Reviewerly/exHarmony huggingface dataset:
Role of Collaborative Filtering in Recommendation Systems

Different categories of techniques can be utilized in recommendation systems (RS), like filtering by the content or hybrid filtering. However, the most extensive and popular RS is collaborative filtering (CF). Its main idea is to calculate and predict the user's interest in any item. If enough data is provided, CF -based RS is sufficient to provide the most accurate prediction as it is a user's preference-based technique. The most crucial part of RS is to predict its user's behavior, and in the past, user-based CF has done it successfully. But their specific usage has uncovered a few genuine problems, like information sparsity and information versatility, with, bit by bit increasing the number of clients and things. This work presents the central ideas of CF, its essential usage for clients with versatile networks, the hypothesis and practice for the calculation of CF and plan settlements concerning rating frameworks & appraisals securing.
Regular Memory Structures and Domain Descriptions for Operations in Intelligence Systems

The concept of a regular memory area of an artificial intelligence system defined. Semantic hierarchies are considered as a uniform and universal format for structural descriptions of knowledge representations. An infinite saturated binary tree, the vertices of which are binary sequences, implements a single memory format. Finite saturated binary trees with labeled vertices represent individual knowledge in infinite memory. Regular expressions are used as a specification for the memory areas in question. Regular memory domains generalize the system of morphism domain class developed for semantic hierarchy formalisms. The study of regular memory structures and the choice of special tools for modeling memory and thought processes in artificial intelligence systems carried out. The structure of regular memory is associated with classes of knowledge processing operations, as well as the goals of classifying, storing and applying knowledge of a given structure in such systems. Applications of regular memory structures creates conditions for a deep formalization of the tools of constructing memory structures and modeling thought processes in intelligent systems. Associating regular expressions with state machines allows using them as a parameter for effective modeling of the intelligent processes.
Soft mode in the dynamics of over-realizable online learning for soft committee machines

Overparametrized deep neural networks trained by stochastic gradient descent are successful in performing many tasks of practical relevance. One aspect of overparametrization is the possibility that the student network has a larger expressivity than the data generating process. In the context of a student-teacher scenario, this corresponds to the so-called over-realizable case, where the student network has a larger number of hidden units than the teacher. For online learning of a two-layer soft committee machine in the over-realizable case, we present evidence that the approach to perfect learning occurs in a power-law fashion rather than exponentially as in the realizable case. All student nodes learn and replicate one of the teacher nodes if teacher and student outputs are suitably rescaled and if the numbers of student and teacher hidden units are commensurate.
Keynote: Advances and Challenges in Payment Channel Networks

Permissionless cryptocurrencies such as Bitcoin enable se-cure payments in a decentralized, trustless environment. Trans-actions are verified through a consensus mechanism and all valid transactions are recorded in a public, distributed ledger, often called blockchain. This approach has inherent scalability issues and fails to meet the growing user demands: In Bitcoin, the transaction throughput is technically limited to tens of transactions per second and the transaction confirmation time is around an hour. In contrast, more centralized payment networks such as the Visa credit card network, can handle peaks of more than 40K transactions per second.
Semantic similarity algorithm of geo-ontology concept based on BP neural network

Aiming at the problems of single considerations, poor universality, and strong subjectivity in the semantic similarity calculation method for the geographic information field, combined with the structural characteristics of the geo-ontology, an algorithm for semantic similarity of geo-ontology concept based on BP neural network is proposed. Based on the traditional calculation of semantic distance and information factor based on the ontology hierarchical tree structure, the ontology properties value and semantic relationship between geographical concepts are extracted based on the authoritative concept description in the field, and they are transformed into the expression of semantic similarity. Synthesize the influence of four types of factors on semantic similarity, and use BP neural network to optimize the weight. Calculating the correlation coefficient between this algorithm and the expert evaluation results and comparing it with other algorithms shows that this method can effectively improve the accuracy and effectiveness of the semantic similarity calculation of geographic information concepts.
A machine visual-based ship monitoring system for offshore wind farms

Ship allision is a crucial topic for offshore wind farms (OWFs) due to catastrophic consequences. To safeguard the OWF facilities, operators adopt CCTV to detect and monitor passing ships in the waters near OWFs manually. This paper proposed an automatic ship detection system for offshore wind farms on the basis of machine vision technologies. In the system, a YOLOv3 algorithm is used to track and identify ships. A stereo vision algorithm is applied to locate ship positions. The system is tested in a real scenario and validated by comparing the visual location results and automatic identification system data. Extensive experiments demonstrate that the detection system of ships can automatically and accurately detect and locate ships. The potential uses of the system include ship invasion and allision avoidance.
Classic concentration: Productivity and organization tools for remote and hybrid marketers

