Reviewerly/exHarmony huggingface dataset:
Multi-surface phase separation based on wavelength-tuning phase-shifting interferometry

Wavelength-tuning phase-shifting interferometry is a modern optical measurement method based on the propagation characteristics of light, which can determine sample topography by wavelength-level non-contact method with high-precision. The accuracy of this method can be up to nanometer and sub-nanometer precision. The measurement of transparent parallel plates is important and significant in optical measurement. Based on the design of window and sampling functions, the introduced 36 steps phase-shifting algorithm can effectively extract the target information. The influences of constant and first-order phase-shifting errors are analyzed. The least square separation method based on 19 steps iteration can determine the surface phase information accurately and separate surfaces including front, rear, thickness information, even the parasitic term. The influence of different iteration steps for the PV and RMS values is considered, which provides the basis and reference for the design of the separation algorithm.
Smart Lexicography for Low-Resource Languages: Lessons Learned from Buddhist Sanskrit and Classical Tibetan

Traditional lexicography requires titanic efforts and enormous resources. For many languages, such resources have never been available. As a result, they have received only limited lexicographic coverage. Today, these languages can take advantage of many of the same digital tools and strategies that have simplified and expedited dictionary-making for mainstream languages. However, the resource gap remains evident even in the digital era, with basic corpus processing tasks that lie at the foundation of contemporary ‘smart lexicography’ still constituting a challenge for many under-resourced languages. Drawing on my own experience in Sanskrit and Tibetan lexicography, this paper aims to offer some guidance as to the advantages and limitations of the application of smart lexicography to under-resourced languages. In particular, this paper suggests that in order to optimize resources, it may be advisable to prioritize high-quality lexical annotation of the corpus over highly curated dictionary entries, and to let digital tools take care of the lexicographic representation of the annotated linguistic information.
The impact of social media on consumer awareness towards buying copy brand

This study was based in Jordan to investigate the benefits that the social media offers and to explore how consumers access, process, and use the information of the social media, especially Facebook and YouTube. The paper used a quantitative method and an SEM-PLS technique for validating the findings and testing the theory's constructs. Notably, the research was based on a behavioural model known as the theory of reasoned action. The main outcomes show that attitude and subjective norm have positive significant relationships with the consumer's intention to use the social media search for information about a given brand. The search creates brand awareness, which also significantly correlates to the intention to buy. In sum, the social media has enhanced socialisation and social e-commerce.
Dynamic 3D shape measurement based on the phase-shifting moire algorithm

Structured-lighting projection methods are the important parts of the optical three-dimensional (3D) measurement. Phase-shifting profilometry has a higher accuracy,however it requires multiple phase-shifting sinusoidal patterns’ projection,it can only be used for static measurement. The 3D shape measurement of dynamic objects is a challenging issue and attracts many scholars’ attention. The single frame 3D reconstruction technique (such as the Fourier transform, color-encoded or composite coded grating method and single frame Moiré retrieval method) can meet the requirements of dynamic measurement well since only one-frame deformed pattern is required to obtain the 3D information of the object, but there are still issues in the stability and accuracy when using these methods. Recently, Wang et. al. [26] presented a high-speed Moiré-based phase retrieval method. However, it is used only to measure the thin objects. Inspired by reference 27, we combined phase-shifting, moire algorithm and reconstruction algorithm of complex Fast Fourier Transform (FFT), proposed a dynamic three-dimensional (3D) measurement based on four-step phase-shifting Moiré algorithm. Only one fringe pattern of the object was required to reconstruct the 3D shape of the tested object after the four fringe patterns with a &pi; /2 phase shift of the reference plane were captured in advance. Only a single Fourier transform of a complex fringe composed of two multiplexed fringe patterns is calculated,the calculation time of the inverse 2D FFT is decreased due to the smaller calculated data matrix. First, four sinusoidal fringe patterns with a &pi;/2 phase-shift are projected on the reference plane and acquired four deformed fringe patterns of the reference plane. Then single-shot deformed fringe pattern of the tested object is captured in measurement process. Four Moiré fringe patterns can be obtained by numerical multiplication between the the AC component of the object pattern and the alternating components(AC) of the reference patterns respectively. The four low-frequency components corresponding to the Moiré fringe patterns are calculated by the complex encoding FT (Fourier transform) ,spectrum filtering and inverse FT. Thus the four phase-shifting Moiré fringe patterns can be retrieved. Then the wrapped phase of the object can be determined in the tangent form from the four phase shifting Moiré fringe patterns using the four-step phase shifting algorithm.The continuous phase distribution can be obtained by the phase unwrapping algorithm.The 3D shape distribution can be reconstructed according to the phase-to height mapping relation after the calibration of the system. Finally, experiments are conducted to prove the validity of the proposed method. The results demonstrate that our method not only can expand the measurement scope, but also can improve accuracy and speed.
Feature Selection Using Submodular Approach forFinancial Big Data

