Reviewerly/exHarmony huggingface dataset:
Analytical and experimental study of center-line miscalibrations in Mølmer-Sørensen gates

A major challenge for the realization of useful universal quantum computers is achieving high fidelity two-qubit entangling gate operations. However, calibration errors can affect the quantum gate operations and limit their fidelity. To reduce such errors it is desirable to have an analytical understanding and quantitative predictions of the effects that miscalibrations of gate parameters have on the gate performance. In this work, we study a systematic perturbative expansion in miscalibrated parameters of the M\o{}lmer-S\o{}rensen entangling gate, which is widely used in trapped-ion quantum processors. Our analytical treatment particularly focuses on systematic center-line detuning miscalibrations. Via a unitary Magnus expansion, we compute the gate evolution operator, which allows us to obtain relevant key properties such as relative phases, electronic populations, quantum state purity and fidelities. These quantities, subsequently, are used to assess the performance of the gate using the fidelity of entangled states as performance metric. We verify the predictions from our model by benchmarking them against measurements in a trapped-ion quantum processor. The method and the results presented here can help design and calibrate high-fidelity gate operations of large-scale quantum computers.
Study of Quantization of Space-Time Explains Matter and Its Aspects

By quantizing space-time in such a way that I can calculate all of the constants of physics and derive and explain every equation therein. The magnetic field appears when the electric field seems to travel faster than c. The same way an “anti-gravity” perpendicular field appears, totally depended on relative velocities, since the electric and gravitational forces are made of the micro field lines and are different aspects of the same force [1]. The “gravitational” perpendicular force can be repellant, when masses travel in the same direction, (massive distant galaxies repel enlarging the universe, new current mass just being released) and it can be attractive (opposite spiral arms moving in opposite directions, combined with a super-massive black hole). The recent detection of gravity waves, is clear evidence for all of this since these waves need a perpendicular field, like electro-magnetic waves. The magnetic style of the perpendicular field, at extremely high can cause matter and dark to interact
Paraphrasing Techniques for Maritime QA system

There has been an increasing interest in incorporating Artificial Intelligence (AI) into Defence and military systems to complement and augment human intelligence and capabilities. However, much work still needs to be done toward achieving an effective human-machine partnership. This work is aimed at enhancing human-machine communications by developing a capability for automatically translating human natural language into a machine-understandable language (e.g., SQL queries). Techniques toward achieving this goal typically involve building a semantic parser trained on a very large amount of high-quality manually-annotated data. However, in many real-world Defense scenarios, it is not feasible to obtain such a large amount of training data. To the best of our knowledge, there are few works trying to explore the possibility of training a semantic parser with limited manually-paraphrased data, in other words, zero-shot. In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.
Oversampling Techniques for Diabetes Classification: a Comparative Study

This paper presents a comparative study between oversampling variants and machine learning (ML) algorithms to discover the better method for predicting diabetes using unbalanced data. “PIMA Indian Diabetes Data Set” is an unbalanced dataset used by numerous researchers to study ML applied to diabetes. This work presents a comparative analysis between different oversampling variants and the ML algorithms to predict diabetes disease. Ten ML methods and six oversampling techniques have been tested to solve the problem of unbalanced data and make a correct prediction of diabetes disease. The results recommend the combination of SVM SMOTE and AdaBoost algorithm with 83.12%, 73.53%, 86.21%, 90.70% and 26.47% Accuracy, Recall, Precision, Specificity and False Negative Rate, respectively.
Image Steganography embedded with Advance Encryption Standard (AES) securing with SHA-256

The proposed paper, works upon the idea of securing the classified information. This is achieved by using steganography which is an approach to hide classified information into some other file while maintaining its visual aids and secondly is cryptography which works upon textual data and transform it in a way that no one can comprehend it. The proposed method secures the weaker section which is the key in Advance Encryption Standard using hashing technique. The proposed work enhances the level of concealment of information from unauthorized access and for covert information exchange by encrypting the data and hiding it into a multimedia file known as image. The Secure Hash Algorithm 256 generates a hash key of 256 bits which is an unbreakable hashing technique after that the key is used in the process of encrypting the text with Advance Encryption Standard 256 which is an unbreakable encryption technique till this time and a cipher text is obtained. The cipher text is embedded into a target image using Least Significant Bit method which make changes in image that cannot be understand by naked eyes. The change in byte is 0.000002%. It ensures the visual quality of an image remains intact. The distortion or change in the image remains intermittent to human eyes. The major issue concerned for the government and security agencies such as were to exchange highly classified information in a secure and undetectable manner and abide the notion of hacker to comprehend any such information.
Multi objective ant colony algorithm for electrical wire routing

