Reviewerly/exHarmony huggingface dataset:
Case marking is different in monolingual and heritage Bosnian in digitally elicited oral texts

Heritage languages may differ from baseline languages spoken in the home country, particularly in the domains of vocabulary, morphosyntax and phonology. The success of acquiring and maintaining a heritage language may depend on a range of factors, from the age of acquisition of the second language; quantity and quality of input and frequency of first language use, to non-linguistic factors, such as Socio-Economic Status (SES). To investigate case marking accuracy in heritage Bosnian in relation to these very factors, we recruited 20 heritage Bosnian speakers in Austria and Germany, and 20 monolingual Bosnian speakers in Bosnia, aged between 18 and 30 years. Participants were assessed remotely in two sessions, on a battery of tests that included a background language questionnaire investigating participants’ history of language acquisition, current usage and SES, and a newly adapted Bosnian version of the Multilingual Assessment Instrument for Narratives (MAIN). A significant difference in case marking accuracy was found between the two groups, despite the 97% correct performance in the heritage speakers, and an almost 100% performance of the monolinguals. In the heritage speakers group only, errors indicated a trend toward case system simplification as well as uncertainty in distinguishing between case meanings. The use of Bosnian, assessed through quantity and quality of input, as well as frequency of current usage, was shown to be a significant predictor of case marking accuracy in heritage speakers. In contrast, SES and age of acquisition of German did not play a role in these participants’ case accuracy. The observed patterns of quantitative and qualitative differences in the case marking accuracy between heritage Bosnian speakers and their monolingual counterparts, in the face of a high level of accuracy, contribute to our understanding of the heritage language attainment in more diverse language dyads where L1 is a lesser studied language.
Blockchain in Real Estate

Abstract: Real estate plays an important role in the national economy, but the existing system has many flaws that affect everything from finding a property to renting to buying and selling. Real estate and blockchain have the potential to work well together. In order to strongly support the premise of real estate in multiple wearing exercises, the land business process utilizes a blockchain model. Either way, the majority of digital data is enabled across multiple frameworks, putting an end to a lack of transparency and an increased rate of errors that create more obvious potential for fraud. Blockchain technology has the potential to improve apartment trading activities. A new strategy for companies that are steadily revealing some of these facts is the un-middle of blockchain for a real estate approach.
THE EFFECT OF USING ELSA (ENGLISH LANGUAGE SPEECH ASSISTANT) SPEAK APPLICATION ON STUDENTS’ SPEAKING ABILITY FOR THE ELEVENTH GRADE OF MAS DARUL AL MUHAJIRIN IN THE ACADEMIC YEAR 2021/2022

This research was conducted to the eleventh grade students of of MAS Darul Al Muhajirin in the academic year 2021/2022. This was an experimental research which used two group pre-test and post-test designs. The sample of research was 40 divided by two classes, XI-MIPA 2 as experimental class and XI-MIPA 1 as control class. This research was conducted to know the positive effect of using ELSA Speak Application on students’ speaking ability. Based on the students’ problems such as students worked hard to produce the English expression, students pronounced the words of the expression in inexact way, student used wrong intonation and stress in sounding the English expression. Having analyzed the data of pre-test and post-test by using t-test, from the result of calculation, it was obtained the value of the t-test was 10,619. The degree of freedom is 38 (df = because there was not df 38 in the t-table, so the writers decide to use df 40 since it was closer to 38 that df 30. The table at df 40 showed that the value of t-table ( was 2,02 for 5% and 2,70 for 1%. This result meant that the value of t-test was not appropriate with the criteria of acceptance of Ho, because the fact showed the value of t-test was higher than t-table (2,02 &lt; 10,619 &gt; 2,70). So, from that explanation, writers concluded that null hypothesis was rejected and alternative hypothesis was accepted. Hence, hypothesis stated that there was a significant effect of using ELSA (English Language Speech Assistant) Speak Application on students’ speaking ability for the eleventh grade of MAS Darul Al Muhajirin in the academic year 2021/2022.
DFSNet: Deep Fractional Scattering Network for LoRa Fingerprinting

