Reviewerly/exHarmony huggingface dataset:
Design and analysis of wide-band transmission spheres for multiwavelength interferometers

A design of high-precision 4-in. transmission spheres that can work in wide-band interferometric measurement is proposed. A motorized precision rotation mount is used to drive one of the lenses in the design to make the transmission sphere work at any wavelength in the wavelength range of 532 to 1550 nm, where the F-number of the transmission sphere remains constant during the movement of the lens. OpticStudio software is utilized to verify the design under F-numbers of 1.0, 2.2, and 3.3, and the results show that a transmission sphere with an F-number of 1.0 to 3.3 can be designed based on our model. The tolerance analysis and the secondary adjustment results show that the peak-to-valley transmission wavefront error of the transmission sphere can be controlled within λ / 20 in actual machining and assembly, which meets the requirement of high-precision interferometric measurement.
Imprecise Oracles Impose Limits to Predictability in Supervised Learning (Extended Abstract)

Supervised learning operates on the premise that labels unambiguously represent ground truth. This premise is reasonable in domains wherein a high degree of consensus is easily possible for any given data record, e.g. in agreeing on whether an image contains an elephant or not. However, there are several domains wherein people disagree with each other on the appropriate label to assign to a record, e.g. whether a tweet is toxic. We argue that data labeling must be understood as a process with some degree of domain-dependent noise and that any claims of predictive prowess must be sensitive to the degree of this noise. We present a method for quantifying labeling noise in a particular domain wherein people are seen to disagree with their own past selves on the appropriate label to assign to a record: choices under prospect uncertainty. Our results indicate that `state-of-the-art' choice models of decisions from description, by failing to consider the intrinsic variability of human choice behavior, find themselves in the odd position of predicting humans' choices better than the same humans' own previous choices for the same problem. We conclude with observations on how the predicament we empirically demonstrate in our work could be handled in the practice of supervised learning.
Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot

We propose a single sensor-based activity classification method where the Poincare plot was introduced to analyze the variance of the angle between acceleration vector with gravity calculated from the raw accelerometer data for human activity classification. Two datasets named ‘Human Activity Recognition’ and ‘MHealth dataset’ were used to develop the model to classify activity from low to vigorous intensity activities and posture estimation. Short-term and long-term variability analyzing the property of the Poincare plot was used to classify activities according to the vibrational intensity of body movement. Commercially available Actigraph’s activity classification metric ‘count’ resembled value was used to compare the feasibility of the proposed classification algorithm. In the case of the HAR dataset, laying, sitting, standing, and walking activities were classified. Poincare plot parameters SD1, SD2, and SDRR of angle in the case of angle variance analysis and the mean count of X-, Y-, and Z-axis were fitted to a support vector machine (SVM) classifier individually and jointly. The variance- and count-based methods have 100% accuracy in the static–dynamic classification. Laying activity classification has 100% accuracy from other static conditions in the proposed method, whereas the count-based method has 98.08% accuracy with 10-fold cross-validation. In the sitting–standing classification, the proposed angle-based algorithm shows 88% accuracy, whereas the count-based approach has 58% accuracy with a support vector machine classifier with 10-fold cross-validation. In the classification of the variants of dynamic activities with the MHealth dataset, the accuracy for angle variance-based and count-based methods is 100%, in both cases, for fivefold cross validation with SVM classifiers.
THE EFFECT OF FLOW OF FUNDS UNCERTAINTY ON FINANCIAL DEVELOPMENT IN SOME SELECTED AFRICAN COUNTRIES

The purpose of this paper is to analyze the effects of financial globalisation uncertainty on financial development, included in the model are foreign direct investment and economic growth as control variables to avoid the misspecification of variables.This paper analyzed the effects based on the panel data analysis using the Westerlund cointegration method, Pooled Mean Group estimator and Dumitrescu and Hurlin causality test of eight top African economies, from 1970-2018.The result of the co-integration showed a long-run relationship between the variables with and without structural breaks.Also, there exists a bidirectional causality relationship between financial development and financial globalisation uncertainty.
Fuzzy membership functions tuning for speed controller of induction motor drive: performance improvement