As the world is moving towards digitization, data is generated from various sources at a faster rate. It is getting humungous and is termed as big data. The financial sector is one domain which needs to leverage the big data being generated to identify financial risks, fraudulent activities, and so on. The design of predictive models for such financial big data is imperative for maintaining the health of the country’s economics. Financial data has many features such as transaction history, repayment data, purchase data, investment data, and so on. The mainproblem in predictive algorithm is finding the right subset of representative features from which the predictive model can be constructed for a particular task. This paper proposes a correlation-based method using submodular optimization for selecting the optimum number of features and thereby, reducing the dimensionsof the data for faster and better prediction. The important proposition is that the optimal feature subset should contain features having high correlation with the class label, but should not correlate with each other in the subset. Experiments are conducted to understand the effect of the various subsets on different classificationalgorithms for loan data. The IBM Bluemix Big Data platform is used for experimentation along with the Sparknotebook. The results indicate that the proposed approach achieves considerable accuracy with optimal subsetsin significantly less execution time. The algorithm is also compared with the existing feature selection andextraction algorithms.
Spatial and temporal bounds on quantum effects for micro-organisms

Progress in the experimental nano-sciences and laser cooling has led to the manipulation of material objects with sizes and temperatures such that quantum effects start to appear. This range of siz ...
Road identification system based on CNN

Abstract In recent years, convolutional neural network (CNN) has been widely used in image recognition, but there is still a big gap in automatic driving and road recognition. Here we have designed a system that maps raw pixels directly from a single front-facing camera to a steering command. The system mainly uses the camera to recognize the auxiliary lines on the road to realize automatic driving, and the accuracy rate has reached 98%. Unfortunately, such data can only be used in the laboratory, and the recognition of complex road conditions needs further development of machine learning.
A Holistic View of the Server Consolidation and Virtual Machines Placement Problems

Alongside the fourth industrial revolution and many other emerging technologies and markets, IT sectors are obligated to achieve a much higher level of efficiency in managing IT infrastructure through automation. Recent studies reported various statistical analysis that suggests tremendous growth in the energy consumption of data center operation. Accordingly, the CO2 emission footprint of data centers has reported a troubling increase in the past ten years, which is estimated to be the fastest growing CO2 footprint among different IT sectors. Therefore, in this research, we will present a holistic view of current technologies and solution strategies targeting sustainable virtual machines placement in virtualized data centers. Based on the presented literature analysis, we studied the advent and impact of virtualization strategies, the live migration algorithms, cloud computing model, and machine learning approaches on the management of data center for reducing energy consumption. Our findings suggest a steady increase in the complexity of the problem formulation with the advent of new technologies and a similar increase of the possible achievable optimization potential.
Deep Learning Based Language Identification System From Speech

The paper outlines a language identification system from speech with the help of deep learning. In a multilingual country like India, language identification systems have a wide variety of applications. For this, a custom speech database of seven Indian languages from IIIT-Indic has been taken. Preprocessing is done in order to avoid the noise interference and the disturbances in the audio signal. After subsequent framing and windowing, the spectrograms are extracted and fed to a Convolutional Neural Network (CNN). Utterances in languages: Bengali, Hindi, Marathi, Malayalam, Kannada, Tamil and Telugu are utilized in this study. A mixture of speech utterances from binary combination of these languages is used in this analysis. The performance of the system is measured in the form of accuracy, precision, recall, F1 score and confusion matrix. For the data set analyzed, a maximum accuracy of 99.5% is achieved for Bengali vs Kannada and 99% for Kannada vs Malayalam.
Public support for Live Facial Recognition and implications for COVID-19 policing

Julia Yesberg, Jonathan Jackson, and Ben Bradford discuss a London-based study exploring public responses to Live Facial Recognition – a technology that enables the police to carry out real-time automated identity checks in public spaces. They find that people accepted this technology if they generally trusted the police to ‘do the right thing’.
Cloud computing: peer to peer keyword search on cloud