Last month's column featured resources for remote and hybrid higher education marketing teams that focused on three areas: communication, collaboration, and camaraderie.
An Overview on Handling Anti Forensic Issues in Android Devices Using Forensic Automator Tool

The digital forensic methods and tools have become a very important part for the investigators. During the investigation on Cyber-crimes, collection of the digital evidences during the case is the vital part. Mobile forensics being an important domain in digital forensics which includes acquisition of the data and analysing it to recover the digital evidences in a forensic manner. But there are some of the anti-forensic issues faced during the investigation. The Computer Emergency Response Team (CERT) investigates an incident for creation of a detailed description on how the crime was executed and makes a report on the incident. This paper focuses on how the antiforensic issues can be handled in android mobiles and how the improvements took place in the field of mobile forensics and data acquisition. This paper also aims to introduce how the files are acquired during the investigation, what are the techniques and tools that were used to tamper with the evidence found and how the forensic investigators can identify the data tampered. The solution is proposed for one of the Forensic issue of files that are in encoded format where the encoding of the file is not known, using the SVM Machine Learning technique how it can be achieved.
Decision Theory

This chapter begins by introducing the notions of mathematical expectation and utility. Then we explain how expected utility theory connects preference rankings to credence and utilities. We develop Savage’s decision theory, Jeffrey’s evidential decision theory, and causal decision theory. Along the way, such problems as risk aversion, Allais’ paradox, and Newcomb’s problem are considered.
When Infrastructure Studies Meets Digital India (DI)

This chapter provides an introduction to infrastructure theory, to key concepts and contributions includes a review of literature and offers insights into the ways in which infrastructure theory can be applied to an understanding of information infrastructures in India. It includes the example of the Digital India project and critically explores the provisionings of information infrastructures in India. This chapter makes a case for understanding information infrastructures from the perspective of critical political economy and highlights the “tangled” provisionings of information infrastructures in India. Access to infrastructure in India be it to water, electricity, or information is by no means assured for all—and it is therefore important that we understand the gaps between infrastructure ambitions and infrastructure realities.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Double Anonymous • Conference submission management system : Morressier • Number of submissions received : 89 • Number of submissions sent for review : 89 • Number of submissions accepted : 46 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 51.6 • Average number of reviews per paper : 2 • Total number of reviewers involved : 30 • Contact person for queries : Name : Jia-Yush Yen Email : icmaa_info@163.com Affiliation : National Taiwan University of Science and Technology, Taiwan
Implementation of Blockchain Technology in Indian Banking Sector

Blockchain technology has emerged in the last few years to play a path-breaking role in modifying banking operations as we know it traditionally. It helps in cutting down the paperwork that made banking difficult to operate while also becoming prone to delays and, at times, frauds. The convenience of blockchain also comes with robust security measures and is being deployed by banks swiftly to enhance their services and make banking institutions ready for the next digital leap. Features of blockchain technology, such as confidentiality, data integrity and authenticity, can aid India in turning into a cash-free economy. India will particularly benefit from this technology since the projected 136 crores (1.36 billion) population can only be brought into the banking folds when the services are delivered through convenience of digital mediums while not compromising on the security aspects. The study explores various blockchain initiatives taken by Indian public- and private-sector banks and showcases various challenges and perspectives of blockchain technology in the Indian banking sector along with recommended future measures. The study is descriptive in nature and is based on secondary data taken from published reports of public- and private-sector organizations and banks in India and from various reports published by the Government of India.
How SMEs can prevent third-party vendor data breaches

Small businesses are born from passion, personality and a will to grace the world with unique products or services. But in an increasingly digital world, they're built and sustained on practicalities such as having a functional website, providing punctual customer support or efficiently managing finances. Technical skills – and especially security – are unlikely to be one of your core competencies. But if you outsource it, are you really in control?
Cybersecurity

Abstract Dr Deepthi Ratnayake explains money mules and their role in cyber crime.
Python based Audiobook “Lisnbook”

The Lisnbook (or speaking book) is a book that is read aloud. This audiobook needs to be available verbally, in schools, in public libraries, and gradually in music stores. It is often more useful in poetry and theater than in books. It was not until the 1980's that the center began to attract booksellers, and booksellers began displaying audio books on bookshelves instead of keeping them separate .
Variation in White Blood Cell Differential Fluorescence Scattergram: A Clue for Automated Malaria Diagnosis

