Reviewerly/exHarmony huggingface dataset:
Multi-lane vehicle counting based on DCN-Mobile-YOLO model

The single object detection method cannot achieve the accurate statistics of object counts and the accuracy and speed of model detection cannot be improved simultaneously. To solve these problems, we propose a mobile-side object tracking and multi-lane vehicle counting method-DCN-Mobile-YOLO based on the YOLO v4 object detection framework. This method uses the DCNs v2 convolution kernel and MobileNet v3 framework to replace the conventional convolution kernel and backbone network of YOLO v4, respectively, and uses the DeepSORT algorithm to achieve multi-object tracking and counting. At the same time, an adaptive lane detection rule is proposed to achieve the accuracy of vehicles in the lane count. Finally, the effectiveness of DCN-Mobile-YOLO method is verified on the VOC2007+2012 data set and GoPro collected data set. The experiment results show that the mAP of DCN-Mobile-YOLO method is improved by 13.19% and 6.63% respectively compared with YOLO v4 algorithm of MobileNet v3 and CSPDarkNet as the backbone network, and the average frame rate of object detection is 12 frame/s. Our DCN-Mobile-YOLO method not only improves the detection accuracy of the object detection model but also achieves the speed of real-time detection on the mobile terminal.
Self Services And Monitoring Of Weak Heart Disease Based On The Internet Of Things And Mobile App Using Certainty Factor

Heart disease can be suffered by anyone regardless of age and gender. Heart disease can be caused by many things, such as unhealthy lifestyles (smoking), as well as heredity. Low self-awareness of someone with weak heart disease to monitor their heart health regularly. To diagnose weak heart disease, this system uses the Certainy Factor method. With this method, patients can find out the results of the decision whether the patient is at risk of developing weak heart disease or heart health conditions in normal circumstances. This method can provide comparative results (and decision results) of several parameters (tested), one of which is the heart rate parameter. Therefore, the authors build a mobile application system with the concept of service and monitoring of weak heart disease named "iHeart". With this application, patients can monitor and detect weak heart disease and use various features (features) contained in the application. One of the iHeart application facilities is the GUI Chart for heart rate monitoring. This application can also accommodate the patient's heart rate history data into a database as a data storage medium. This application system uses IoT technology, which makes it easy for users or patients and practitioners to see the results of a heartbeat to find out whether a patient has a weak heart or not, in real-time via a smartphone (smartphone) without interfering with patient mobility.
Global Solver for Low-Rank Subspace Clustering

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Fall Detection for Elderly Person Monitoring using Wearable Inertial Sensors and Locality Sensitive Hashing

Falling represents the main threat for elderly people. An early detection mechanism is crucial to assist the person and prevent further injuries from concussion to underlying pain from fractures. In this paper, we propose a new model for fall detection to improve the detection accuracy and to reduce the detection delay, while considering detection processing in the sensor to reduce the energy consumption by wireless transmission of Inertial data from sensor to processing unit. Our proposed approach is based on change detection in the slope of angular velocity, which enhances the sensitivity and drastically reduces the detection delay. After the detection of change, the local sensitivity hashing is applied to detect change in the amplitude of accelerometer signal. We applied our multimodal system on publicly available data set built for fall detection during daily activities. The proposed approach uses only data from 3D gyroscope and 3D accelerometer acquired by inertial sensor from different positions: ankle, wrist, waist, pocket, neck and forehand. Our experiment results show that our proposed model achieves high detection accuracy with low false alarms when compared to existing work in the literature. Furthermore, we compare our approach with existing work in terms of computational complexity and detection delay to prove that it reduces the energy consumption and achieve better performance than existing models.
Neural Language Models

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Learning Algorithms

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Formation of Ecological Culture of Students in the Conditions of Distance Learning