&lt;p&gt;Fuzzy &lt;span&gt;logic controller (FLC) has gained high interest in the field of speed control of machine drives in both academic and industrial communities. This is due to the features of FLC of handling non-linearity and variations. FLC system consists of three main elements: scaling factors (SFs), membership functions (MFs), and rule-base. Fuzzy MFs can be designed with different types and sizes. For induction motor (IM) speed control, (3x3), (5x5) and (7x7) MFs are the most used MFs sizes, and normally designed based on symmetrical distribution. However, changing the width and peak position of MFs design enhance the performance. In this paper, tuning of MFs of FLC speed control of IM drives is considered. Considering (3x3), (5x5) and (7x7) MFs sizes, the widths and peak positions of these MFs are asymmetrically distributed to improve the performance of IM drive. Based on these MFs sizes, the widths and peak positions are moved toward the origin (zero), negative and positive side that produces a controller less sensitive to the small error variations. Based on simulation and performance evaluations, improvement of 5% in settling time (Ts), 0.5% in rise time and 20% of steady-state improvement achieved with the tuned MFs compared to original &lt;/span&gt;MFs.&lt;/p&gt;
Path Planning of Underwater Vehicles Based on Improved Whale Optimization Algorithm

In order to better solve path planning problems of underwater vehicles on complicated seabed terrains under ocean currents, a global path planning method based on whale optimization algorithm (WOA) is proposed. We focus on the fixed-depth navigation, and the underwater environment is modeled based on the MAKLINK graph. Three criteria of the length, smoothness and energy cost are used to evaluate a path. Considering that the original WOA algorithm lacks of balance in the exploration and exploitation at different positions in the search space and is easy to fall into local optima, the improved whale optimization algorithm (IWOA) is presented. The proposed algorithm creates an adaptive elite set by selecting best individuals from the population, and the members of the set will be randomly selected to be combined into new individuals to guide the searching process of the proposed algorithm. The corresponding guidance strategy is that the search agents update the position directionally based on the locations and orientations of the new individuals, replacing the encircling update centered on the position of prey in the original algorithm. Dijkstra algorithm is utilized for “coarse-grained planning” of path planning, and an approach to augment the waypoints is developed to cooperate with the proposed IWOA to complete the “fine-grained planning”. Above two-step planning forms the global path planning method proposed in this paper. The simulation results show that the proposed method is feasible and effective through testing under different circumstances. Compared with original WOA, paths planned by proposed method and IWOA have higher qualities, with shorter lengths, smoother paths and more energy saving.
Research Issues of Information Security Using Blockchain Technique in Multiple Media WSNs

This chapter covers the bibliometric review of papers that has been carried out and some interesting applications and advantages of Blockchain-based WSN that has been reported in the literature. The Blockchain architecture, characteristics, consensus algorithms, cryptocurrency, smart contracts, and use cases are systematically discussed. Classification, advantages and disadvantages of Blockchain are also reported in the literature. The cryptographic principles of Blockchain technology such as SHA, ECC, digital signature, and Merkle tree are included. The fundamental motivation behind this segment is to tell individuals about the basics of Blockchain technology. It introduces the idea of security threats in WSN and how communication links, nodes, and data can be secured with the help of Blockchain. This chapter describes the list of projects and funding agencies worldwide and shows how developing countries are implementing Blockchain-based services.
SIAM-REID: confuser-aware Siamese tracker with re-identification feature

Siamese deep-network trackers have received significant attention in recent years due to their real-time speed and state-of-the-art performance. However, Siamese trackers suffer from similar looking confusers, that are prevalent in aerial imagery and create challenging conditions due to prolonged occlusions where the tracker object re-appears under different pose and illumination. Our work proposes SiamReID, a novel re-identification framework for Siamese trackers, that incorporates confuser rejection during prolonged occlusions and is wellsuited for aerial tracking. The re-identification feature is trained using both triplet loss and a class balanced loss. Our approach achieves state-of-the-art performance in the UAVDT single object tracking benchmark.
Sign Language Detection using Deep Learning

In this paper we put forward a system that bridges the gap between people who use Sign Language tocommunicate and the computer. Sign Language Recognition systems, though much crucial, suffer the lack of implementationin our common day to day devices. This paper focuses on various techniques and tools to help resolve this gap using DeepLearning. Here we propose a system that recognizes sign language and predicts the right sign using a web camera. Thesystem uses Deep learning techniques, Convolution neural networks, max pooling and ReLU activation function. We aim tocreate a software which is both affordable, much more accessible to the users and works without compromising with thedesired results.
Computing Techniques for Securing Healthcare Data with Blockchain Technology