Radio frequency fingerprints (RFF) identification is a critical enabling technology to support rapid and scalable device identification in long rang (LoRa) based Internet of Things (IoT). In recent years, the identification precision of RFF has been significantly improved by leveraging artificial intelligence (AI) technologies to deeply exploit RFF features which are hardware-level, unique and resilient. However, traditional AI technologies lack strong interpretability, require massive amounts of training data and occupy huge computing resources. To address above challenges, we in this paper propose a deep fractional scattering network (DFSNet) to extract the RFF features hidden in non-stationary LoRa chirp signal through linear translation-variant multiscale fractional wavelet filters. Due to the fractional-domain deformation stability in DFSNet, the influence of noise on feature extraction can be reduced to the greatest extent by fractional transformation. Firstly, we apply DFSNet to build a hybrid RFF identification interpretability framework where the scattering coefficients of input can be calculated and characterized. Ben-efiting from the application of fractional wavelet transform, we can clearly explain the features represented by each coefficient. Then, the robustness characteristic of the fractional deformation is analyzed. Finally, experiment results show that our proposed hybrid DFSNet can achieve up to about 98.5% recognition accuracy rate with only about 5000 LoRa practical training samples per device.
Crow search algorithm with time varying flight length Strategies for feature selection

Feature Selection (FS) is an efficient technique use to get rid of irrelevant, redundant and noisy attributes in high dimensional datasets while increasing the efficacy of machine learning classification. The CSA is a modest and efficient metaheuristic algorithm which has been used to overcome several FS issues. The flight length (fl) parameter in CSA governs crows' search ability. In CSA, fl is set to a fixed value. As a result, the CSA is plagued by the problem of being hoodwinked in local minimum. This article suggests a remedy to this issue by bringing five new concepts of time dependent fl in CSA for feature selection methods including linearly decreasing flight length, sigmoid decreasing flight length, chaotic decreasing flight length, simulated annealing decreasing flight length, and logarithm decreasing flight length. The proposed approaches' performance is assessed using 13 standard UCI datasets. The simulation result portrays that the suggested feature selection approaches overtake the original CSA, with the chaotic-CSA approach beating the original CSA and the other four proposed approaches for the FS task.
Facial expression recognition based on deep convolutional neural network

Facial expression is a part of body language, which can often convey physiological and psychological reactions. Facial expression has always been a hot topic in the area of image analysis and pattern recognition, whose basic task is to model facial images to recognize human emotions at a certain time. Due to CNN’s strong ability on feature expression, the researches of facial expression recognition based on deep learning have developed rapidly. In this paper, in order to improve the accuracy of facial expression recognition which is low because of the complex environments and structure of the existing expression recognition network, we propose a facial expression recognition method based on region of interest extraction, which uses ROI pooling to extract the local features of different face regions to construct a fixed size shared feature layer. In addition, we also add L2 regularization and learning rate decay mechanisms to find the optimal solution. Quantitative and qualitative experimental outcomes show that it is an effective way to achieve the facial expression task.
SAR and Optical Image Registration Based on Uniform Feature Points Extraction and Consistency Gradient Calculation

Synthetic aperture radar (SAR) satellites have an active sensor on board, which emits electromagnetic signals and measures the strength and time delay of the returned signal backscattered from ground objects. Optical images have rich spectral information, but it is easily affected by atmospheric attenuation and weather conditions. Thus, the study of the registration between these two images is of great significance. We present a novel method for SAR and optical image registration. In the stage of feature points extraction, the method combines phase consistency intensity screening and scale space grid division to obtain stable and uniform feature points from the image. During the stage of feature description, the method employs the extended phase consistency method to calculate the gradient amplitude and direction of the image, and improves the correctness of the main direction calculation and descriptor construction. Experimental results demonstrate its superior matching performance with respect to the state-of-the-art methods.
Enhancing Federated Learning With Spectrum Allocation Optimization and Device Selection