Ant colony optimisation algorithms have been applied to solve wide range of difficult combinatorial optimisation problems like routing problems, assigning problems, scheduling problems and revealed remarkable solutions. In this paper we present a novel approach of ant colony optimisation algorithm to solve the electrical cable routing problem. The study focuses on optimising wire lengths, number of bends and angles of bends. We have studied these objectives in cable routing and modified the ant colony system algorithm to get better solutions. Ants are directed to search for the optimal path between the starting and the ending points by avoiding the obstacles. While ants are navigating, they travel the paths with less number of bends and consider angles of the bends towards 90, 180, and 270 degrees. Normal walls are presented as a grid and doors, windows and other obstacles are represented as rectangles. The possible points to follow by ants are designed according to the BS 7671 (IET Wiring Regulations) standards. The results of the simulation prove with comparisons that this method is feasible and effective for optimising the electrical wire routing.
Leveraging Information Bottleneck for Scientific Document Summarization

This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle.Inspired by previous work which uses the Information Bottleneck principle for sentence compression, we extend it to document level summarization with two separate steps.In the first step, we use signal(s) as queries to retrieve the key content from the source document.Then, a pre-trained language model conducts further sentence search and edit to return the final extracted summaries.Importantly, our work can be flexibly extended to a multi-view framework by different signals.Automatic evaluation on three scientific document datasets verifies the effectiveness of the proposed framework.The further human evaluation suggests that the extracted summaries cover more content aspects than previous systems.
VitaFALL: NXTGeUH System for Well-Being Monitoring with Fall Recognition and Real-Time Vital Sign Monitoring

The blend of computerized data processing with the existing engineering and medic techniques has enabled explorers in the betterment of controlling of patients concerning the two at homes along with at clinics. In this work, numerous fall assessment for fall prediction and detection with vital signs monitoring techniques and methods particularly to establish a research gap and its allied research problems has been reviewed and incorporated using a triple-axis accelerometer and Vital Signs Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the ancient people with a Internet of Medical Things based Vital Signs and Fall Detection (VitaFALL) is proposed which is well-timed and gives an effective judgment of the fall. The four layers comprise sensing, network, data processing and application layer. A caretaker and doctor can be notified by sending alert using a GSM and GPRS module in order that elder can be helped on time, however, a delay in the time is noticed when comparing the gradient and minimum value to predetermine the state of the old person. From a few decades, vital signs have been important parameters to find out the patient’s health level. Vital signs estimation has always been the initial step for the evaluation of the patient and this is also possible by checking the pulse rate or checking the palpation of their forehead for high temperature. ADXL335 Three-Axis Accelerometer Module, tri-axial 14-bit ± 8g accelerometer collects motion information in the VitaFall device. The basic idea is to avoid falls and not to detect them after the loss is done. Walking, stumbling, sitting, falling (right, forward, backward and left) and all other normal motion data patters in the daily life of an older adult (who did no longer have any records or walking issues) are collected. The proposed VitaFall Fall detection model has achieved 85% accuracy, specificity of 100%, and sensitivity of 96% when detecting directional falls. The model uses motion data, real-time vital signs values, falls history to foresee the lows, medians and the highs of falls risks in hospitalized elderly people. When compared with the manual falls risk tools known as the Morse Falls scale, the system got an accuracy of 85%, predictability of 100%, and a sensitivity of 100% too.
An empirical study of the big data classification methodologies

The two hasty emanating technologies are big data and cloud computing. Cloud computing is a novel archetype for providing the computing environment in contrast the big data processing technology is convenient for most of the resource types. Now, a productive cloud-based methodology must be devised for the effective management of the big data. This survey presents the distinct cloud-based classification and clustering approaches adopted for the effective big data classification. This paper reviews 40 research papers in the field of big data classification methodologies, like fuzzy classifier, Bayesian model, support vector machine (SVM) classifier, K-means clustering, collaborative filtering based clustering and so on. Moreover, an elaborative analysis and discussion are made by concerning the employed methodology, evaluation metrics, accuracy range, adopted framework, datasets utilised and the implementation tool. Eventually, the research gaps and issues of various conventional cloud-based big data classification schemes are presented for extending the researchers towards a better contribution of significant big data management.
An Augmented Reality Application for Animal Learning Media at Alian Butterfly Park