The environmental situation that has developed in Russia and around the world, the global nature of environmental problems and their peculiar manifestation in each region of the planet urgently require an early change of the thinking of humanity, individual peoples and each individual person. One of the reasons for this situation is the environmental illiteracy of the majority of the population and the inability to foresee the consequences of human intervention in nature. In this regard, environmental education as a continuous process is becoming a new priority area of pedagogical theory and practice. The importance of environmental education in modern world is beyond doubt. It is also obvious the earlier a child learns the basics of ecology, the more environmentally literate he will be, no matter what profession he will choose. Of particular interest today is the study of the impact of distance learning on the formation of environmental culture, especially during the coronavirus pandemic, which has provoked implementation of e-learning and distance learning technologies everywhere. Based on the above, the purpose of the study is to identify the impact of distance learning on the formation of environmental culture of schoolchildren. The following methods were used in the study: analysis of scientific and methodological and psychological-pedagogical literature; questionnaire (questionnaire "Studying the influence of distance learning on the formation of environmental culture of schoolchildren"); testing; mathematical processing of the data obtained.
Pronunciation Mobile Application for Oral Communication

English is a basic prerequisite for people to achieve accomplishments in various fields. In today’s age, students are well-diverse in eclectic mechanisms to enhance one’s English skills. The 21st century learners have embraced the new way of acquiring skills through the combination of internet sources and mobile applications. This research was done at the Advance Institute of Technology (AIT) to assess the efficacy of smartphone pronunciation in oral communication for 10 English. For the control and experimental classes, a quasi-experimental design was used. There were 42 students as subjects in the study. The students answered a twenty-five (25) item oral production questionnaire as a research instrument. Using the required statistical instrument, based on the result of the analysis, the control group had a very good performance on the pretest while a bigger proportion of the experimental group also performs the same while there was one who performed excellently. Hence, with today’s young learners who are digital savvy, oral literary skills are best improved when English teachers make use of the most utilized and enjoyed simulations for visual and auditory to enhance pronunciation skill development.
New Constructions for Near-Optimal Sets of Frequency-Hopping Sequences via the Gaussian Periods in Finite Fields

Frequency-hopping sequences (FHSs) have been widely applied in frequency-hopping codedivision multiple-access (FH-CDMA) systems, since they can be used for transmitting messages efficiently along with switching frequencies at set intervals by each sender.The performance of the FHSs has a great impact on the performance of FH-CDMA systems.The optimality achieving exactly the Peng-Fan bounds is an important performance measure.However, optimal sets of FHSs do not always exist for all lengths and alphabet sizes.Thus, it is meaningful to seek and design more near-optimal FHS sets whose parameters are near to achieving the Peng-Fan bounds.Let q be a power of a prime.In this paper, we present some classes of near-optimal sets of FHSs, whose parameters are ( 3(q+1) 2 , 2(q-1) 3 , 3; q) with q ≡ 1 (mod 12), ( q+1 k , k(q -1), 2; q) with even q+1 k , (2(q 2 + 1), q 2 -1 2 , 2(q + 1); q) with q ≡ 3 (mod 4), (19, 18, 4; 7), (7, 9, 3; 4) and (91, 45, 7; 16) respectively.Most importantly, these classes of near-optimal sets of FHSs have new parameters which are not covered in the foregoing literature.
Classification with Support Vector Machines

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
PKM Pelatihan Kosakata Bahasa Inggris melalui Media Audio-Visual bagi Santri di Asrama Excellent Language Organization [EXO] MAN 1 Probolinggo

English learning from conventional ways to technology-based learning is well known today. It is necessary to know the efforts to use technology against the performance of students. One of the technologies that are currently popular is audio-visual. This training activity aims to improve the vocabulary of students after learning English using audio-visual. The participants of this training are students who live in the excellent language organization [EXO] MAN 1 Probolinggo dormitory. The results of this training activity are expected to provide motivation for students in the use of audio-visual media and can increase a high sense of learning interest and improve vocabulary mastery in English.
Current Challenges

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A no reference image quality assessment method based on RepVGG

Most state-of-art deep learning based no reference image quality assessment (NR IQA) methods usually have a complex network structure which are memory-consuming, hard to train and so not applicable in practical scenarios. Aim at these problems, a RepVGG based NR-IQA algorithm with transfer learning is proposed. The method uses ImageNet dataset to pre-train RepVGG network to get network parameters, and then uses the trained network to extract image features of image quality assessment data set. Finally, a simple fully connected network is trained to get the quality score of the image based on these features. The experimental results show that on the KADID-10K, LIVE、TID2013 and CSID datasets, the overall objective assessment obtained by the method is better than the state-of-art deep learning-based methods with good consistency with the subjective assessment.
Deep Neural Network Based Reconciliation for CV-QKD