Background: Malaria is an arthropod-borne disease caused by Plasmodium. Malaria is a significant public health challenge with about 3.3 billion human lives at risk in 97 countries with two hundred million estimated malaria cases and 600,000 fatalities. Timely diagnosis can prevent disease spread, loss of human lives, and its harmful impact on the economy therefore malaria diagnosis by automated hematology analyzer is of great value. Objective: To assess identification of scattergram abnormalities in malaria samples and to compare its performance with Microscopy, RDTs &amp; PCR. Study Design: Descriptive and cross-sectional study Place and Duration of Study: Department of Pathology, Liaquat University of Medical &amp; Health Sciences Jamshoro, 06 months from May 2020 to October 2020. Methodology: Eighty seven malaria suspected samples were included. CBC performed on Sysmex XN1000i followed by Microscopy of thin blood film, RDTs by using STANDARDTM Q Malaria P.f/Pan Ag Test, DNA extracted by QIAamp DNA mini kit, PCR performed by GenoAmp Real-time qPCR Malaria. Results: The median age of patients was 24+13.93 years. WDF Scattergram abnormalities were observed in 95.4 %of samples with sensitivity of 97.6% and specificity of 100% respectively. PPV was 100% and NPV was 99.8%. 96.4% of patients were malaria positive by microscopy. ICT was positive in 96.6% of samples while PCR confirmed plasmodium presence in 97.7% samples. Conclusion: Automated hematology analyzer provides high sensitivity and specificity for malaria diagnosis. The Sysmex XN-1000i can detect certain abnormalities in WDF scattergrams in malaria patients. As a result, the appearance of an abnormal WDF scattergram in a patient with a history of febrile illness, aids the pathologist in confirming a malaria diagnosis. Keywords: Malaria, Plasmodium Vivax, Plasmodium Falciparum, WDF scattergram, ICT, RT qPCR
Practical Applications of Analysis and Comparison of Natural Language Processing Algorithms as Applied to Bitcoin Conversations on Social Media

In <ext-link><bold><italic>Analysis and Comparison of Natural Language Processing Algorithms as Applied to Bitcoin Conversations on Social Media</italic></bold></ext-link>, from the February 2022 issue of <bold><italic>The Journal of Investing</italic></bold>, authors <bold>Benjamin McMillan</bold>, <bold>Joshua Myers</bold> (both of <bold>IDX Digital Assets</bold>), <bold>An Nguyen</bold> (of <bold>Johns Hopkins University</bold>), <bold>Don Robinson</bold>, and <bold>Mark Kennard</bold> (both of <bold>Palladiem</bold>) studied how natural language processing (NLP) algorithms capture investor sentiment online and whether changes in sentiment can predict investment returns. The authors used four different NLP algorithms to measure trends in sentiment toward bitcoin on two Reddit message boards, r/bitcoin (which focuses on bitcoin) and r/investing (which covers general investments). The authors used sentiment analysis to determine whether positive or negative posts about bitcoin correlated with bitcoin’s short-term performance. The authors found a statistically significant correlation between negative posts on r/bitcoin and bitcoin price changes. Bitcoin’s performance tended to drop in the five days before a negative post and rise in the five days afterward. There was no such correlation for positive posts on r/bitcoin or for any posts on r/investing. Therefore, clusters of negative posts on r/bitcoin may mark a local bottom and be predictive of increased performance.
Ancient crystals provide clues about early Earth

Analysis of recently discovered zircon crystals reveals a sharp change in Earth’s geochemistry around 3.8 billion years ago. Researchers studying the ancient crystals say the likely explanation is the onset of plate tectonics ( AGU Adv. 2022, DOI: 10.1029/2021AV000520 ). Earth is the only planet in the solar system that experiences plate tectonics, which causes earthquakes and volcanoes and influences atmospheric chemistry and climate. “The Earth today is so dynamic,” says Nadja Drabon , a geologist at Harvard University who led the research. Some believe this dynamism started less than a billion years ago; others suspect that early Earth was much like the planet of today. Earth’s top layer is now made up of plates that move slowly on the underlying molten mantle. As the plates move over one another, the planet’s crust gets broken up, pulled back down into the mantle, and recycled. Through this process, plate tectonics itself
NFTs: The Future of Digital IP or Just a Remix of DRM?

