Reviewerly/exHarmony huggingface dataset:
Optical enhanced interferometry with two-mode squeezed twin-Fock states and parity detection*

We theoretically investigate the quantum enhanced metrology using two-mode squeezed twin-Fock states and parity detection. Our results indicate that, for a given initial squeezing parameter, compared with the two-mode squeezed vacuum state, both phase sensitivity and resolution can be enhanced when the two-mode squeezed twin-Fock state is considered as an input state of a Mach–Zehnder interferometer. Within a constraint on the total photon number, although the two-mode squeezed vacuum state gives the better phase sensitivity when the phase shift φ to be estimated approaches to zero, the phase sensitivity offered by these non-Gaussian entangled Gaussian states is relatively stable with respect to the phase shift itself. When the phase shift slightly deviates from φ = 0, the phase sensitivity can be still enhanced by the two-mode squeezed twin-Fock state over a broad range of the total mean photon number where the phase uncertainty is still below the quantum standard noise limit. Finally, we numerically prove that the quantum Cramér–Rao bound can be approached with the parity detection.
Study of Computerized Segmentation &amp; Classification Techniques: An Application to Histopathological Imaginary

Recent trends with Histopathological imagery led to rapid progress towards quantifying the perceptive issues, while prognostic, due to subjective variability among readers. This variability leads to distinguished prognosis reports and generates variability in treatment as well. Latest advancements in image analysis tools have allowed the powerful computer-assisted diagnostic system to assist oncologist in their diagnosis process on radiological data. In this study, we present a thoughtful analysis of texture heterogeneity and morphological characteristic on various lymphomas like Follicular, Neuroblastoma, Breast, and Prostate tissue images for classifying them into respective grades. The study presents a systematic survey of the computational steps of these Lymphomas, which includes recent scenario of diagnosis process to classify these lymphomas into respective grades along with its limitations, followed by it shows the pre-requisite of the computer-assisted diagnosis system and finally explains various segmentation techniques based on image descriptor and subsequent classification of biopsy into respective grades. This paper reviews recent state of the art technology i.e. Computer Assisted Diagnosis (CAD) for Histopathology and also briefly describes the recent development in histology and its application towards quantifying the perceptive issue in the domain of histopathology being pursued in the United State and India.
Hamming Code Strategy for Medical Image Sharing

In medical practice, the scanned image of the patient between the patient and the doctor is confidential. If info is stored on a single server and the server is successfully attacked, it is possible to expose confidential information. Password encryption and data authentication are commonly used to protect patient data, however, encryption and data authentication are computationally expensive and take time to execute on a mobile device. In addition, it is not easy for the patient details related to medical images to leak if the hacked image are not visual.Therefore, in this paper, we propose a way to make medical images remain untouched in this sense. We use our method to quickly create two shadows from two medical images and store them on two servers. Revealing a shadow image does nothing to compromise the confidentiality of a patient’s health. This method is based on Hamming code. With low computational cost, the proposed scheme is suitable for tablet, pamphlets and other mobile devices.
TOWARDS SUSTAINABLE CRYPTOCURRENCY: RISK MITIGATIONS FROM A PERSPECTIVE OF NATIONAL SECURITY

Cryptocurrency market is developing fast during the past few years.Cryptocurrency now is available as a form of payment for retail goods, as an instrument for a wholesale international transaction a mean of exchange for whatever goods and is available through ATM's.Moreover, it is developing as a possibility for fundraising a) as a private debt b) as seed capital.Companies like Facebook are discussing launching own cryptocurrency.Bank UBS is developing its blockchain based virtual currency as well.However, scientist agrees that cryptocurrency has an important impact to national security.It became a relevant instrument for illegal good transactions, a mean of exchange in the darknet and an instrument for money laundering or infrastructure for new kind of money-laundering practices (for example-"Smurfing" phenomena (EU Observer, 2019)) European Union is launching AML and KYC procedures for the cryptocurrency market.Would it be efficient?Why are we implementing KYC and AML procedures for cryptocurrency?Is it able to minimize risks?
Definability for model counting

