Reviewerly/exHarmony huggingface dataset:
Towards Learning Hierarchical Structures with SyncMap

Objects or events perceived by human are often organized in a sequence that forms into chunks which exhibit hierarchical structure, e.g., words or videos. Such a sequence can be represented as a group of temporally correlated variables at multiple levels referred as chunk. In this work, an unsupervised method known as SyncMap is used to perform chunking on sequences of input data with hierarchical structure. We design a fixed and probabilistic chunk experiment to test our model capability, measured by the mutual information between the predicted chunk with the ground truth. Surprisingly, without too much modification on the original algorithm, the result has shown that SyncMap can perform chunking with hierarchical structure, although with limitation. Possible future works are proposed to overcome the limitation. Observation on the dynamic of weight map also indicates that SyncMap adapts to the low-level hierarchical representation of chunks faster than the one on the higher level.
Vulnerability Analysis Through Ethical Hacking Techniques

This article explains the analysis of vulnerabilities with the ethical hacking modalities classified as External Hacking and Internal Hacking used in the information-gathering phase. This study was carried out in the network of the Decentralized Municipal Government of the Olmedo – Manabí County and different techniques of each modality were used to obtain information from this institution. The results obtained, such as the level of severity, what are the most common types of vulnerabilities, or to which categories they belong, are very important information for executing the next phases of ethical hacking and determining the real level of risk of the vulnerabilities present.
Second-Order Attention Network for Magnification-Arbitrary Single Image Super-Resolution

Recently, owing to the application of deep convolutional neural networks (CNNs), single image super-resolution (SISR) has been developed rapidly. Nevertheless, for low-resolution image magnification, most of the super-resolution methods only take into account the integer scale factor, and there are few methods to consider arbitrary scale factor magnification. Besides, most of the super-resolution methods train separate models for different scale factors, which greatly reduces efficiency. Second-order Attention Network for Single Image Super-Resolution (SAN) is one of the super-resolution models with the best effect, but it cannot achieve magnification-arbitrary super-resolution. To address this issue, we use Meta- Upscale Module as a new upscale module of SAN. This module can realize arbitrary scale magnification by dynamically predicting filter weights through scale factors and position-related vectors. Finally, we propose a super-resolution model based on the SAN feature learning module and Meta-Upscale Module named Meta-SAN. Experiments on Set5, Set14, Urban100, and BSDS100 datasets demonstrate the superiority of our Meta- SAN network.
Principles of Robotization in Education

The following is considered, the application of robotics in the education system and artificial intelligence. The special role of AI in robotics is noted. The goals and objectives of the project are indicated: "Robotization in the Higher Education System." The goal of this project is to create software intelligent teaching robots that will fully provide distance learning for students without the participation of teachers in various disciplines and specialties. A description of the structure of a robot- professor and a robot-assistant is given. Expert systems are used as the basis for creating robots for teachers. It is noted that the use of teaching robots in the higher education system opens up unlimited opportunities and horizons for the development of open education for the general population.
Memory-Efficient Database Fragment Allocation for Robust Load Balancing when Nodes Fail

Load balancing queries that access the same data fragments to the same node improves caching for a memory-efficient scale-out. However, to suitably allocate fragments to multiple nodes is a highly challenging problem, particularly when nodes might fail. The problem is to find a good balance between memory efficiency and allocating enough fragments to nodes to obtain robustness through load balancing flexibility. Existing allocation approaches are either not memory-efficient or result in load imbalances, both degrading cost/performance. In this paper, we present an optimal approach and a scalable heuristic, based on three mutually supportive linear programming models, to calculate memory-efficient fragment allocations that guarantee to distribute the workload evenly - even in the case of node failures. We demonstrate the applicability and the effectiveness of our three-step approach using numerical as well as end-to-end evaluations for TPC-H and TPC-DS workloads. We find that our robust solutions clearly outperform state-of-the-art heuristics by achieving a better workload distribution with even less memory.
New Mode of Supply Chain Management and Financing Based on Block-Chain

Since economy is developing rapidly, supply chain finance has become a highly innovative financial model. Some characteristics of block chain technology are well adapted to the development needs of supply chain finance and bring opportunities to finance of supply chain. This paper first introduces the background and characteristics of block chain, then analyzes the main problems in the development of supply chain finance, and puts forward the optimization path of its ecosystem and block-chain technology ecosystem. By doing so, this study hopes to provide a new solution to this problem. By exploring the potential of block chain technology in the field of financing, it will contribute to the research in the field of Finance and supply chain management. This paper aims to explore the potential application of block chain in the field of financing, including the transparency of supply chain and the sustainability of contract performance. The results show that the development of market scale increases with the annual growth, and the maximum market scale in 2019 is 23.93 trillion yuan.
Designing for collaborative learning through the use of Projection-Based Augmented Reality