Abstract Jude Umeh MBCS ponders the promise of NFTs as the future of digital intellectual property and the creative world, or whether they’re simply a reincarnation of DRM and all its woes.
PGN-LM Model and Forcing-Seq2Seq Model: Multiple Automatic Models of Title Generation for Natural Text using Deep Learning

In the current era, the amount of information from the Internet in general and the electronic press in particular has increased rapidly and has extremely useful information value in all aspects of life, many popular users have posted several high-quality writings as casual blogs, notes or reviews. Some of them are even selected by editors to be published in professional venues. However, the original posts often come without titles, which are needed to be manually added by the editing teams. This task would be done automatically, with the recent advancement of AI techniques, especially deep learning. Even though auto-title can be considered as a specific case of text summarization, this job poses some major different requirements. Basically, a title is generally short but it needs to capture major content while still maintaining the writing style of the original document. To fulfill those constraints, we introduce PGN-LM Model, an architecture evolved from the Pointer Generator Network, with the ability to solve Out-of-Vocabulary problems that traditional Seq2Seq models cannot handle, and at the same time combined with language modeling techniques. In addition, we also introduce a model called Forcing-Seq2Seq Model, an enhanced Seq2Seq architecture, in which the classical TF-IDF scores are incorporated with Named Entity Recognition method to identify the major keywords of the original texts. To enforce the appearance of those keywords in the generated titles, the specific Teacher Forcing mechanism combined with the language model technique are employed. We have tested our approaches with real datasets and obtained promising initial results, on both metrics of machine and human perspectives.
Ready for a Career in Quantum?

Abstract Interested in engineering? Programming? Lateral thinking? Brian Runciman MBCS spoke to IBM Distinguished Engineer Richard Hopkins about the many career possibilities presented by quantum computing — both now and those that are coming down the line.
Mathematical Morphology Operators for Harmonic Analysis

Mathematical Morphology provides powerful tools for image processing, analysis and understanding. In this paper, we apply these tools to analyze scores, that are image-like representations of Music. To do that, we consider chroma rolls, a representation of scores similar to piano rolls that use chromas instead of pitches. Endowing this representation with a lattice structure, one can define Mathematical Morphology operators, and setting a group structure to the Time-Frequency plane allows us to use the notion of structuring element. We show throughout some examples that this relates with the notion of pitch-class set and chord progressions, and we analyze two Chopin’s Nocturnes with this technique.
Image processing and pattern recognition issues

The article addresses some practical issues of image processing and pattern recognition in relation to medical and clinical trials. Medical and clinical diagnosis acts as an applied task. It is associated with the recognition on photographs and radiographs of sick people of pathologies of various types. Discretization of objects is carried out using specially designed rules that have different views for tone, color and multi-color images. Also, its algorithm has discretization of continuous curves and lines, points and polyhedra. Various image transformations are described, allowing the doctor to obtain several different projections of any internal organ of the patient. These are axial transverse tomography, image filtering, change of coordinate system, decomposition of images into rows, interpolation and approximation of images.
A variational auto-encoder framework with attention mechanism for image captioning (Withdrawal Notice)

As two core fields of machine learning, Natural Language Processing and Computer Vision have derived a variety of cross research topics, and image captioning as a key topic of the current cross field has an important relationship with the development of both. Image captioning has been attached importance to by researchers for a long time, and has formed a relatively perfect theoretical model system. At present, this topic has made great progress in accuracy, but this is only one aspect of the evaluation of image content description algorithm, which has new requirements in diversity, stylization and controllability. Based on this, this paper studies the technical routes and modeling methods of various auto-coding models, variational models and attention mechanisms, proposing a new way to solve the inherent contradiction between diversity and accuracy, that is to increase the diversity of each sample while ensuring the upper bound of accuracy index scores in multiple samples. The test results on MS COCO data set show that the model implemented in this paper has good performance in accuracy and diversity.
Recognize How Capital Campaigns Are Evolving

The Major Gifts ReportVolume 24, Issue 6 p. 3-3 Expert Opinion Recognize How Capital Campaigns Are Evolving Megan Venzin, Megan VenzinSearch for more papers by this author Megan Venzin, Megan VenzinSearch for more papers by this author First published: 04 May 2022 https://doi.org/10.1002/mgr.31925Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume24, Issue6June 2022Pages 3-3 RelatedInformation
The Realization of Directive Illocutionary Act of Male and Female English Teachers in Junior High School

This study deals with Directive Illocutionary Act of male and female English Teacher in SMP Ad-Durrah Medan. The objectives of this study is to describe the realization of directive illocutionary act of male and female English teachers at SMP Ad-Durrah Medan. This study applied qualitative descriptive method. The techniques for collecting data are through applying observation by taking eight meetings for male and female English teachers to get realization of directive illocutionary act by male and female English. The results of this study found that realization of directive illocutionary act realized from the collaborative, competitive, conflictive , and convivial, where one of the realization was showed that the teacher makes a group discussion for doing the task and the teachers help the students by making a discussion for each student, making a collaboration for their task related to the content of their lesson and presenting the discussion in the class.