We define and evaluate a new preprocessing technique for propositional model counting. This technique leverages definability, i.e., the ability to determine that some gates are implied by the input formula Σ. Such gates can be exploited to simplify Σ without modifying its number of models. Unlike previous techniques based on gate detection and replacement, gates do not need to be made explicit in our approach. Our preprocessing technique thus consists of two phases: computing a bipartition 〈I,O〉 of the variables of Σ where the variables from O are defined in Σ in terms of I, then eliminating some variables of O in Σ. Our experiments show the computational benefits which can be achieved by taking advantage of our preprocessing technique for model counting.
Requirement Elicitation Framework for Global Software Development

Objectives/methods: In the development process of a software product the requirement gathering is the most significant part of this procedure.The very close interaction of all stakeholders is required to build quality software and in a global software development environment, daily or frequent meetings between all stakeholders are not possible and also costly.To achieve a common understanding of requirements is a challenging task in case of global software development projects.In this situation to get the right requirements is a complicated job.In this study, a framework is presented for improving communication during Requirement Elicitation using strategies and best elicitation techniques for them with Cased Based Reasoning in GSD environment.Findings/application: In this research, we have to use a case study of a real-time project.This has resulted in a framework for improving communication, selecting the best elicitation techniques and CBR for requirement repository in GSD environment.The framework provides a guideline to requirement engineers regarding issues in communication, best strategies and suited elicitation techniques to effectively elicit requirement is GSD environment.
Academic career of young scientists: Motivations and professional roles

The paper investigates the problems of human capital reproduction in higher school.Falling effectiveness of graduate school underlie the research interest in the way, in which motivations for entering a doctoral program affect the choice of young scientists and lecturers about their career development and professional roles in the academic environment.Methodologically the study relies on Vroom's expectancy theory of motivation, as well as theoretical approaches to analyzing the motives behind embarking on a doctorate and the role in academia.The information base includes the results of a survey of 1,429 graduate students from five Russian federal universities.Applying multinomial logistic regression, the authors test the hypothesis that each of the professional roles (tutor, researcher and administrator) is dependent on an individual set of motivations for entering graduate school.The choice of the professional role is largely conscious and independent.Graduates who prefer the roles of tutor and researcher are usually motivated towards this type of activity and interested in benefits of the higher education system, such as flexible working hours, stable pay, etc.The career of researcher exhibits a wider range of motivations and is more sustainable in comparison with the others.The choice of the administrator role is typical of those entering higher school by inertia.The results of the study allow us to identify key motives of graduate students behind choosing particular professional roles.This is of value when formulating the applicant selection policy in accordance with the goals of the university and needs of society and the state.
Latent semantic analysis and cosine similarity for hadith search engine

Search engine technology was used to find information as needed easily, quickly and efficiently, including in searching the information about the hadith which was a second guideline of life for muslim besides the Holy Qur'an. This study was aim to build a specialized search engine to find information about a complete and eleven hadith in Indonesian language. In this research, search engines worked by using latent semantic analysis (LSA) and cosine similarity based on the keywords entered. The LSA and cosine similarity methods were used in forming structured representations of text data as well as calculating the similarity of the keyword text entered with hadith text data, so the hadith information was issued in accordance with what was searched. Based on the results of the test conducted 50 times, it indicated that the LSA and cosine similarity had a success rate in finding high hadith information with an average recall value was 87.83%, although from all information obtained level of precision hadith was found semantically not many, it was indicated by the average precision value was 36.25%.
Self-Enhanced R-CNNs for Human Detection With Semi-Supervised Assumptions

Vision-based human detection is a fundamental task in visual content analysis. It has a wide range of applications, especially for person search and retrieval. To reduce the reliance of detection models on large amount of labeled data, we modify Faster R-CNN to facilitate semi-supervised human detection. Specifically, a Reliability Analysis (RA) module is included as an add-on into our Self-Enhanced R-CNN (SE-RCNN) model. The unlabeled images can be pseudo-annotated reliably under the help of this module. As a result, both labeled and unlabeled data are fed simultaneously for model optimization. The additional supervision, in turn, guides the training of a detection module in our model. The two aspects, extracting precise proposals and generating reliable pseudo annotations, can be mutually reinforced. Unlike previous related works, it is the first attempt to build a single-stage semi-supervised human detection model. In our experiment, we observe that the RA module plays an important role in exploiting unlabeled data and leads to state-of-the-art results of SE-RCNN on multiple benchmarks.
The Challenges of International EFL Students to Learn English in a Non-English Speaking Country

