Reviewerly/exHarmony huggingface dataset:
The Detection and Control over the offences of White Collar Crimes, Frauds and Hacking of information, by using effectively the relevant Software and Electronic Devices

In both developed and emerging nations, fraud, white-collar crime, and malpractices in small and medium-sized businesses, banking, and other sectors are on the rise. The criminals are carrying out their fraud-related offenses by using the most up-to-date information technology structures and similar electronic media to create unjust loss and harm in the fields of white-collar crimes, banking, business, and to persons. The goal of this research paper is to make these businesses understand the need of utilizing the most up-to-date, trustworthy, and legitimate software and the necessity of making their networks more secure from external threats. The thieves are able to accomplish their goals thanks to the usage of sophisticated features for tracking and hacking private information utilizing software and information technology resources. In essence, their tactics rely on deceit and dishonesty to harm others. In essence, their methods of operation rely on deceit and dishonesty to commit the crime. Employees of the affected organizations are frequently complicit in banking and white-collar scams, aiding and abetting outside criminals by transferring sensitive information. Parody, distortion, misrepresentation, twisting of the fact or truth, and concealing of actual information that is harmful to another person are all examples of fraud. The legislation's attempt to punish fraudsters seems extremely naive. There are many reasons why criminals flee, including the fact that their identities are frequently kept a secret. The criminals are able to move toward exoneration and freedom due to a lack of evidence, shoddy investigation, and naive prosecuting tactics. The fraudsters possess high-quality Software Engineering Tools and Electronic Equipment to commit offenses of such to harm entrepreneurs, financial organizations, and commercial banks by depriving and extracting information and money from the concerned accounts, Technically and operationally feasible and valuable suggestions for implementation are presented in this paper to safeguard the Networks of organizations.
Noble metal catalyst detection in rocks using machine-learning: The future to low-cost, green energy materials?

Carbon capture and catalytic conversion to methane is promising for carbon-neutral energy production. Precious metals catalysts are highly efficient; yet they have several significant drawbacks including high cost, scarcity, environmental impact from the mining and intense processing requirements. Previous experimental studies and the current analytical work show that refractory grade chromitites (chromium rich rocks with Al2O3 > 20% and Cr2O3 + Al2O3 > 60%) with certain noble metal concentrations (i.e., Ir: 17-45 ppb, Ru: 73-178 ppb) catalyse Sabatier reactions and produce abiotic methane; a process which has not been investigated at the industrial scale. Thus, a natural source (chromitites) hosting noble metals might be used instead of concentrating noble metals for catalysis. Stochastic machine-learning algorithms show that among the various phases, the noble metal alloys are natural methanation catalysts. Such alloys form when pre-existing platinum group minerals (PGM) are chemically destructed. Chemical destruction of existing PGM results to mass loss forming locally a nano-porous surface. The chromium-rich spinel phases, hosting the PGM inclusions, are subsequently a second-tier support. The current work is the first multi-disciplinary research showing that noble metal alloys within chromium-rich rocks are double-supported, Sabatier catalysts. Thus, such sources could be a promising material in the search of low-cost, sustainable materials for green energy production.
ANALYSIS OF TWITTER DATA USING MACHINE LEARNING ALGORITHMS

Sentiment analysis is one among the distinguished fields of knowledge and pattern mining that deals with the identification and analysis of sentiment within the text. The main challenges in sentiment analysis are word ambiguity and multi polarity. The problem of word ambiguity is to define polarity because the polarity for words is context dependent. The tweets are initially preprocessed. The preprocessing includes the removal of stop words, and lower case conversion. The tweets are then passed to the feature extraction techniques. Then the data is splitted as training and testing data. The trained data is passed to the different machine learning algorithm like Naive Bayes. Support Vector machine, Random forest, and Decision Tree and k-NN algorithm. The accuracy obtained using the Naive Bayes. Support Vector machine, random forest, and Decision Tree, k-NN and Logistic regression algorithm is 80%, 77%, 72%, 61% ,56% and 78%. The naïve bayes algorithm has achieved a better accuracy when compared to the other algorithm. KEYWORDS: SVM, Naive bayes, Decision tree, Random forest
The REBUS project at Volga Tech: on the way towards student-centered learning