Cloud computing is come forth computing paradigm in which resources of the computing infrastructure are provided as services over the Internet. This paradigm also brings forth many new challenges for data security and access control when users outsource nociceptive data for sharing on cloud servers, which are not within the same countenanced domain as data owners. Although in the traditional way, encryption scheme to exploit edit distance to quantify keywords similarity and develop an advanced technique on constructing discrete keyword sets, which greatly reduces the storage and representation overheads. In this paper for the first time we formalize and solve the problem of sensitive data shared and stored by third-party sites on the internet. The attack correlation center, dshield.org, presents aggregated views of attacks on the Internet, but stores intrusion reports individually submitted by users. Given the variety, amount, and importance of information stored at these sites, there is cause for concern that personal data will be compromised. This worry is escalated by the surge in recent attacks and legal pressure faced by such services. In this paper, we introduce new techniques to implement fine grained access control. In our techniques, the data is stored on the server in an encrypted form while different users are still allowed to decrypt different pieces of data per the security policy. This effectively eliminates the need to rely on the storage server for preventing unauthorized data access.
Planter Pressure Information Acquisition System Based on STM32

Abstract This paper introduces a plantar pressure detection and analysis system based on flexible membrane pressure sensor. The system collects and analyzes the data through the wireless Bluetooth module, and transmits the data and results to the upper computer.The performance of the system was tested and analyzed through the upper computer platform written in C ++.The results showed no significant jumps and mutations.
The Role of Knowledge Management Practices on Innovation Performance in Public Telecommunication Company

The aim of this research is to identify the role of knowledge management practice in enhancing the innovation capability of an organisation. The study was conducted in a national public telecommunication company using 100 participants in Jakarta. Multiple regressions were used to analyse whether knowledge acquisition, knowledge sharing, and knowledge application can contribute to the organisation's innovativeness. The results showed that partially, only knowledge sharing and application had significant and positive relation to innovation capability. While simultaneously, knowledge management practices in the company could impact the innovation capability. The insights from the study were that knowledge management practices should be systematically implemented in the organisation. The sharing and application of important knowledge throughout the organisation would help increase the innovative thinking of the employees, hence indirectly affect the innovation capability of the organisation.
Shot Detection in Racket Sport Video at the Frame Level Using A Recurrent Neural Network

In recent years, there has been a demand in the sports industry to reduce the burden of data collection and video editing for tactical analysis. To achieve these, a system that can recognize the game context is needed. In this study, we proposed a method to identify the player's shot timing at the frame level during a ball-striking sport. In this study, players' shots were detected in video of a tennis match. It was shown that shots could be detected with an F-score value of 87% or more within an error range of 1 frame (0.033 sec) by considering time-series information using a recurrent neural network. This technology is expected to be applied not only to tennis, but also to other sports that involve ball shots, such as table tennis, baseball, and volleyball. At the same time, it can be used to detect moments of a specific action (for example, touching or hitting an object).
A Reversible Information Hiding Scheme Based on Index Encryption Strategy in VQ-Image

In 2017, Yan et al. proposed a method of image cryptosystem based on VQ and cross chaotic map. In their method, they applied index table to swap the index with randomization as the method of image cryptosystem. Swapping the index can achieve the effect of image encryption, but it could be suffered from lower security in image while the statistical analysis is used. This paper improves the Yan et al. scheme and proposes a novel image cryptosystem based on encryption of index-table and codebook. Our scheme uses two methods, confusion on index table and diffusion on codebook, to encrypt the images and make the encrypted image more security. Experimental result shows that the proposed scheme can efficient prevent the attack of histogram analysis and pay fewer effort on preprocessing. The proposed scheme not only has more security against statistical analysis but makes our scheme more flexibility on information hiding.
Overview of Deep Learning in Facial Recognition

In recent years a wide range of techniques have been developed to achieve accurate automated facial recognition. Ever since the success of the AlexNet neural network model based on convolutional neural networks (CNN) in the ImageNet competition in 2012, algorithms for object detection and recognition based on the so-called deep learning have attained significant improvements in performance. This success has inspired the implementation of similar models in facial recognition, resulting in vastly improved performance. As a result, recent research has for the most part been based on this paradigm. This chapter presents an overview of currently available deep neural network models in facial recognition. We outline the architectures and methods used by the best current models, and discuss performance issues related to the loss function, the optimization method and the choice of training dataset.
A systematic approach to the phased implementation of psychological support for professional activities of specialists working with the family