In the recent era, a wider global community has used English for any purpose; one of the most important is academic purposes. In terms of academic purposes, learning English now becomes a must for any students all around the world, specifically EFL students. Focusing on the learning English issue, this research took a rare case about the EFL students do a study abroad program in a non-English speaking country, especially in the context of English learning problems and strategies. Under a qualitative case study, the researcher addressed two main methods to two international EFL students at a private Islamic university in Yogyakarta, Indonesia. By using reflective essays and interviews as the methods, the data concluded that most of the problems found had similarities to the EFL students’ problems when they study in their home country. Along with the problems, the researcher also discovered strategies done by both of the participants to resolve the problems.
Quantum teleportation with hybrid entangled resources prepared from heralded quantum states

In this work, we propose the generation of a hybrid entangled resource (HER) and its further application in a quantum teleportation scheme from an experimentally feasible point of view. The source for HER preparation is based on the four-wave mixing process in a photonic crystal fiber, from which one party of its output bipartite state is used to herald a single photon or a single photon added coherent state. From the heralded state and linear optics, the HER is created. In the proposed teleportation protocol, Bob uses the HER to teleport qubits with different spectral properties. Bob makes a Bell measurement in the single photon basis and characterizes the scheme with its average quantum teleportation fidelity. Fidelities close to one are expected for qubits in a wide spectral range. The work also includes a discussion about the fidelity dependence on the geometrical properties of the medium through which the HER is generated. An important remark is that no spectral filtering is employed in the heralding process, which emphasizes the feasibility of this scheme without compromising photon flux.
Strategies to Improve English Vocabulary and Spelling in the Classroom For ELL, ESL, EO and LD Students

Vocabulary and spelling are two of the most important skills to achieve success in an academic setting. This review of 15 articles highlights classroom interventions that successfully enhanced vocabulary and spelling skills among ESL, English Only, English language learners (ELL), and learning disabled (LD) students. The strategies that enhanced vocabulary skills were reading strategies, story book reading strategies, and memorization strategies. The strategies that enhanced spelling skills were Cover, Copy, Compare (CCC) and writing strategies. Results showed that the strategy of storybook reading enhanced the vocabulary skills among both English Only and ESL students. Writing strategies resulted in spelling skill improvement for students with LD. Future research should focus on the CCC strategy application to improve their vocabulary skills for ESL students who also have LD.
A Basic Framework of Blockchain-Based Decentralized Verifiable Outsourcing

With the development and application of blockchain technology, more and more fields are beginning to use blockchains for industrial innovation, In recent years. At the same time, the data integrity verification techniques based on blockchain have also attracted wide attention of researchers. However, the state-of-the-art schemes are only using the structure of the blockchain on centralized server which cannot leverage the characteristics of the blockchain and resist malicious modification attacks. In addition, the centralized blockchain structure introduces additional storage and computing costs. In this paper, we propose a decentralized verifiable outsourcing framework based on blockchain networks, in which no administrators are required to be in charge of auditing and recording data changes.
CENTRAL AUDIT ACTIVITIES AS A CONTINUOUS AUDIT APPROACH IN THE TURKISH BANKING SECTOR: A CASE STUDY ABOUT FRAUDS IN SAVINGS ACCOUNTS

Developments in information technology constitute the core of a fundamental transformation of internal audit approaches. The traditional concept of internal audit on periodic and traditional auditing with the inclusion of concepts such as data mining, artificial intelligence, and machine learning is replaced by modern continuous, risk-focused auditing. Recently, in sectors that have high transaction volume such as banking, continuous audit activities combined with data mining applications have become prominent in effectively managing fraud risk. In this study, an example of the examination process related to savings account fraud in Turkey has been presented by providing basic information about the central audit activities applied as a continuous audit approach in the banking sector. The banking transactions used as data in this fraud auditing were generated realistically and at random. Data mining techniques have been applied to these transactions to detect fraud risks.
The Effect of Homodyne-Based Feedback Control on Quantum Speed Limit Time

The minimal time a system requires to transform from an initial state to target state is defined as the quantum speed limit time. quantum speed limit time can be applied to quantify the maximum speed of the evolution of a quantum system. Quantum speed limit time is inversely related to the speed of evolution of a quantum system. That is, shorter quantum speed limit time means higher speed of quantum evolution. In this work, we study the quantum speed limit time of a two-level atom under Homodyne-based feedback control. The results show that the quantum speed limit time is decreased by increasing feedback coefficient. So, Homodyne-based feedback control can induce speedup the evolution of quantum system.
Analysis and Design of Waste Management System Using the Spiral Model Towards Smart Cities