Machine learning (ML) is a widely accepted means for supporting customized services for mobile devices and applications. Federated Learning (FL), which is a promising approach to implement machine learning while addressing data privacy concerns, typically involves a large number of wireless mobile devices to collect model training data. Under such circumstances, FL is expected to meet stringent training latency requirements in the face of limited resources such as demand for wireless bandwidth, power consumption, and computation constraints of participating devices. Due to practical considerations, FL selects a portion of devices to participate in the model training process at each iteration. Therefore, the tasks of efficient resource management and device selection will have a significant impact on the practical uses of FL. In this paper, we propose a spectrum allocation optimization mechanism for enhancing FL over a wireless mobile network. Specifically, the proposed spectrum allocation optimization mechanism minimizes the time delay of FL while considering the energy consumption of individual participating devices; thus ensuring that all the participating devices have sufficient resources to train their local models. In this connection, to ensure fast convergence of FL, a robust device selection is also proposed to help FL reach convergence swiftly, especially when the local datasets of the devices are not independent and identically distributed (non-iid). Experimental results show that (1) the proposed spectrum allocation optimization method optimizes time delay while satisfying the individual energy constraints; (2) the proposed device selection method enables FL to achieve the fastest convergence on non-iid datasets.
ESP32-driven Physical Layer Key Generation: A Low-cost, Integrated, and Portable Implementation

Physical layer key generation (PLKG) is one of the promising security solutions for communications in the Internet of Things $(\mathrm{I}\mathrm{o}\mathrm{T})$. Among implementations for the PLKG, a low-cost, integrated, and portable design is still lacking. To this end, we build a cheap and moderately complex testbed with ESP32, which supports on-chip channel estimation. In the proposed testbed, four tasks are created to achieve channel probing and message exchanging for the PLKG. Considering independent clocks and packet mismatches in the real world, we implement a PLKG prototype that generates secret keys at a rate of 19.45 bit/s. Experimental results show that the generated keys can pass all commonly-used National Institute of Standards and Technology (NIST) randomness tests, and the transmitted information cannot be decrypted by the illegitimate user.
Towards Automatic 2D Materials Detection Using YOLOv7

Two-dimensional (2D) materials have become a research focus in the field of nanotechnology. Because of their unique physical and chemical properties, these materials can be applied to various industries such as sensors, batteries, and display screens. However, identifying if optical microscope images contain 2D materials flakes is a tedious and time-consuming process. In this paper, we propose a deep learning-based object detection system using YOLOv7, to automatically search for 2D materials with few atomic layers (thickness in between 1 to 13 layers). To train the model, we have constructed Molybdenum Disulfide (MoS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) image dataset that is captured in 20x magnification and applied digital image processing and data augmentation techniques to increase the model performance. In addition, we developed a software pipeline that can be easily integrated with the optical microscope to detect 2D materials and store the result in the database. The result of the experiments shows that the trained model achieves high accuracy in detecting a few layers of MoS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> .
A domain-agnostic approach for characterization of lifelong learning systems

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.
Ontology of Musical Meaning

The article deals with the study of the phenomenon of musical meaning. What does it mean to understand music? It means to grasp some musical meaning. This meaning is purely musical. The article provides a rationale for the fact that music is not a language. Language is “transparent”: in communication, its prop­erties do not attract special attention. Music draws attention to itself. It does not describe emotions, but directly evokes them. Its conventionality is negligible. The article gives examples of musical works performed in different ways, differ­ently arranged, differently interpreted, translated from one key to another, and everywhere the question is raised, what is the musical meaning of the work. The answer is proposed: it consists in the drawing of a motif, conveying a se­quence of intervals and rhythm. A freer musical idea, which consists in a certain structure of a sequence of intervals is also considered. Based on these considera­tions, the question is raised, what is the nature of musical meaning. Music is cer­tainly an intentional subject. At the same time, the musical meaning has an ideal character, similar to the character of mathematics and logic. Actually, to under­stand the musical motive means to understand the “logic” of its development. R. Ingarden believes that a musical piece is not ideal, because it appears at a cer­tain moment. I point out that mathematical entities are also discovered at a cer­tain point in history, but they are ideal. A connection is being made with the “world three” of K. Popper. The concept of inner music is proposed. Thus, musi­cal meaning is both intentional, personal, and ideal, common to all people.
Quantum Oscillator at Temperature T and the Evolution of a Charged-Particle State in the Electric Field in the Probability Representation of Quantum Mechanics