This research will study how to design digital technology for teaching and learning content in history and culture subjects in an engaging and collaborative manner, using projection-based augmented reality (PBAR). There is a need to design novel technologies to further education in the arts and humanities. In its mission of preserving heritage locations, UNESCO has encouraged institutions, designers, and educators to raise awareness of cultural heritage through technology. This study will explore how PBAR can be designed to support students’ collaborative learning and engagement with the learning content. It will take architectural history and cultural heritage as the content domains for learning. The research process will be informed by HCI and design methods, including participatory design of a PBAR system with students and educators. It intended to design, deploy and evaluate a novel PBAR environment in learning contexts (schools and universities). A range of data types and methods, including participant observation, open-ended interviews, take-home tasks, video analysis, and questionnaires, will be used to investigate the research question. Students’ impressions of and interactions with the novel system will be analysed to identify challenges and opportunities for PBAR to stimulate students’ interest and encourage their collaboration and engagement.
Probability Laws for Nearly Gaussian Random Variables and Application

In an earlier work we described and applied a methodology to find an adequate distribution for Nearly Gaussian (NG) random variables. In this work, we compare two different methods, m1 and m2 to estimate a power transform parameter for NG random variables. The m1 method is heuristic and based on sample kurtosis. Herein, we describe and apply it using a new reduced data set. The second method m2 is based on the maximization of a pseudo-log-likelihood function. As an application, we compare the performance of each method using high power statistical tests for the null hypothesis of normality. The data we use are the daily errors in the forecasts of maximum and minimum temperatures in the city of Porto. We show that the high kurtosis of the original data is due to high correlation among data. We also found that although consistent with normality the data is better fitted by distributions of the power normal (PN) family than by the normal distribution. Regarding the comparison of the two parameter estimation methods we found that the m1 provides higher p-values for the observed statistics tests except for the Shapiro-Wilk test.
Blockchanging Trust

By cutting transaction costs and streamlining agreements' execution via “smart contracts,” blockchain technology (BT) turns decentralization into an economic advantage and an antidote against politically harsh decisions that can obliterate privacy, freedom, and democracy. Although BT's ethical bottom line is still uncertain, its use can smooth out the trade-off between privacy and convenience, reconciling both. BT can also help reconfigure the compromise between intellectual property rights and the common good, opening more ethical routes to the diffusion of innovation. BT's data security can be translated into straightforward access to information. On the one hand, this signals new inclusion routes for “identityless” and unbanked people, and on the other, it releases society from biased information and fake news providing access to trusted data. BT guarantees contents precision, distributing a consensual tamper-proof “hyperledger” proving transactions' authenticity and data's integrity. As consensus should be plural, BT's decentralization is thought to be a must in ethical terms.
CEA-TM: A Customer Experience Analysis Framework Based on Contextual-Aware Topic Modeling Approach

Text mining comprises different techniques capable to perform text analysis, information retrieval and extraction, categorization and visualization, is experiencing an increase of interest. Among these techniques, topic modeling algorithms, capable of discovering topics from large documents corpora, has many applications. In particular, considering customer experience analysis, having access to topic coherent set of opinions expressed in terms of text reviews, has an important role in both customers side and business providers. Traditional topic modeling algorithms are probabilistic models words co-occurrences oriented which can mislead topics discovery in case of short-text and context-base reviews. In this paper, we propose a customer experience analysis framework which enrich a state-of-art topic modeling algorithm (LDA) with a semantic-base topic-tuning approach.
Review of Ideas that Created the Future

What are the most important papers in computer science? What are the most important 46 papers in computer science? Yikes! Far more than 46 seem to qualify. Picking out the top 46 papers in computer science is the task that befallen Harry Lewis (henceforth Harry). I suspect it was both a burden (gee, which ones to omit?) and a joy (Wow, these papers are insightful!). He used two (probably more) criteria that helped him cut it down (1) he prefers short readable papers to long unreadable ones (don't we all!), and (2) no paper past 1980 (arbitrary but firm cutoff). There were also absurd financial constraints based on how much the rights to republish a paper costs. In some cases a paper that is available online for free cost too much to put into a book. See my comment on Turing's 1936 paper. This book is a collection of the 46 most important papers in computer science, in the opinion of Harry, relative to some constraints. The complete list is at the end of the review. While I am sure many readers will think why isn't X in the list, or why is X in the list, or I never heard of X, I suspect that 2/3 of the people reading this review will agree with 2/3 of the papers on the list. I would urge people to read the book AND THEN have an intelligent debate about which papers should or should not be in it.
NumER: A Fine-Grained Numeral Entity Recognition Dataset