IT Governance has provided hope in human life, especially waste management and environmental cleanliness services. The role of IT becomes very important when people want services that are fast, accurate and relevant. Technology using Geographic Information Systems (GIS) has provided opportunities and made it possible to interact easily. The purpose of this research is to provide a GIS for the city environmental and sanitation services in order to provide good services in creating a clean environment. The research method uses the system analysis method with a spiral model approach in system design. The results of the design of GIS have been able to improve the performance of waste management in the city of Makassar.
A Single-stage Multi-class Object Detection Method for Remote Sensing Images

Impressive progresses have been achieved in object detection for images by convolution neural networks. However, a robust multi-class object detection method is still one of the great challenges for remote sensing images. Due to the great diversity of scale, orientation, density and background of objects, most advanced object detection algorithms in natural scenes usually suffer a sharp decline in remote sensing images detection. To solve these problems, we proposed a Single-stage Multi-class Object Detection (SMOD) method, aiming at remote sensing images, which can be trained from scratch and detect multi-class objects quickly and precisely. The proposed method introduces a novel Feature Reuse and Attention (FRA) structure as a key module of feature extraction backbone, which combines SE Attention module and dense Feature Reuse connection. Especially, a multiclass detection structure is proposed to learn from multi-scale, multi-level feature map and get effective attention representation for multi-class remote sensing object detection. SMOD can be trained from scratch without pre-trained network stably and converge well simply by employing batch normalization throughout the network. Experiments show that our trainingfrom-scratch method can obtain better performance compared with some state-of-art algorithms on public multi-class remote sensing dataset AIIA2018-6.
Detection and Analysis of Tor Onion Services

Tor onion services can be accessed and hosted anonymously on the Tor network.We analyze the protocols, software types, popularity and uptime ofthese services by collecting a large amount of .onion addresses. Websites arecrawled and clustered based on their respective language. In order to alsodetermine the amount of unique websites a de-duplication approach is implemented.To achieve this, we introduce a modular system for the real-timedetection and analysis of onion services. The overall data revealsthat a large amount of permanent services provide no actual content for Torusers. A significant part consists instead of bots, services offered via multipledomains, or duplicated websites for phishing attacks. The total amount ofonion services is thus significantly smaller than current statistics suggest
Quantifying Total Influence between Variables with Information Theoretic and Machine Learning Techniques

The increasingly sophisticated investigations of complex systems require more robust estimates of the correlations between the measured quantities. The traditional Pearson correlation coefficient is easy to calculate but sensitive only to linear correlations. The total influence between quantities is, therefore, often expressed in terms of the mutual information, which also takes into account the nonlinear effects but is not normalized. To compare data from different experiments, the information quality ratio is, therefore, in many cases, of easier interpretation. On the other hand, both mutual information and information quality ratio are always positive and, therefore, cannot provide information about the sign of the influence between quantities. Moreover, they require an accurate determination of the probability distribution functions of the variables involved. As the quality and amount of data available are not always sufficient to grant an accurate estimation of the probability distribution functions, it has been investigated whether neural computational tools can help and complement the aforementioned indicators. Specific encoders and autoencoders have been developed for the task of determining the total correlation between quantities related by a functional dependence, including information about the sign of their mutual influence. Both their accuracy and computational efficiencies have been addressed in detail, with extensive numerical tests using synthetic data. A careful analysis of the robustness against noise has also been performed. The neural computational tools typically outperform the traditional indicators in practically every respect.
Wandering Machines

The development of automatic narrative systems has been largely driven by the engineering tendency to anthropomorphize the machine logic so they can ‘tell stories’ similar to how humans do. From the artists’ perspective, however, the experimentation with their media is often more important than the (plausibility of) storytelling, and it often unfolds in non-verbal events that have a potential to generate diverse narratives through experience of the audience. We discuss the emergence of the creative practices that enrich the poetic repertoire of new media art by playfully utilizing the machine flaws, irregularities, errors and systemic technical imperfections thus revealing the human biases and fallacies entangled with technology. One of the implications of these practices is that if the AI research opens up a broader space in which a machine could achieve its own authorial voice, our concept and understanding of the narrative would need to be reconsidered.