A short review constructing the probability representation of quantum mechanics is given, and examples of the probability distributions describing the states of quantum oscillator at temperature T and the evolution of quantum states of a charged particle moving in the electric field of an electrical capacitor are considered. Explicit forms of time-dependent integrals of motion, linear in the position and momentum, are used to obtain varying probability distributions describing the evolving states of the charged particle. Entropies corresponding to the probability distributions of initial coherent states of the charged particle are discussed. The relation of the Feynman path integral to the probability representation of quantum mechanics is established.
The design of expert system to determine the university majoring based on multiple intelligence using forward chaining method

An expert system is one of artificial intelligence that uses a computer to work like and as well as humans do. An expert system is a computer system that is designed so that it has the ability and reasoning like an expert. But, the expert system is not used for replacing an expert who in this problem im a psychologist as a consultant for students want to recognize the potential and their abilities. The expert system is only used as equipment and limited tools. There is a problem is how to design tools to help students recognize their potential and abilities, so that they can choose the right department in the University based on their potential and abilities. Expert system design for assign department in the University based Multiple Intelligence use visual basic programming language and Microsoft Office Access as database.
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation

Abstract Multilingual task-oriented dialogue (ToD) facilitates access to services and information for many (communities of) speakers. Nevertheless, its potential is not fully realized, as current multilingual ToD datasets—both for modular and end-to-end modeling—suffer from severe limitations. 1) When created from scratch, they are usually small in scale and fail to cover many possible dialogue flows. 2) Translation-based ToD datasets might lack naturalness and cultural specificity in the target language. In this work, to tackle these limitations we propose a novel outline-based annotation process for multilingual ToD datasets, where domain-specific abstract schemata of dialogue are mapped into natural language outlines. These in turn guide the target language annotators in writing dialogues by providing instructions about each turn’s intents and slots. Through this process we annotate a new large-scale dataset for evaluation of multilingual and cross-lingual ToD systems. Our Cross-lingual Outline-based Dialogue dataset (cod) enables natural language understanding, dialogue state tracking, and end-to-end dialogue evaluation in 4 diverse languages: Arabic, Indonesian, Russian, and Kiswahili. Qualitative and quantitative analyses of cod versus an equivalent translation-based dataset demonstrate improvements in data quality, unlocked by the outline-based approach. Finally, we benchmark a series of state-of-the-art systems for cross-lingual ToD, setting reference scores for future work and demonstrating that cod prevents over-inflated performance, typically met with prior translation-based ToD datasets.
Grey Wolf Optimizer for Load Balancing in Cloud Computing

Task scheduling is an NP-Hard problem, so it is one of the most pressing issues in the cloud computing environment. The primary goal of task scheduling is to schedule jobs on virtual machines and to improve processing performance throughout. In this work, we used the grey wolf optimizer algorithm with changes to the fitness function to accommodate multigoals in a single fitness the makespan and load are the objectives included inside the fitness to address task scheduling difficulties. The simulation results shown that the suggested approach grey wolf optimizer outperforms the other algorithms in terms of makespan and load variation.
Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics

Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model. Besides giving the background and the details of the proposed algorithms we apply them to optimization of selected variants of the problem and discuss the results.
Research Syntheses in L2 Vocabulary Research: A Scoping Review

In this brief report, we present a scoping review of 31 second language (L2) vocabulary research syntheses published in the Web of Science from January 1990 to May 2022. The purpose of this undertaking was to understand what L2 vocabulary research synthesizers have aggregated and the investigative foci they intended to address. It was observed that most of the cataloged reports were experimental design meta-analyses, and thus there was room for future research syntheses in the vocabulary space to consider correlational research questions and hypotheses. Because systematic reviews were relatively scarce, there is also a need for these types of syntheses to be conducted in the future, especially those focused upon the observed quality of aggregated L2 vocabulary research.