The paper analyses the impact of the REBUS project (REady for BUSiness) on the development of student-centered learning (SCL) at Volga State University of Technology, one of the two Russian universities participating in the EU funded Erasmus+ endeavor to introduce entrepreneurial education for engineering students. Along with mobility, lifelong learning and employability, SCL composes philosophical grounds of a European Higher Education Area. Such tools as ECTS, Diploma Supplement and Qualification Frameworks are all aimed to help students to achieve certain learning outcomes, or statements that describe the knowledge 50 POLIS No. 19, ISSUE 2/ 2020 or skills students acquire by the end of a particular assignment, and help students understand why that knowledge and those skills will be useful to them. The authors perceive SCL as a complex phenomenon, representing both a mindset and culture of teaching and learning. In practice, it deploys innovative methods of teaching, and fostering transferable skills such as problem solving, critical thinking and reflecting thinking. The Russian government signed the Bologna declaration in 2003, thus giving path to series of reforms aimed at modernization of national university system, and increase of global competitiveness of Russian higher education institutions. Since that, Russia has taken extensive efforts to adjust its higher education system to the European standards. One of the most prominent steps was move towards Bachelor’s – Master’s – Postgraduate learning cycles in the vast majority of universities, along with introduction of mobility programmes, ECTS-like credit transfer system and Diploma Supplement. However, not too much has been changed at programme and classroom level to make learning process more student-centered. The authors argue that REBUS project with its intense use of blended learning, personalized tools for validation of competences and skills, and international mobility has created a new type of student-teacher relationship within one piloting programme, and can serve as a good example of SCL in practice.
A feature-driven variability-enabled approach to adaptive service compositions

Service compositions are widely used to construct complex applications. Due to the frequent changes of environment and requirements, service compositions need to be adaptable enough. In this work, we propose a feature-driven variability-enabled adaptive service composition approach to systematically treat the variability in the full life-cycle of service compositions. Specifically, the feature model is introduced to represent common and variable requirements and drive the variability design of service compositions. An abstract service composition model is used to define the variable business process. Rules and algorithms are then defined to transform the feature model to the abstract service composition model, from which different process instances are derived on demand to meet different requirements. We have developed a prototype tool to facilitate and automate our approach as much as possible. Finally, a case study is conducted to demonstrate the proposed approach and validate its effectiveness and efficiency.
A brief survey on image segmentation based on quantum inspired neural network

Information retrieval is a rudimentary approach which is highly solicited for image analysis research. In classical image analysis, image processing recovers the objects from the noisy images in an efficient way at the cost of substantial time complexity. Quantum computation assisted algorithms play a vital role to extract objects from binary, grey, pure or from true colour images using quantum computing principles which reduces the time complexity while improving the segmentation efficiency. These algorithms take recourse to the quantum superposition principle in achieving the task. A neuro-biological network architecture comprises of different nodes corresponding to the image pixels which are converted to the equivalent qubit neurons in the quantum inspired versions. The ability of information retrieval capability from the images by means of qubit neurons is a much-talked affair now-a-day. In this paper, the authors attempt to present a brief survey on image segmentation techniques using the state-of-the-art techniques including quantum inspired neural networks.
Teman (Tablet Effervescent Manggis) Pengembangan Dan Pengolahan Ekstrak Kulit Manggis Menjadi Effervescent Sebagai Suplement Kesehatan ditinjau dari Value Propotion Canvas dan Lean Canvas

Indonesia is one of the strategic markets for the world's health and food supplement industry. And along with the increase in people's purchasing power, the consumption of health supplements is also becoming a trend. And currently, mangosteen fruit extract is processed and marketed in conventional forms, ranging from syrups to capsules and another inefficient packaging. The business model for making mangosteen fruit extract into an effervescent tablet product is currently not available on the market, so producing it can be a business opportunity. The system development method used in this research uses the lean canvas method as business analysis. By applying lean canvas, it can make it easier for makers to carry out the validation process because they can see the overall business model, reducing the risks of doing business. This is done because the product offered is new
Semantic Scene Understanding for Human-Robot Interaction