Today the world is facing severe problems due to COVID-19. World Health Organization (WHO) declared COVID-19 as a pandemic. Early diagnosis and security of patient’s data are of utmost importance for all of us. Because electronic health records are available in hospitals and due to the COVID situation, all researchers and hospitals are sharing their diagnosis and treatment based data. If we understand the disease better, we can soon find the remedy. Therefore, there is a need to protect all the data from breaching; hence, cloud computing is introduced in this chapter because it provides solutions against all kinds of threats. Its services Data as a Service (DAAS), Software as a Service (SAAS), Platform as a Service (PAAS), and Infrastructure as a Service (IAAS) allow users to protect data at each level. These services allow storing and analyzing patient’s data in a secure manner. Machine learning–based techniques are allowed to diagnose patients with low, medium, and high risks of COVID. Simultaneously all the record is recorded, and it is recommended that what treatment is required for patient and then patients will move to healthcare provider with its secure ID and all the details. These can be tracked, and patient health monitoring is done by the cloud system having blockchain. In this chapter, security-enhanced machine learning blockchain (SeMB) model is developed and its application in healthcare is discussed with pros and cons. This model will help in maintaining secure electronic health care data in hospital.
Psychophysiological changes during workspace virtualization

Aim. To estimate psychophysiological changes during workspace virtualization. Materials and Methods . We evaluated the psychophysiological profile of 10 healthy right-handed males aged 25 to 45 years before, during and after the working in a virtual reality (VR) headset. All participants had higher education, normal or corrected to normal vision, and were experienced computer users. Psychometric testing included a neurological examination, assessment of functional and feedback-related brain activity (reaction time, errors, and missed signals) and attention span, quantification of processed symbols in the 1st and 4th minutes of Bourdon test, analysis of short-term memory (10 words, 10 numbers and 10 meaningless syllables memorization) and spatial perception, and multi-channel electroencephalography recording in rest. Results . Deterioration of psychometric indicators after a cognitive load in a VR headset was documented only in the most difficult tasks: the number of errors increased by 93% in the brain performance test and by 65% in the attention distribution test. The analysis of electroencephalography data showed that the delta rhythm and theta1 rhythm activity decreased by 28 and 13%, respectively, after working in a VR headset as compared to baseline values, while alpha1 rhythm activity increased by 96%. Probably, the observed electroencephalography changes corresponded to the patterns of brain activation associated with cognitive load and the resulting fatigue. Conclusions . We developed a suitable approach for the psychometric testing before and after working in VR headset, which demonstrated general tolerance and acceptable subjective difficulties to VR load.
DOM and DSM in Turkish

Current formal studies of Case center on one or both of two main approaches: Case assignment via Agree or dependent case, whereby the case features of a given NP/DP are valued in a particular structural configuration within a particular local domain, unless another NP/DP is assigned Case first (Marantz 1991, Baker & Vinokurova 2010). Some have suggested a mixed approach: Case licensing via Agree for subjects, but via dependent case for direct objects (DOs), for example Baker & Vinokurova (2010). This paper proposes a novel conciliatory approach to Case. In all instances of structural Case, i.e. for subjects as well as direct objects, Agree with a functional head is necessary. This addresses the conceptual issue of one unique type of licensing mechanism, rather than two. However, the functional licenser of the Case on DOs has to be itself licensed in this capacity. This is possible only when the subject has first been licensed via Agree. This results in the appearance of dependent case, which is an indirect symptom of dependent Agree. This approach is then extended to structural datives.
Person Property Estimation Based on 2D LiDAR Data Using Deep Neural Network

Video-based estimation plays a very significant role in person identification and tracking. The emergence of new technology and increased computational capabilities make the system robust and accurate day by day. Different RGB and dense cameras are used in these applications over time. Video-based analyses are offensive, and individual identity is leaked. As an alternative to visual capturing, now LiDAR shows its credentials with utmost accuracy. Besides privacy issues but critical natural circumstances also can be addressed with LiDAR sensing. Some susceptible scenarios like heavy fog and smoke in the environment are downward performed with typical visual estimation. In this study, we figured out a way of estimating a person's property, i.e., height and age, etc., based on LiDAR data. We placed different 2D LiDARs in ankle levels and captured persons' movements. These distance data are being processed as motion history images. We used deep neural architecture for estimating the properties of a person and achieved significant accuracies. This 2D LiDAR-based estimation can be a new pathway for critical reasoning and circumstances. Furthermore, computational cost and accuracies are very influential over traditional approaches.
In depth: The languages in cyberspace

By the end of the century, around half of the approximately 7,000 languages spoken in the world today could disappear, according to the 2021 UNESCO World Report of Languages: Towards a Global Assessment Framework for Linguistic Diversity, released in May.
Students’ grammatical error analysis in the recount text translation