Named entity recognition (NER) is essential and widely used in natural language processing tasks such as question answering, entity linking, and text summarization. However, most current NER models and datasets focus more on words than on numerals. Numerals in documents can also carry useful and in-depth features beyond simply being described as cardinal or ordinal; for example, numerals can indicate age, length, or capacity. To better understand documents, it is necessary to analyze not only textual words but also numeral information. This paper describes NumER, a fine-grained Numeral Entity Recognition dataset comprising 5,447 numerals of 8 entity types over 2,481 sentences. The documents consist of news, Wikipedia articles, questions, and instructions. To demonstrate the use of this dataset, we train a numeral BERT model to detect and categorize numerals in documents. Our baseline model achieves an F1-score of 95% and hence demonstrating that the model can capture the semantic meaning of the numeral tokens.
On the Optimality of Dolinar’s Receiver

Dolinar's receiver is an architecture for distinguishing between two possible coherent states, using a photon detector and a local signal which may depend on past detector measurements. The optimal local signal satisfies very favorable properties: it is independent of the time horizon, and the resulting error probability is independent of the measurements. It was also shown that the same signal is optimal in the sense of maximizing the mutual information between the identity of the state and the measurements. In this work we show that the same signal is optimal for the optimization of the expected value of a wide class of objective functions. Our proof is based entirely on convex optimization and functional analysis, without resorting to any "quantum" arguments.
Reconstruction algorithm an object image based on a series of its images distorted in a random environment

The article considers an algorithm for statistical blind deconvolution of images distorted in a random environment and registered as a set of random implementations. The proposed approach reduces the problem of statistical blind image deconvolution to the problem of blind identification of one-dimensional signals. Further, the technique of second-order polynomial statistics generated by random polynomials is used to solve the one-dimensional problem. The article presents the results of modeling the proposed algorithm.
Methodological proposal for the optimization of the installation times of hardenized Linux operating systems through the Spacewalk solution in critical infrastructures

Critical infrastructures are physical or virtual assets that provide a variety of continuous services through various networks like LAN, MAN, WAN, and cloud services. Any interruption in critical infrastructures would have a great impact on the services they provide, preventing the continuity of various business models. The purpose of this methodology is to increase the levels of hardening in critical infrastructures that use the Linux operating system, allowing the continuity of the services they provide and reducing the risk of network attack vectors, adjacent and local. The hardening level will increase during the installation of the Linux operating system by means of a kickstart file with the hardening settings and the Spacewalk server that hosts this file. The proposed methodology consists of four phases. The first phase will be carried out in the installation and configuration of the Spacewalk server. The second phase is to establish the hardening configurations based on the CIS Benchmark in the kickstart file, which will be hosted on the Spacewalk server. The third phase is the deployment of the kickstart file when performing a hardenized installation. In the last phase, the percentage of hardening and the optimization achieved in the installation time will be verified.
Intelligent Transaction System for Fraud Detection using Deep Learning Networks

Abstract Detecting online transaction fraud is a basic study of the new era of electronic transactions. Because the payment patterns of customers and the fraud behaviour of offenders are continually changing, improving the consistency of the fraud detection model and ensuring its stability is exceedingly challenging. In this report, we will look at We concentrate on acquiring deep feature representations of legal and fraud transactions from the perspective of a deep neural network’s loss function in this report. Our aim is to increase the separability and discrimination of features in order to boost the efficiency and stability of our fraud detection platform, with the rapid evolution of the technology, the world is turning to use online transaction instead of cash in their daily life, which opens the door to many new ways for fraudsters to use these cards in a nefarious manner. Global losses are projected to reach $35 billion by 2020, according to the Nilson report. To guarantee that users of these credit cards are secure, the credit card issuer should provide a program that protects them from any threats they can experience. As a result, we illustrate our framework for predicting whether transactions are genuine or illegitimate using Kaggel’s IEEE-CIS Fraud Detection dataset. BiLSTM-MaxPooling-BiGRUM is the name of our model. Long bi-directional gated repeated unit and long bi-directional memory term (BiLSTM) are used in axPooling (BiGRU).
Property, Institutions, and Social Stratification in Africa