Service robots will be co-located with human users in an unstructured human-centered environment and will benefit from understanding the user's daily activities, preferences, and needs towards fully assisting them. This workshop aims to explore how abstract semantic knowledge of the user's environment can be used as a context in understanding and grounding information regarding the user's instructions, preferences, habits, and needs. While object semantics have primarily been investigated for robotics in the perception and manipulation domain, recent works have shown the benefits of semantic modeling in a Human-Robot Interaction (HRI) context toward understanding and assisting human users. This workshop focuses on semantic information that can be useful in generalizing and interpreting user instructions, modeling user activities, anticipating user needs, and making the internal reasoning processes of a robot more interpretable to a user. Therefore, the workshop builds on topics from prior workshops such as Learning in HRI, behavior adaptation for assistance, and learning from humans and aims at facilitating cross-pollination across these domains through a common thread of utilizing abstract semantics of the physical world towards robot autonomy in assistive applications. We envision the workshop to touch on research areas such as unobtrusive learning from observations, preference learning, continual learning, enhancing the transparency of autonomous robot behavior, and user adaptation. The workshop aims to gather researchers working on these areas and provide fruitful discussions towards autonomous assistive robots that can learn and ground scene semantics for enhancing HRI.
A coherence interpretation of nonlocal correlation

Quantum correlation is the keyword in quantum information technologies. Over the last several decades, quantum correlation violating local realism has been intensively studied in various areas, but still left as a mysterious quantum phenomenon. Here, a coherence approach is applied to investigate the fundamental physics of nonlocal quantum correlation for a typical model based on entangled photon pairs without violation of quantum mechanics. As a result, a complete coherence solutions are found for coherent photons via coincidence detection, resulting in a deterministic joint basis relation between space-like separated parties in an inseparable basis-product manner.
Distributed quantum sensing and entanglement-enhanced multiphase estimation theory

We study a phase estimation protocol based on the distribution of a single squeezed state among an array of Mach-Zehnder interferometers (MZIs). The fundamental component of our scheme is the quantum circuit (QC), a linear network that optimally distributes the squeezing generated at one of its inputs among the d MZIs, where d unknown parameters &theta;<sub>1</sub>, . . . , &theta;<sub>d</sub> are then imprinted and the number of photons at the outputs finally measured. For any given linear combination of the parameters, we can optimize the QC and achieve sub-shot-noise sensitivity. Our parallel strategy, based on the mode-entanglement created by the QC, outperforms the rival and more common sequential strategy, in which the same unknown parameters are estimated independently. It also saturates the ultimate sensitivity bound, the quantum Cramer-Rao bound, in a relevant regime of parameters, thus constituting an optimal estimation method in that regime.
Improving Workplace and Societal Cybersecurity via Post-Secondary General Education

Everyone has a role to play in cybersecurity and cyber risk management, but people without security backgrounds seldom understand—let alone accept or endorse—such roles. Public and private organizations face common challenges in facilitating more secure behaviors among employees. As part of their missions, most colleges and universities in the United States have general education programs that aim to instill certain competencies and characteristics in all graduates (for individual and greater good). This paper proposes that a cybersecurity general education course could help improve common workplace challenges in cybersecurity training and awareness, and that such a course could align with each institution’s general education goals to benefit not only graduates but also communities and society writ large.
Re-embracing MSL approaches in realities of emergent conditions

In recent years, educational routines have been challenged with emergent conditions related to COVID-19. These challenges impact how teaching, learning, and evaluations are being exercised. This paper describes and suggests an alternative approach to alleviate challenges related to evaluation activities required in programming courses during days of the pandemic. In such cases, emergent conditions restrict evaluation activities, and accordingly, we suggest that an overall experience should be seamlessly conducted across contexts and settings (Hwang &amp; Chang, 2021). Thus, an activity provides lecturers and students with a smooth transition while shifting between the formal course and the evaluation activity. This activity is examined in light of ten dimensions concerning Mobile Seamless Learning (MSL) reflecting education exercised across context and settings. Accordingly, we conducted qualitative research to examine quotations from 23 students addressing the activity. We searched for students’ insights concerning MSL dimensions, and their perceived potential to alleviate educational challenges in the realities of emergent conditions. This research indicates the potential of this alternative approach for an evaluation activity capable of coping with challenges experienced by lecturers and students during the time of COVID-19. Occasionally, and due to technical and administrative issues, MSL activities were perceived as challenging to conduct. We present the outcomes of our research efforts, as we hope to encourage lecturers to reconsider and re-embrace MSL activities as part of the practices they exercised during emergent conditions experienced in the days of the pandemic.
Detecting acute leukaemia in blood slides images using a CNN ensemble