Augmented Reality (AR) is an interactive computer program that combines real and virtual objects. In this study, we developed an AR technology that aims to educate visitors to the Alian butterfly park. The application development is based on a preliminary study to the Alian butterfly park location, there are several obstacles in the system of providing educational information to visitors, including butterfly education depending on the facilitator, in an uncertain season the butterflies result in the number of butterflies in the park having small amount, the butterfly museum collection does not display complete information because there is only the name of the butterfly and a brief explanation and the absence of interactive learning support media. Based on the problems, AR application developed packaged in the form of interactive information and educational games targeted to kindergarten to elementary school students. The development method used in this study id Feature-Driven Development. The result of this development, there are three main features developed in this application, namely, butterfly introduction game, quiz, and collecting butterfly cards. From the result of black box testing, it concludes that all the features are well functioned. Therefore, this application is ready to use. This research is limited to the features development, for further research can evaluate the developed application.
Students' Acceptance of Mobile Learning

With the rapid development of telecommunications, mobile learning is appearing as a promising learning approach for students to learn anytime anywhere. However, many key issues regarding the design and implementation of mobile learning are still unclear. This research investigates key factors leading to the acceptance of mobile learning based on students' use of Blackboard Mobile Learn. The research findings will help instructors to design courses for mobile users and system developers to design better mobile learning systems.
A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics

Streaming data analytics has been an important topic of research in recent years. Large quantities of data are generated continuously over time across a variety of application domains such as web and social analytics, scientific computing and energy analytics. One of the key requirements in modern data analytics services is the real-time analysis of these data streams to extract useful and timely information for the analyst. Several distributed data analytics platforms have been developed in recent times to meet this growing requirement of real-time streaming analytics. Nowadays, a large amount of data is generated continuously by geographically distributed sources (e.g., agents, sensors, mobile devices, edge nodes, etc.) in many streaming applications. For instance, services like Facebook, Twitter and Netflix continuously gather data from the end users for a variety of analytical purposes such as finding the popular web content amongst their users or monitoring the QoS metrics. Large content delivery networks (CDNs) like Akamai that serve a significant fraction of content on the Internet continuously collect data from their edge servers and clients from around the globe to understand what, where and how content is accessed for the purpose of providing content analytics insights to businesses.
Supervised approach for object identification using speeded up robust features

This paper proposes a vision based novel approach for real-time object counting. The proposed approach uses the textural information for object counting. Speeded up robust features (SURF) are used to extract the textural information from the image. Firstly, the approach selects stable SURF features from prototype image, i.e., object of interest. These features are matched with the SURF features of scene image captured using vision interface. Feature grid vectors (FGVs) and feature grid clusters (FGCs) are formed for matched SURF features in the scene to indicate the presence of object. Support vector machine (SVM) learning is used to identify true instances of the object. A parameter tuning approach is used to find optimised heuristics for more accuracy and less computation. The proposed approach performs well irrespective of illumination, rotation and scale. A run time environment of the proposed approach is also developed to get real-time status of the object count.
Exploration and application of the value of big data based on data-driven techniques for the hydraulic internet of things

The use of big data technology to screen the massive amounts of hydraulic engineering data in the internet of things is important for its efficient application. This research applies big data methodology to water management to solve numerous problems, such as the demand diversification of related interest groups, overall water difficulties and other problems that arise in hydraulic engineering. A historical database that contains a large amount of data and feedback information is used to design an early-warning health model for a reservoir using big data methods and based on the C5.0 decision-tree algorithm. The health status of Dingdong reservoir is forecast using the model as a case study. The results show that the reservoir is in a healthy state corresponding to no warning level. The early-warning health model is feasible and effective for utilising abundant case resources, and could be used widely in reservoir health management. The results obtained in this paper are beneficial to the sustainable development and scientific management of reservoirs.
Translation of Sign Language for Deaf and Dumb People