High-speed reconciliation is indispensable for supporting the continuous-variable quantum key distribution (CV-QKD) system to generate the secure key in real-time. However, the error correction process’s high complexity and low processing speed limit the reconciliation speed. Therefore, reconciliation has also become the bottleneck of system performance. In this paper, we proposed a high-speed reconciliation scheme that uses the deep neural network to optimize the decoding process of the low-density parity-check (LDPC) code. We first introduced a network structure of decoding implementation based on the deep neural network, which can be applied to decoding algorithms of parallel strategy and significantly reduce the decoding complexity. Subsequently, we proposed two improved decoding algorithms based on this structure, named linear fitting algorithm and deep neural network-assisted decoding algorithm. Finally, we introduced a high-speed reconciliation scheme based on the CPU-GPU hybrid platform. Simulation results show that the proposed reconciliation scheme reduces the complexity and enables us to realize the high-speed CV-QKD system. Furthermore, the improved decoding algorithm can also reduce the FER, thereby increasing the secret key rate.
An optimal representation of Random Maximum kSatisfiability on a Hopfield Neural Network for High order logic(k 3)

This paper proposed a new logical rule by incorporating Random maximum kSatsifiability (RANMAXkSAT) in the Hopfield neural network (HNN) as a single network model (HNN-RANMAXkSAT). The purpose is to combine the optimization capacity of the Hopfield neural network (HNN) for optimal representation to random maximum kSatsifiability (MAXRANkSAT). The energy function of a Hopfield neural network has been considered as a programming language for dynamics minimization mechanism. Several optimization and search problems associated with machine learning (ML), decision Science (DS) and artificial intelligence (AI) have been expressed on the Hopfield neural network(HNN) optimally by modelling the problem into variables to minimize the objective function that corresponds to Lyapunov energy function. The computer simulation has been developed based on RANMAXkSAT to explore the feasibility of a Hopfield neural network as a neuro-symbolic integration model in carrying out RANMAXkSAT logic programming optimally. The proposed model has been compared with the existing models published in the literature in term of Global minimum ratio (zM), Fitness energy landscapes (FEL), Root Means square error (RMSE), Mean absolute errors and computation time (CPU). Hence, based on the experimental simulation results, it revealed that the RANMAXkSAT can optimally and effectively represented in the Hopfield neural network (HNN) with 85.1 % classification accuracy.
An Image-Based Algorithm for the Automatic Detection of Loosened Bolts

The bolted joint has been widely used to connect load-bearing elements in aerospace, civil, and mechanical engineering systems. During its service life, particularly under external dynamical loads, a bolted joint may undergo self-loosening. Bolt looseness causes a reduction in its load-bearing capacity and eventually leads to the failure of a bolted joint. This paper presents an automated image-based algorithm combining the Faster R-CNN model with image processing for the quick detection of loosened bolts in a structural connection. The algorithm is validated using a lab-scale bolted joint model for which various bolt-loosening events are simulated. The imagery data of the joint is captured and passed through the algorithm for bolt looseness detection. The obtained results show that the loosened bolts in the joint were well-detected and that their loosening degrees were precisely quantified; therefore, the image-based algorithm is promising for real-time structural health monitoring of realistic bolted joints.
Is There Any Witching in the Cryptocurrency Market?