The article provides a theoretical analysis of the possibility of using a systematic approach in the phased implementation of psychological support for professional activities of specialists working with the family. The following components are defined: professional recruitment of family specialists, in accordance with the requirements of the professional standard; adaptation to the profession (professional adaptation), in the long professional path of the specialist; professional certification that confirms professionalism, occupation of the position of a specialist in family work, a certain level of motivation and stress; professional training, which includes training in educational programs of higher education and continuing education throughout the entire period of professional activity; dynamic monitoring-control of professional development of specialists in family work; General comprehensive assessment (feedback). The proposed model of a system of step-by-step psychological support of professional activities of specialists working with the family as a certain subordinate set of hierarchically interconnected structural components, implemented in practice in the dynamics of professionalization, is a developed systematic approach, which allows the use of tools and methods that can provide a certain level of success in performing professional activities
Game design and the Semantic CAT Method for journalism

Future semantic learning systems for journalism should aim to integrate the values of the domain by using an ontological approach and a participatory design method like the Semantic CAT Method (Dowd). This method draws on game design and contextual approaches, as well as language structures. A focus on language and game design methods can work for semantic ends as well as modelling game-play. Ambiguity in design is also informative, but an ontology approach sorts language ambiguities, such as the same word with different connotations for journalism, social media, and public relations. It also helps to reveal domain characteristics that put journalism in a new light. The Trigger documents in the CAT Method include a focus on potential data and are scaffolded in participatory workshops. They include tasks for drawing and labelling typical scenarios in journalism, as well as UML diagrams for logic in processes, producing good results for an ontological base for journalism.
Emotions, behaviours, and context-aware systems for journalism

New system ideas drawing on Affective Computing for journalism training include synthetic players with behaviour attributes, conceptualised as sub-systems and integrated with emotion engines, but they need to be designed from participatory data. System models could also look to advances in EmotionML (mark-up), but elicitation and articulation of emotions contextualised to professional interactions is the first step. Trigger documents from the Semantic CAT Method are used for these ends. Emotion-based vocabulary can also be integrated into logic models to consider machine reasoning for future journalism systems, but the conceptual models of Finite State Machines (FSMs) for Robo-Journo and Promo-Robo include health and emotion attributes assigned to transitions between states in professional processes. These automated systems are for verifying a story and treatment of press releases in the hands of journalists. Future context-aware systems can also include sensors and wearable systems, with potential to protect journalists reporting in conflict zones.
Probability and Decision Theory

This chapter introduces the mathematics of probability and decision theory. The probability calculus is introduced in both a set-theoretic and a propositional context. Probability is also related to measure theory, and stochastic truth-tables are presented. Problems with conditional probability are examined. Two interpretations of the probability calculus are introduced: physical and normative probabilities. The problem of logical omniscience for normative probabilities is discussed. Dutch Book arguments and accuracy-based arguments for Probabilism (the claim that our credences must satisfy the probability axioms) are examined and rejected. Different interpretations of the “idealization” reply to the problem of logical omniscience are considered, and one of them is tentatively endorsed. The expected utility maximization conception of decision theory is introduced, and representation arguments are considered (and rejected) as another reply to the problem of logical omniscience.
DATA DISTRIBUTION METHOD WITH TEXT EXTRACTION FROM BIG DATA

Right now, Enormous information extraction strategies incorporate the recognition of examples and secured connections between factors numbering and acquire the necessary data. A quick examination of monstrous information can prompt the development and ideas of the hypothetical worth. Contrasted and comes about because of mining between customary informational indexes and the immense measure of huge heterogeneous information associated it can extend the information and thoughts regarding the objective space. Data isolating in immense data examination is creating as a helpful resource for outfitting the force of unstructured scholarly data by separating it to expel new data and to perceive essential models and connections concealed in the data. At present, we isolated the information on gigantic measures of the pages and examined the pages of the site using Java code, and we incorporated the removed information into a remarkable database for the site page. We utilized the information arrange capacity to get precise consequences of assessing and classifying the information pages discovered, which recognizes the believed web or unsafe site pages, and imported the information onto a CSV expansion. Large information emerges new difficulties for IE methods with the quick development of multifaceted likewise called multidimensional unstructured information. Conventional IE frameworks are wasteful to manage this tremendous downpour of unstructured large information. The volume and assortment of huge information request to improve the computational capacities of these IE frameworks. It is imperative to grasp the competency and limitations of the present IE methods related to data pre-taking care of, data extraction and change, and depictions for gigantic volumes of multidimensional unstructured data. Different assessments have been driven on IE, watching out for the challenges and issues for different data types, for instance, content, picture, sound, and video.