Deaf-mute people can communicate with normal people with help of sign languages. Our project objective is to analyse and translate the sign language that is hand gestures into text and voice. For this process, RealTimeImage made by deaf-mute peopleiscapturedanditisgivenasinput to the pre-processor. Then, feature extraction process by using otsu’s algorithm and classification by using SVM(support Vector Machine) can be done. After the text for corresponding sign has been produced. The obtained text is converted into voice with use of MATLAB function. Thus hand gestures made by deaf-mute people has been analysed and translated into text and voice for better communication.
Does bank-based financial development spur economic growth Empirical evidence from the Democratic Republic of Congo

In this study, we examined the dynamic causality between financial development and economic growth in the Democratic Republic of the Congo (DRC), using time-series data from 1965 to 2015. Unlike some previous studies, the current study used three proxies to examine this linkage. In addition, the study used savings and inflation as intermittent variables, thereby creating a multivariate Granger-causality model, and limiting the omission-of-variable bias. Using the ARDL bounds testing approach, the study found that there is a short-run causal relationship between financial development and economic growth in the DRC, but the direction of causality is dependent on the proxy used to measure the level of financial development. The study recommends that policy efforts in the DRC should be directed at developing both the financial and the real sectors in the short run as both sectors have been found to be mutually beneficial to each other, in this study.
Distributed data-dependent locality sensitive hashing

Locality sensitive hashing (LSH) is a popular algorithm for approximate nearest neighbour (ANN) search. As LSH partitions vector space uniformly and the distribution of vectors is usually non-uniform, it poorly fits real datasets and has limited efficiency. In this paper, we propose a novel data-dependent locality sensitive hashing (DP-LSH) algorithm, which has a two-level structure. In the first level, we train a number of cluster centres, and use the centres to divide the dataset. So the vectors in each cluster have near uniform distribution. In the second level, we construct LSH tables for each cluster. Given a query, we first determine a few clusters that it belongs to, and perform ANN search in the corresponding LSH tables. Furthermore, we present an optimised distributed scheme and a distributed DP-LSH algorithm. Experimental results on the reference datasets show that the search speed of DP-LSH can be increased by 48 times compared to E2LSH, while keeping high search precision; and the distributed DP-LSH can further improve search efficiency.
A Deep Learning Based Level Set Model for Pancreas Segmentation

A deep learning based active contour framework is proposed for pancreas segmentation. Data extension and fractional differential operation are firstly applied for pre-processing. Second, deep learning method is designed to acquire the initial contour of pancreas. Subsequently, an intensity constrained term is designed to stop the contours at the edges. The intensity constrained term is integrated into a variational active contour model with three terms. The accurate pancreas segmentation is obtained by the evolution of the active contour model. Our approach reaches high detection dice similarity coefficient (DSC) of 83% and sensitivity of 85% in a dataset containing 40 abdominal CT scans. Comparisons with other level set models provide evidence that the proposed method offers desirable performances.
How to Maximize the Advantages of Online-learning under the New Blended Teaching Mode for College English

In the era of information technology, college English should make good use of the advantages of online resources to promote the reform and development of college English teaching. This paper discusses how to use online teaching to promote blended teaching of college English based on constructivism theory. Online teaching has many advantages, such as richness, integration, high efficiency, autonomy, supervision and so on. But how to bring its advantages into full play depends on various factors, such as teachers, students, and network resource developers and so on. Our country should strengthen the construction of online courses and improve the development of teaching platforms. Teachers should learn to design the teaching contents reasonably before, during and after class. Students’ learning autonomy should be mobilized. Also, a perfect learning evaluation system for blended teaching is also needed. Only in this way can college English teaching develop better with the help of online teaching.
Towards a Better Understanding of Relationship between Dubai Smart Government Characteristics and Organizational Performance

The study aims at examining the influence of Dubai smart government characteristics on the governmental organization performance. Online survey was used to collect data for this study, the sample size was determined as 250 users of Dubai smart government services, who are users who got the services from five major strategic or government partners of smart government establishment: Dubai Police, RTA, DEWA, DHA, and Dubai Municipality. PLS (Partial Least Squares) SEM-VB (Structural Equation Modelling-Variance Based) was employed to assess the research model by utilizing the software SmartPLS 3.0. This paper adds to the existing literature of smart government characteristics (Information System Quality, Relationship with Public Agencies, Leadership, Accountability and Transparency, and Productivity) and governmental organization performance (Innovativeness, Efficiency, Collaboration, Communication, and Competition Intensity). The results of this study have the potential to give further insights into Dubai government to improve their organizations performance.