optimization problems (LSOPs), which decomposes LSOPs into multiple nonseparable subcomponents and solves them alternately based on a divide-and-conquer strategy. The essence of the successful implementation of the CC framework is the design of decomposition methods. However, in LSMOPs, variables in different objective functions may have different interactions, and the design of a proper decomposition method for LSMOPs is more difficult than for single objective optimization problems. Our proposed LMM can identify the relatively strong interactions and search the better decomposition iteratively. We evaluate our proposal on 21 benchmark functions of 500-D and 1000-D, and numerical experiments show that our proposal is quite competitive with the current popular decomposition methods.
Development of an SIS Mixer-Based Low-Noise Amplifier Amenable to Josephson Oscillator Pumping

We propose a low-noise and low-power-consumption microwave amplifier using superconductor-insulator-supercon <?hbrk?> ductor (SIS) mixers as quasi-particle frequency up- and down-converters pumped by a Josephson array oscillator in the millimeter-wave band. A proof-of-concept study of an amplifier with two W-band waveguide mixers using Nb/Al-AlOx/Nb junctions in a cascade connection demonstrated an average gain of 6 dB and a noise temperature of 12 K from nearly DC to 5 GHz. We also designed and fabricated a Josephson array oscillator at 100 GHz, which consisted of 31 overdamped Josephson junctions placed at each half-wavelength in an Nb microstrip line. The estimated output power of the oscillator was 0.2 μW at 100 GHz, which can be increased by increasing the number of the oscillator junctions. These results suggest that the proposed SIS mixer-based amplifier is suitable for on-chip integrated circuits to be implemented in large-scale multipixel SIS receivers and quantum computers.
Federated Learning Hyperparameter Tuning From a System Perspective

Federated learning (FL) is a distributed model training paradigm that preserves clients’ data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose, an automatic FL hyper-parameter tuning algorithm tailored to applications’ diverse system requirements in FL training. iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of for diverse applications and FL aggregation algorithms, we show that is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of is available at.
Design and Control of a Novel 3-D Piezoelectric Scanning Coaxial Optical Microscope System

This paper presents a novel three-dimensional (3D) optical measurement system combining an optical microscope (OM) and a z-axis piezoelectric scanner. The proposed system includes an advanced controller for the piezoelectric scanner and an algorithm to process OM imaging data. The z-axis piezoelectric scanner uses a back-propagation neural network (BPNN) to control the upward movement of the sample in a step-wise manner. At the point where each step induces a small steady-state error, the OM captures an image in that position. Each 2D OM image undergoes denoising via connected-component labeling (CCL) before being converted into 3D data via image processing. The layered images are then stacked in a 3D arrangement to construct an accurate 3D image of the sample.
FORTAGONO: A Model for the Technological Mediation of the Teaching and Learning Processes

The role of technology in society is not passive. Little by little, technology has changed the way we communicate, express, and conduct ourselves in our daily lives. In the case of education, this has led to a transformation of the educational experience of teachers and students. However, not all are equally prepared to take advantage of it. To explore ways to contribute to the mitigation of this problem, inspired by participatory design techniques, we conducted a series of activities with teachers and students. After a qualitative analysis of the results, a model for the successful technological mediation of the teaching and learning processes emerged, which we call FORTAGONO. In this document we present this model and its evolution. We also discuss its benefits and limitations, and talk about our future work to develop a framework of use.
On the Shortest Codeword of the Optimal RVLC

We prove that if the probability of the most likely symbol of a memoryless source <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{{\text { max}}}$ </tex-math></inline-formula> is greater than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {5}{8}$ </tex-math></inline-formula> , then the optimal reversible variable length code (RVLC) assigns a single bit to it. Moreover, we prove that if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{{\text { max}}} &gt; \frac {1}{2}$ </tex-math></inline-formula> (respectively, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p_{{\text { max}}} &gt; \frac {2}{5}$ </tex-math></inline-formula> ), then its codelength in the optimal RVLC can be up to two bits (respectively, three bits).
Robustness Analysis of Neural Network Designs for ReLU Family and Batch Normalization

The so-called neural network (NN) robustness problem, its original definition is that if an image is perturbed, the classification result of the image can still maintain the original correct category. It means that at the end of the entire NN operation, the lower bound of the target category value must be greater than the upper bound value of all other categories. There are multiple design techniques which can either bring the neural networks higher accuracy or maintain accuracy while reducing the computation effort at the same time. However, very few work focus on giving an efficient and reliable estimation of the trend of robustness changing directly with respect to the design factors. Lacking such information would either damage on designing a robust NN for critical systems or postpone the robustness analysis after the design completed and then results in paying more cost on NN design modifications. In this paper, we not only provide numerous experimental results but also propose three extended lemmas based on the related work which analyzes robustness with Lipschitz constant, to discuss how the two commonly used design factors, the ReLU based activation functions and batch normalization technique, bring the effectiveness to the robustness changing trend, under the condition of that the NN can still retain acceptable accuracy. We can conclude that we encourage to adopt ReLU than its other family activation functions (e.g. Leaky-ReLU and ELU) but discourage to use batch normalization compared with adopting it in the same NN design if we expect for higher robustness.
Emergent Generation of Camouflage Patterns by Artificial Ants

A camouflage pattern is a texture pattern that contains various colors and localized shapes to be blended with landscapes or objects. Although most of camouflage patterns are generally created by randomly combining colors and various local shapes based on human subjectivity, it can be difficult to generate an appropriate camouflage pattern according to a specific background image. In this paper, we propose a method that can emergently generate complex camouflage patterns by using an Ant Painting algorithm. In the proposed method, after some features of an input image are extracted, artificial ants draws a camouflage according to the features. We demonstrate that, by interaction among ants, the proposed method can generate camouflage patterns which are more complex but blends better with the background image than random generation.