This research aims to determine the types of grammatical errors that students make the most and least frequently, as well as the causes of those errors, using the Surface Strategy Taxonomy that was identified in students' recount text translation. The participants in this study were SMAN 1 Pringsewu first-graders in the academic year 2020/2021. This research adopted a descriptive qualitative method by using a translation task and questionnaire for the instruments. According to the Surface Strategy Taxonomy, the students made all types of errors: omission, addition, misformation, and misordering. In recount text translation based on Surface Strategy Taxonomy, the most common error made by students was misformation, whereas the least frequent error type was misordering. Problems with the target language, mother-tongue interference, a lack of eagerness to learn the target language, a lack of confidence, and the absence of error feedback from the teacher were all possible causes of those errors. This indicates that the students’ English grammar understanding was still limited. Therefore, the English teacher is expected to solve it by providing appropriate teaching methods and materials to reduce errors.
Time in digital history

The article is devoted to the problem of representation of the image of time in digital history. The author understands digital history as a scientific direction that studies the history of digital society, as well as the history of the creation and application of information and computer technologies, their impact on the history of a man and a human society within the framework of epistemic arenas. Epistemic arenas bring together human scientists, the masses of Internet users, digital technologies and media. The category of time in digital history has a social significance, since historical knowledge in the digital age is a collective creativity based on the application of technology. The author considers the concepts of time in digital history within the framework of the concept of social time, bearing in mind that the sequence of events that saturate certain periods of time can be both real and virtual. The virtualization of history provides the individual with the opportunity to “dive” into the past and creates the illusion of time management. Time in digital history can be accelerated, «explosive», or, conversely, slowed down, which is subordinate to the interests of users who create or consume historical information. Time management allows for more effective consideration and representation of historical processes and leads to the adoption of the concept of movement in it. The idea of travelling to the past and future has become a part of the historical culture of the digital society. The future is presented as the end of history, the apocalypse, which explains the increased interest of society in the past, which in digital history has the character of a known, and therefore safe, reality.
Performance Improvement of Delay-Based Photonic Reservoir Computing

The techniques for performance improvement of delay-based reservoir computing with photonic systems are proposed and summarized. A chaos input mask signal is introduced to improve the performance of a time-series prediction task. A photonic integrated circuit is used to miniaturize the reservoir computing system. The performance of reservoir computing is compared between a single electro-optic system and a mutually coupled electro-optic system.
Radar Emitter Signal Recognition based on Contour Lines of Ambiguity Function and Stacked Denoising Auto-encoders

Aiming at many problems of complex radar emitter signal recognition methods, like poor anti-noise performance and low recognition rate. In this paper, a new recognition method based on contour lines of ambiguity function and stacked denoising auto-encoders is proposed. First, an ambiguity function is processed by the Gaussian smoothing and calculate contour lines by linear interpolation. Then, principal component analysis is used to reduce its feature dimension, retain the main ambiguity energy information. Finally, deep learning stacked denoising auto-encoders are built to learn and extract the deep and more ubiquitous features of contour lines, and classify them through the Softmax classifier. The simulated experiments show that the overall average recognition rate of the six typical radar signals is maintained above 99.83% when the signal-noise ratio is 0dB. Even in the -6dB environment, it also can reach 83.67%, which proves this method has good performance and feasibility under the extremely low signal-noise ratio conditions.
An Efficient Health Monitoring Method Using Fuzzy Inference System via Cloud

Background: In the midst of invention of sensor mechanization, massive statistics analytics had turn out to be an innovative prototype for quick information processing.Real-time analytics are required for getting access to the information and its processing.Large populace interest in fitness care led to the improvement of health monitoring systems.Methods: This study targets toward the improvement of a cloud based fitness health care system.Here Wireless Body Area Networks (WBAN) is used to blend the information to the server that includes STORM.The analytics are performed on the received physiological data.The non-critical information is ousted and the essential facts are saved and a notification is sent to the medical doctor.Findings: Thus, actual time analytics alongside with the cloud aid to improve the effectiveness of the proposed healthcare device by providing immediate clinical assistance to the users.A sensible selection making device for approximate reasoning that can manage the uncertainty of imperative threat for human fitness with the usage of fuzzy logic is carried out for discovering the health related problems.Novelty: In this study, an analysis to monitor the fitness hazard which is associated to Blood Pressure, Pulse rate and Kidney function is proposed.Levels of blood stress are analyzed with kidney characteristic by using Glomerular Filtration Rate (GFR).Under this concept, fuzzy logic system is proposed to signify the parameters which can also reason the danger for human fitness and evaluation via the usage of rule base component.