Leukaemia is a disease that has no defined etiology and affects the production of white blood cells in the bone marrow. Young cells or blasts are produced abnormally, replacing normal blood cells (white, red blood cells, and platelets). Consequently, the person suffers problems in transporting oxygen and infections combat. Acute leukaemia is a particular type of leukaemia that causes abnormal cell growth in a short period, requiring a quick start of treatment. Classifying the types of acute leukaemia in blood slide images is a vital process, and a system of assisting doctors in selecting treatment becomes necessary. This article presents an ensemble approach composed of three convolutional neural networks (CNNs) - AlertNet-RWD, ResNet50 and InceptionV3. These CNNs, individually, demonstrated effectiveness in differentiating blood slides with acute lymphoid leukaemia (ALL), acute myeloid leukaemia (AML), and healthy blood slides (HBS). We verified that the union of these three well-known CNNs improves the hit rates of current techniques from the literature. The experiments were carried out using 18 public data sets with 3,293 images, and the proposed CNN ensemble achieved an accuracy of 96.17%, and precision of 96.38%.
Learning Media Innovation to Improve the Ability to Remember Hijaiyah Letters for the Deaf

Deaf people have difficulty in remembering things that are abstract so it is absolutely necessary learning media that can help the subject matter more concrete. The purpose of this study is to prove the effectiveness of Bhilar learning media in improving the ability to remember hijaiyah letters in deaf people. The research uses a quantitative approach to the Single Subject Research (SSR) method of A-B-A design. The subjects of this study were DN (12 years old) and OC (11 years). Based on the results of data processing, the baseline phase level mean 1 (A1) was obtained in the DN subject by 18.43 percent and OC by 15.62 percent. Mean intervention phase level (B) in DN subjects was 67.22 percent and OC was 58.89 percent. Mean baseline phase level 2 (A2) in DN subjects by 95 percent and OC by 86.87 percent. Then it can be concluded that Bhilar learning media proved effective in improving the ability to remember hijaiyah letters because Bhilar learning media uses visual imagery that forms mental images of information.
A Hybrid Book Recommendation System Using Genetic Algorithm for Enhancing Book Rating

Recommendation systems have emerged as the most prevailing systems due to the abrupt use of online services during pandemics from past years. Multi-billiondollar industries such as kindle, Alibaba, amazon, Careem, and many other local online applications of grocery and medicine are heavily dependent on these systems. Applications based on machine learning models increase the accuracy and efficiency of the recommendation system and eliminate the possibility of human effort in finding relevant items. Machine learning models learn, recognize patterns, and make decisions with minimal human intervention based on data. We have developed an innovative and novel book recommendation system. We have used a genetic algorithm to enhance the rating of books and find the distance between similar users and recommend books. The Dataset is being used is taken from Amazon web services and it is available on Kaggle as Books.csv.
Online System QoS Enhancement of Flipped Classroom for Intelligent Manufacturing Specialty Based on Intelligent Information Platform

In-depth analysis of the online teaching mode of the "Machine Vision and Inspection" course of the intelligent manufacturing major, and research on the teaching work of the machine vision and inspection course from the teaching objectives and positioning and the key content of the course. And in our school network engineering major Corresponding experiments were carried out. The pilot results show that, for ordinary colleges and universities aiming at cultivating applied talents, the characteristic reform of the original network engineering-related majors can effectively meet the local demand for talents in this field. It is an end-to-end QoS monitoring solution that combines deployment and centralized management.
Real-Time Tracking and Positioning of Underwater Visual Targets Based on Siamese Network

In this paper, the real-time tracking and positioning problem of underwater targets under the monocular vision system is investigated based on Siamese network. First, to solve the problem that monocular vision system cannot spatial positioning, an artificial marker with multi-features information is designed. On this basis, the tracking module and detection module are respectively designed to achieve real-time stable tracking and precise positioning of underwater targets. The experimental results of three sets of different environments show that the detection rate can reach 20FPS, and the positioning error does not exceed 1.4 cm, which demonstrates the real-time performance and strong generalization ability of the method proposed in this paper.
Path Planning Method for Automatic Parking Based on Hybrid A*

To solve the final critical parking process of automated valet parking, for the common vertical, parallel, and oblique parking scenarios, the hybrid A* path planning algorithm which combines the A* algorithm and the Reeds-Shepp curve is used. The algorithm also contains the collision detection method which combines the circular collision detection and the rectangular collision detection, finally, the collision-free parking route of the target vehicle is obtained. The algorithm considers the heading angle and kinematic constraints of the target vehicle, so it is suitable for practical parking problems. The simulation simulates three typical parking scenarios and compares the path planning results with the A* algorithm. The simulation results show that the planned path of the proposed algorithm has higher quality, requires fewer spot turns, and is more in line with the requirements of the vehicle steering motion. The feasibility and generality of the algorithm are verified through the numerical simulation.