‘All fingers are not equal’ is a popular song by Oliver de Coque, a Nigerian High Life musician, who suggested that there would always be inequality in the world. So, if you do not have anything in this world, do not be jealous of those who have, because some fingers are small, some fingers are big. Such a commonsensical view of inequality as natural is often presented in sophisticated theories of economic development without explaining that the tall finger or the fat thumb does not rob and steal from the little pinky or enslave the other fingers or colonize them in order to acquire more ‘finger capital’ or ‘thumb empire’. Rather, all the fingers work together like a family for the benefit of the whole body without any selfish ideologies of racism, homophobia, sectarianism, sexism, and imperialism. Africa went on for tens of thousands of years without these debilitating institutional...
An Automated Management System for References in Programming Code

One way to offset source code plagiarism is by teaching students the importance of referencing externally sourced code. Our work proposes a semi-automated code comment generation system which utilises code similarity detection. This approach is envisioned to ease the code referencing process for students and help establish a proposed referencing standard.
A Comparison between Syllabus of AICTE and various Universities - An NLP Based Approach

This paper focuses on comparative studies between the syllabus of AICTE and various Universities across India and will recommend the changes to be done so as to have holistic development. Today's generation needs to be updated with the technical world. In spite of the growing engineer population, unemployment is on the verge. This is because they have degrees but are not technically skilled. It has become necessary that engineering graduate should possess a set of skills which encompass technical skills and practical knowledge. This paper focuses on NLP based approach and lists the topics which are lagging in those universities as compared to the AICTE syllabus. It also lists the topics which are upfront and updated in those universities and together it provides the means to improve the syllabus.Each course is designed with specific Course Outcomes (COs) and each CO is mapped to the Program Outcome (PO). Hence Course Outcome attainment and mapping of Course Outcome with Program Outcome play a vital role in Outcome-based education (OBE). The attainment of Course Outcomes is quantified by assessing students. Program Outcomes need to be achieved so as to measure the attainment of a particular program. Hence, the program outcomes need to be linked properly. This paper also focuses that if a particular topic is not covered, it will show the Course Outcome not attained for that particular topic. This process will surely help in structuring the syllabus to effectively reach the desired Outcome Based Education goals. A dataset of topics for each module for various courses has been collected and processed through punctuation and stop words, tokenization, stemming, and term weighting.
Integer Sparse Distributed Memory Based on Neural Coding

Sparse Distributed Memory (SDM) is a mathematical associative human long-term memory model that is psychologically and neuroscientifically plausible. It is content addressable and can potentially store unlimited high dimensional information although it only accepts binary vectors. Although an integer SDM has been proposed to store non-binary vectors, the size of the model has been increased significantly. In this paper, we propose a biologically plausible encoding scheme to enable SDM to accept integer vectors, requiring much less space than the existing integer SDM model. A public handwritten digits dataset with integer values has been used for the evaluation of our proposed model and satisfied performance has been achieved in both data retrieval and pattern recognition.
Improving business operation efficiency by using Smart Contract

Industrial Internet of Things (IIoT) is a distributed network of smart sensors, edge gateways, cloud-based applications (e.g. SaaS), which enables precise data monitoring and control of complex industrial process over arbitrary distances. Blockchain technology allows us to have a distributed peer-to-peer network where members can interact with each with a trust-free condition. Smart contract, residing on blockchain technology, is defined in digital form, including agreements on which contract participants can execute these commitments [1]. Several researches have already studied the combination of IoT and Blockchain (especially smart contract) technology, however mainly focused in the framework level [2][3]. This paper will make an experimental study of how smart contract can improve the business operation efficiency together Industrial IoT solution, and further bring new business models to maximize the revenue.
Data Summarization with Hierarchical Taxonomy

Data summarization has wide applications in real world, e.g. attributes filter, image set labeling and personalized recommendation. In this work, we study a new problem HSD to summarize a dataset using k concepts in a hierarchical taxonomy. Different from the existed works of whole hierarchy summarization, we focus on the accurate coverage of the given query set Q. The objective is to cover more items in Q and less items not in Q. To tackle it, we first propose a dynamic programming based algorithm on the tree hierarchy, which is a simple instance of HSD problem. Furthermore, we propose a heuristic method to assign the vertex to one of its in-neighbors for HDAGs and apply the tree algorithm on it. The experimental results confirm the quality of our methods on both tree and HDAG datasets.