Reviewerly/exHarmony huggingface dataset:
Novel Methods of FCM Model Reduction

Hatwágner, Miklós F. FuzzyKóczy, László T. Cognitive Maps are widely applied to support decision making tasks. It is often hard for experts to create the model of a system that provides the required accuracy but simple enough to easily use in practice. In general, it is better to create complex models first, because they can be computationally reduced later until they preserve the required accuracy but become simple enough. Two novel Fuzzy Cognitive Map reduction methods based on K-Means and Fuzzy C-Means clustering are suggested in order to generate simplified models that hopefully mimic the behavior of the original model better than the already existing methods. After the quick overview of the existing techniques found in literature, a simple and a complex model of a real-life problem are reduced to varying degrees with the suggested new methods and with an existing one. The first results of the comparison are published in this paper, too.
COVID-lockdown in English higher education March–June 2020. Were disabled students’ needs forgotten?

The COVID-19 pandemic forced HE Providers (HEPs) to radically transition traditional teaching and assessment to 100% remote delivery. UK Governments’ policies widening HE participation have yielded a significant minority of disabled students (15%). This study investigated English HEPs (n = 133) transition advice to academics regarding these students’ needs. Of 104 respondents, 16% provided new advice regarding remote teaching for disabled students, 22% regarding remote assessment; only 2 mentioned they had conducted Equality Impact Assessments (EIAs) about the changes. Disabled students’ needs appear forgotten. Four response models were identified: Keep Calm and Carry On, Meet an Existential Threat, All Hands to the Pumps, An Opportunity for Change. Emergent good practice examples are given. Most English HEPs should urgently conduct EIAs under their legal Anticipatory Public Sector Equality Duty. The whole sector needs to better institutionalise delivering to disabled students’ needs through effective inclusivity policy implementation, and educating academics and academic management.
Identifying Darknet Vendor Wallets by Matching Feedback Reviews with Bitcoin Transactions

Darknet markets are e-commerce websites operating on the darknet and have grown rapidly in recent years. Darknet only allow cryptocurrencies as the payment methods, making it hard for law enforcement to trace those illicit transactions. In this paper, we present a method to identify vendors&#x2019; bitcoin addresses by matching vendors&#x2019; feedback reviews with bitcoin transactions in the public ledger. The problem is decomposed into two steps in formulation. In Step 1, we solve a bounding box matching between the set of feedback reviews and bitcoin transactions. In Step 2, we find the bitcoin addresses with a maximum coverage of the reviews. Baseline algorithm for Step 1 runs in quadratic time thus we develop a K-D tree to accelerate the computing. Problem in Step 2 is NP-hard thus we develop a greedy algorithm with an approximation ratio of (1 &#x2212; 1/e) based on the submodular property of the objective function. We further propose a cost-effective algorithm to accelerate both steps effectively. Comprehensive experimental results have demonstrated the effectiveness and efficiency of the proposed method.
TPE-MHA: A Malicious Traffic Detection Model Based on Time Position Encoding and Multi-head Attention

The proliferation of malicious software brings severe challenges for network security, hence there is an immediate need for devising efficient anomaly detection caused by malicious behavior. Traditional machine learning-based anomaly detections are highly dependent on expert knowledge to extract the features. This is however a time-consuming and costly process. Deep learning-based anomaly detection techniques have been applied to build end-to-end malicious traffic detection models. Since most of these works are based on image recognition or text classification methods they ignore the structural features of the network traffic and unbalanced distributed feature of the data packets in the time domain. This reduces learning capability and performance of these models. To address these issues, we analyze the hierarchical structure of the traffic features and propose a new time position encoding technique based on the similarity between network traffic and text language. Using this approach we then devise the Time Position Encoding and Multihead Attention (TPE-MHA) as an end-to-end detection model for traffic anomaly detection. TPE-MHA is based on the Multi-head Attention mechanism and does not require prior knowledge of network protocol. Experimental results based on public datasets of USTC-TFC2016 and CICIDS2017 confirm the validity and efficiency of the proposed model.
Design a Remote Patient Health Monitoring System (RPHMS) Using IoT

Healthcare has become one of the principal issue with the rise in human population and medical expenditure. For a healthy life, it is essential to follow human body’s vital signals. Continuous Monitoring of patient’s vital signals cannot be provided outside hospital. As it is hard to monitor the patient’s condition for 24 hours, it was proposed in this paper to observe continuously the condition of patient despite the patient being busy with his routine and to screen the health status to the doctors through Internet of Things. This paper proposes health monitoring system using non-intrusive biomedical sensors that measure five parameters like ECG, SPO2, toxic gas, temperature and blood pressure. Proposed method makes use of Arduino Uno Controller to which non-invasive biomedical sensors are connected. The output is displayed on any digital monitoring system using Arduino Uno. The data obtained from the sensors is uploaded to the Thing Speak cloud to store and to access patient’s information by their doctors or by the concerned for necessary follow-ups in real-time. IoT is a powerful domain where sensors can connect and data is viewed over the Internet.
COCOKG: A Multimodal Knowledge Graph Building on MSCOCO

Multimodal knowledge graphs play important roles for multimodal recommendation and query answering systems. Multimodal knowledge graph is drawing more and more attentions because it can benefit from the large number of images and texts available on the Web. In this paper, we propose COCOKG, a multimodal and common sense knowledge graph building on MSCOCO dataset. Each image in MSCOCO has five or more documents describing the image. To build COCOKG, we first summarize the multiple MSCOCO captions to extract image-document pairs representation for the dataset. Second, each document is parsed and analyzed for its syntactic and dependent structures. Finally, entity and relationship triples are extracted based on the syntactic and dependency features to construct COCOKG knowledge graph. To enrich COCOKG, we also propose an encoder-decoder based pipeline to augment COCOKG with new images and triplets.
Measurement of acylindrical surface with transport of intensity equation

High-precision aspherical cylindrical (acylindrical) lenses are difficult to directly measure because of the phase deviation in the off-axis region. To achieve rapid and non-contact measurement of the acylindrical lens surface, a novel optical structure phase measurement, to the best of our knowledge, is presented in this work. Both common finite-difference and noise-reduction finite-difference methods were used for solving the transport of intensity equation (TIE) for reconstruction of high-resolution surface measurement. The results suggest that both common finite-difference and noise-reduction finite-difference methods can obtain good measurement results. The proposed method allows for the direct measurement of surface information without interference stitching. The accuracy of the TIE measurement has been verified through direct contact measurement.
Development of a Mobile Application to Buy Books through Visual Recognition

Mobile devices have become the most used tool for a large number of tasks that we regularly perform such as relating them, searching for information, and in particular for making purchases. A situation that is frequently repeated in many areas is discovering an object that belongs to another person but we would be interested in being able to acquire it. However, the problem arises of knowing where to buy it. For example, this happens with the clothes that other people are wearing. Today, technology offers recognition mechanisms that can help solve this problem. This article presents an Android app that can recognize a book based on an image and offer places where it can be purchased. For this, Google technology was used to recognize objects from images and it has been combined with the information provided by Google Books to find stores that sell recognized books. In this way, a system has been created that makes it easier for any user to identify and purchase books that they discover at any given time.
Semantic Model-based Dynamic Adaptation Method for Heterogeneous Peripherals of Instrument

Different hardware interface circuits and corresponding external device drivers and functional modules are needed to be designed and developed to meet specific product configuration requirements in the traditional instrument development model. However, due to the high coupling degree of instrument software development system architecture and inefficient management and reuse of design resources, the development of instrument devices with different peripherals (sensors, actuators, etc.) based on the same type of instrument microcontroller chip(IMC) still needs a lot of development work, which has been unable to meet the needs of the rapid development of multi-protocol, multi-service, multi-perception and multi-series instrument equipment. This paper presents a dynamic adaptation method of instrument heterogeneous peripherals based on semantic model, which can support the rapid development of product families based on the same core chip but with different peripherals. By using the instrument-specific development kit, the semantic model is customized and instantiated according to the requirement information of heterogeneous peripheral devices. Then the model instance is parsed to obtain the resource adaptation software code of the heterogeneous peripheral. In particular, this method designs the statically compiled adaptation mode and the dynamically running adaptation mode respectively for resource-constrained and resource-rich instrument forms, which can support the rapid development of a family of products with the same core chip but different peripherals as well as the dynamic replacement of peripherals during the use of instrument and equipment. The method we proposed can reduce the product cost and shorten the development cycle of instrument products compared with the traditional development mode.
Automatic formulation and optimization of linear problems from a structured paragraph

Though plenty of research work has been done on natural language processing and linear problem optimization, none of them focused on automatic linear problem formulation from passages and optimizing them at a time. In this research, we developed a problem formulation method using NLTK and developed a completely new linear optimization solver using C# GUI and Python pulp library. We classify the problem with Support Vector Machine(SVM) to identify that the problem is a linear or a non-linear problem. Support Vector Machine(SVM) has an accuracy of 70% due to the small dataset. We extract all the cardinal digits using NLTK Pos_Tagger from the passage. Using the proposed algorithm, we generate all the equations for objective functions and constraints. We also developed software for the solution of the formulated problems. We got an accuracy of 100% for equation generation with our software. Moreover, the proposed system shows an impressive result in problem formulation from our designed structured passage.
The practice of using the fourier transform in processing the results of mathematical modeling of the hydrodynamics of getaways

Abstract The article deals with the actual aspects of practical mathematical modeling of hydro-dynamic processes in the chambers of navigational locks. The use of direct and inverse Fourier transforms has been tested to obtain the representations of non-stationary graphs acceptable for analysis. Cross-sections of the water flow filling the chamber of a typical lock in the Volga-Don shipping channel were used as reference data (VDSC). The control sections in the flow were selected with a qualitatively different hydrodynamic nature of motion. A two-dimensional array of non-stationary data results were decomposed into Fourier series. The resulting graph of the amplitude-frequency spectrum was analyzed by the harmonics forming it. Its amplitude was taken as the criterion for the harmonics’ selection. After zeroing the insignificant harmonics, the inverse Fourier transform was performed. The quality of the data array approximation was controlled by visual overlay of the original graphs on the processed one. In all cases, it was possible to obtain the acceptable approximation results. This created a reliable basis for the scientific analysis and development of engineering measures for the implementation of safe ship passage through gateways. At the end of the article, a number of the data processing specific features are presented, caused by a variety of hydrodynamic features of the flow in various sections.
Big Data Analytics Using Apache Hive to Analyze Health Data

A large volume of data is stored electronically. It is very difficult to measure the total volume of that data. This large amount of data is coming from various sources such as stock exchange, which may generate terabytes of data every day, Facebook, which may take about one petabyte of storage, and internet archives, which may store up to two petabytes of data, etc. So, it is very difficult to manage that data using relational database management systems. With the massive data, reading and writing from and into the drive takes more time. So, the storage and analysis of this massive data has become a big problem. Big data gives the solution for these problems. It specifies the methods to store and analyze the large data sets. This chapter specifies a brief study of big data techniques to analyze these types of data. It includes a wide study of Hadoop characteristics, Hadoop architecture, advantages of big data and big data eco system. Further, this chapter includes a comprehensive study of Apache Hive for executing health-related data and deaths data of U.S. government.
Learning Convex Polyhedra With Margin

We present an improved algorithm for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quasi-properly</i> learning convex polyhedra in the realizable PAC setting from data with a margin. Our learning algorithm constructs a consistent polyhedron as an intersection of about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t \log t$ </tex-math></inline-formula> halfspaces with constant-size margins in time polynomial in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> (where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> is the number of halfspaces forming an optimal polyhedron). We also identify distinct generalizations of the notion of margin from hyperplanes to polyhedra and investigate how they relate geometrically; this result may have ramifications beyond the learning setting.
An HMM-Based PoS Tagger for Old Church Slavonic

Abstract We present a hybrid HMM-based PoS tagger for Old Church Slavonic. The training corpus is a portion of one text, Codex Marianus (40k) annotated with the Universal Dependencies UPOS tags in the UD-PROIEL treebank. We perform a number of experiments in within-domain and out-of-domain settings, in which the remaining part of Codex Marianus serves as a within-domain test set, and Kiev Folia is used as an out-of-domain test set. Analysing by-PoS-class precision and sensitivity in each run, we combine a simple context-free n-gram-based approach and Hidden Markov method (HMM), and added linguistic rules for specific cases such as punctuation and digits. While the model achieves a rather non-impressive accuracy of 81% in in-domain settings, we observe an accuracy of 51% in out-of-domain evaluation, which is comparable to the results of large neural architectures based on pre-trained contextual embeddings.
Impostor GAN: Toward Modeling Social Media User Impersonation with Generative Adversarial Networks

The problem of “fake accounts” on social media has gained a lot of attention recently because of the role they play in generating/propagating misinformation, manipulating opinions and interfering with elections. Among them, “impersonators” or “impostors” are those fake social media accounts that mimic the posts and behavior of a targeted person (e.g., a politician or celebrity) or brand. In this preliminary investigation, we study a GAN-based framework in which an impostor aims to produce realistic social media posts which pass for being created by the target person while the detector aims to identify the posts by the impostors/impersonators. The impostor and the detector are co-trained until an equilibrium is reached, where the impostor is good at mimicking the posts of the target person and the detector is good at identifying the posts of an impostor. Our model allows us to achieve both impersonation and its detection, and also to study how this adversarial scenario might unfold. We applied our method to a Twitter dataset to study its performance. The results demonstrate that our proposed model is promising in generating and detecting impostors’ posts.
Application of the Johnson Problem to Solve Applied Problems

This article discusses the application of Johnson’s algorithm to analyze the work of a volunteer help desk and the work of an online store if the initial data for the algorithm are random numbers from a given interval. For this purpose, a program has been developed for solving the Johnson problem for modeling work, i.e. simulating the processing of applications (orders, calls), written in the Python programming language using IDE – PyCharm 2021.1, Qt Designer and PyQt5, in which the processing time of applications is a random variable from a given interval. The analysis of the results obtained allows us to make recommendations on the number of applications that the service system can process if the processing time is random, but belongs to a given interval, as well as predict the waiting time for applications to arrive.
Gold Price Forecasting using Box-Jenkins Method

In general, the nature of gold that acts as a hedge against inflation and its stable price over the course of the financial crisis has made it a unique commodity. Price forecasts are a must for gold producers, investors and central bank to know the current trends in gold prices. Forecasting the future value of a variable is often done with time series analysis method. This study was conducted to determine the best model for forecasting gold commodity prices as well as forecasting world gold commodity prices in 2018 using Box-Jenkins approach. The data used in this study was obtained from Investing.com from 2015 until 2017. This study shows that ARIMA (1,1,1) is the best model to predict gold commodity prices based on Mean Absolute Percentage Error (MAPE). MAPE value for ARIMA (1,1,1) is 0.02%, where this value proves that forecasting using ARIMA (1,1,1) is the best forecasting because MAPE value is less than 10%.
Feature Selection Method Based on the Intra-Class Redundant Information Ratio

Feature selection is a key step of the classification problem. In this paper, we propose the concept of intra-class redundant information ratio, and use it to measure the importance of redundant items. Based on this, we give a new method of ICRFS. In the experiments, we made the contrast test on 10 data sets. The classification results show that ICRFS has better classification performance than other methods for two classifiers of KNN and SVM.
Study of Establishing a Contamination Control Strategy using a Quality Risk Management for Manufacture of Sterile Products

Manufacturers engaging in Good Manufacturing Practice (GMP) for a sterile product are required to establish a Contamination Control Strategy (CCS) to prevent product contamination. Domestic and overseas regulations including the EU GMP Guideline “Annex.1 Manufacture of Sterile Products” mention the importance of establishing a CCS; however, despite mentioning a risk management method, the Annex does not describe a specific implementation method. Sterile product manufacturers should take measures to prevent product contamination by establishing an appropriate CCS. Therefore, this study presents a method for establishing a CCS strategy, as required by various guidelines, using the quality risk management method presented in ICH Guideline Q9. In this study, when establishing a CCS strategy by applying the quality risk management method, it was possible to confirm factors affecting product contamination through Cause and Effect Analysis, and the degree to which each factor influences product contamination through Failure Mode and Effect Analysis. Based on these results, when establishing product contamination countermeasures, manufacturers can identify the items requiring improvement and evaluate the strength of these measures. Finally, the manufacturers can establish a CCS at the level required by the relevant guidelines. This study is meaningful in providing a method for establishing a CCS that can be applied by sterile product manufacturers in the future by presenting a CCS using the quality risk management method.
Initial Modeling for Smart Farming using Soil Temperature and Humidity

The absence of an increase in the interest of the young population to enter the agricultural sector has an impact on the amount of food production each year. This happens because young people think that the farming profession is not promising for survival. One of the developments that can attract the interest of young people is by utilizing technology such as smart farming. This paper proposes initial modeling before implementing smart farming by analyzing the characteristics of the object to be planted. The purpose of this paper is to optimize plant growth and development to increase the potential of agricultural production according to the surrounding environment. The plants analyzed included eggplant, cherry tomatoes, guava, citrus, and cucumbers. The parameters studied were temperature and soil moisture in plants. The test results found that cherry tomatoes and cucumbers are not suitable if planted in the Surabaya city environment because they require low temperatures as a condition for growing. Meanwhile, eggplant, guava, and citrus plants, if placed in a place exposed to direct sunlight, will cause the soil moisture to decrease, so that the plants do not grow optimally.
Does Generative Adversarial Network (GAN) help in SRAF image generation?

We propose a (Deep Neural Network) DNN based method that applying Generative Adversarial Network (GAN) to the SRAF image generation. We found that though the model with GAN loss may increase distortion, it can generate high fidelity images efficiently and mitigate the overfitting issues that widely exists in all kinds of deep learning methods. We also discuss the necessity of tuning the model structure when GAN loss is adopted.
Fundamentals of Symmetric Key Cryptography

This Chapter introduces/discusses about the fundamental concepts required for the following Chapters. Overall, it consolidates concepts from multiple disciplines into one place.