This paper explores price effects caused by the expiration of derivatives in the cryptocurrency market. Applying different statistical tests (ANOVA, Mann–Whitney, and t-tests) and econometric methods (the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach) to daily and weekly Bitcoin data over the period 2018–2021, the following hypotheses are tested: (H1) Expiration days create patterns in price behavior in the cryptocurrency market; and (H2) Price patterns can be exploited to generate abnormal profits from trading. The results suggest that expiration effects are only nominally present in the cryptocurrency market. There are differences in returns between expiration-related periods and average returns, but these differences are statistically insignificant. The only case in which an anomaly was detected was related to abnormally high returns during the week of expiration: returns during such weeks were positive in 65% of cases, and were on average 5 times higher than during usual weeks. Trading strategies based on this fact were able to generate results different from those of random trading, with a Sharpe ratio above 1. This is evidence in favor of the existence of a real price anomaly, which contradicts the efficient market hypothesis, and this could be implemented in the practice of traders and investors by creating trading strategies based on detected price effects or special technical analysis indicators to generate trading signals. For academics, these results might provide an opportunity to improve time series forecasting analysis in the case of Bitcoin.
Exploring the concept of the digital educator during COVID-19

In this paper, we explore academic identity, specifically the identity of the educator in higher education and academics’ conceptualisations of the digital educator. We suggest that the concept of a digital educator is not only about technology, tools and uses. The context for this exploration is academics’ participation in an online professional development module, Digital Education, and the “pivot online” (Weller, 2020a) during campus closures caused by the COVID-19 pandemic in 2020. Through qualitative research, we explored participants’ sense of teaching identity, whether they had or have a concept of being a digital educator and the extent to which these identities might have shifted while the campus closure continued. We present analysis of their accounts and reflect on the implications of this analysis, particularly in relation to organisational digital capacity defined as “the skills, competencies, attitudes, infrastructure, and resources that enable people to work, live and learn in a world that is increasingly digital world” (National Forum, 2018, p. iv). We consider how higher education institutions will cope with the complex challenges facing us and suggest ways in which the implications of this research could better enable institutions to navigate change and build organisational digital capacity. Implications for practice or policy: The pivot to online teaching and assessment during the COVID-19 pandemic has had complex effects on professional identities which need to be researched and understood. Programme teams in campus-based institutions have experienced erosion of professional norms and relationships. They need support and leadership during the gradual return to campus. Faculty integrated technologies rapidly but unevenly into practice; therefore, the ongoing building of digital capacity and the shift towards post-digital pedagogies needs dedicated support and leadership.
A Blockchain based Scheme for Improved Availability and Security

The usage of information technology in industry brings a rapid growth in industry. The intensive use of information services across the network leads to software complexity and malicious attacks in the network. The data that are being monitored by the service providers are highly vulnerable to get compromised. The blockchain technology is used to increase the complexity of the stored data. It guarantees information security, obligation, and data consistency. The stored data can be replicated to increase the availability, enhance the security. The blockchain is used to replicate the data which can build up trust between the nodes in the system, expel the obstruction from the mediator during the time spent esteem exchange. This article surveys how the blockchain based approaches play a vital role in security services which include authenticity, integrity, confidentiality, privacy as well as data storage provenance. Therefore, the blockchain technology safe guards the data that prone to attacks. The objective of this article provides show the blockchain innovation can resolve the difficulties which associates with security, resources. Furthermore, it discussed more related article that deals with blockchain technology implemented in various platforms.
Machine-Learning Methods for Complex Flows

We are delighted to introduce this Special Issue focused on novel machine-learning (ML) methods aimed at predicting, modeling, and controlling a variety of complex fluid flow scenarios [...]
Ultra Tightly Coupled Position and Attitude Determination with Mitigation of Temporally Correlated Signals using an Antenna-Array

A conventional Phase-Locked-Loop (PLL) is not able to distinguish between temporally correlated signals. Instead, the loop will track the sum of those signals. This is obstructive for position estimation based on carrier-phase measurements. In this case a multipath or spoofing signal will cause so called cycle-slips. Promising methods to distinguish between temporally correlated signals are based on the spatial domain. The recent proposed method to estimate the attitude and position in presence of spoofing and jamming signals using an antenna-array will be extended in such a way that the estimation will be tightly integrated into the tracking loop. Since the proposed method is able to distinguish between authentic and spoofing signals based on their spatial information, the carrier phase of the authentic signal can be tracked exclusively, whereas the conventional PLL would have tracked the summation of both signals. Cycle-slips due to temporally signals are, therefore, less likely.
GloMPO (Globally Managed Parallel Optimization): a tool for expensive, black-box optimizations, application to ReaxFF reparameterizations

Abstract In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager.