Reviewerly/exHarmony huggingface dataset:
Endoscope blood vessel image enhancement method based on adaptive nonlinear transformation

The spectral absorption characteristics of hemoglobin determine that the contrast between R and B components in the white light endoscopic blood vessel image is poor, and the blood vessel features in the G component are the clearest and the contrast is good. Based on this feature, this paper proposes to use the G-component image to perform nonlinear stretching to obtain the stretched image, and subtract the original image from the stretched image to obtain the G-component high-frequency detail image containing blood vessel feature information; Then, using the high-frequency detail image to perform unsharp mask processing on the R, G, and B components of the original image, respectively, to obtain a blood vessel contrast-enhanced image; In order not to cause grayscale dispersion in the transition zone of the blood vessel edge during stretching, Performance simulation experiments are carried out for endoscopic images of fundus and oral cavity. The results show that the proposed algorithm not only improves the image contrast, but also has a better enhancement effect on small blood vessels with inconspicuous original features. By comparing with the performance of Spectra B and the method of literature [6], the average gradient value of the algorithm in this paper is increased by about 300%, the information entropy value is increased by about 30%, and the DV-BV value is increased by about 75%.
Scene Text Detection Based on Expanding the Text Center Region for Bilingual Tibetan-Chinese

Scene text detection is an important research branch of artificial intelligence technology whose goal is to locate text in scene images. In the Tibetan areas of China, scene images containing both Tibetan and Chinese texts are ubiquitous. Thus, detecting bilingual Tibetan-Chinese scene texts is important in promoting intelligent applications for minority languages. In this study, a scene text detection database for bilingual Tibetan-Chinese is constructed using a manually labeled method and an automatic synthesis method, and a text detection method is proposed. First, we predict a text rectangular region and the text center region for each text instance and simultaneously learned the expansion distance of the text center region. Second, based on the classification score of the text center region and the text rectangular region, we obtain the final confidence of each text instance and then filter out the text center region with lower confidence. Third, through the learned expansion distance, the full-text instance from the remaining text center region is recovered. The results show that our method obtains good detection performance; it achieves an accuracy of up to 75.47% during the text detection phase, laying the foundation for scene text recognition in the subsequent step.
Spectral Analysis of Neural Network Long-Term Predictions

Long-term predictions of time series is a task with multiple applications. Exploiting neural networks for such problem has not been studied enough for financial purposes. An empirical study of specific cases with real financial multivariate time series and data augmentation was performed. The results are compared for different neural network models and two datasets using standard and customized spectral evaluation metrics.
YISHAN: Managing Large-scale Cloud Database Instances via Machine Learning

Efficiently managing database instances over cloud-scale clusters is significant for increasing service quality and reducing operational cost, especially confronting the growing cluster size and heterogeneous application services. Alibaba Cloud provides a large-scale Relational Database Service (RDS) for millions of users including enterprises from start-ups to large international corporations. To manage tremendous amount of RDS instances in the Cloud with the goal of reducing cost while guaranteeing service level agreement(SLA), YISHAN, an intelligent database instance management system, is designed to dynamically manage the placement of instances using machine learning techniques. YISHAN collects historical performance data to analyze patterns of the resource utilization of instances and hosts. By learning “good packings” in which instances colocate harmoniously, YISHAN is able to optimize the instances placement to provide better quality of service and improve the efficiency of CPU, memory, and disk resources. We deploy and run YISHAN in Alibaba Cloud RDS. The running logs show that YISHAN successfully saves 17% of the resources in hosts and efficiently reduces the burdens and crash risks of RDS instances.
Tor Network Detection By Using Machine Learning And Artificial Neural Network

The Internet is a virtual world where everyone can express themselves as much as they wish and perform the operations they want. In this virtual world, some users want to experience the internet without giving their identity for certain reasons. The concept of an anonymous network has emerged so that they can use the internet without revealing their identity. The Tor project is a product that provides anonymous communication on the Internet without revealing users&#x2019; identities. In this project, we aimed to determine whether network traffic is the TOR network by using machine learning and artificial neural networks. With the dataset we have, we first performed data analysis and gained more information about the data set. Categorical values were assigned to numerical values to learn the dataset. After converting the categorical data to numerical data, normalization is applied to the data set and all features are taken between -1 and 1. It was estimated whether the future traffic was TOR by learning the past data by using K Nearest Neighbor, Naive Bayes Classifiers, Random Decision Forest, Logistic Regression, Support Vector Machine, one of the machine learning classification algorithms. In addition, artificial neural networks were used. After each algorithm, confusion matrix, precision, recall, and F1-score values, which are among the model evaluation tools, were calculated, and compared which model performed better for our dataset.
A Survey: Crowds Detection Method on Public Transportation

Face recognition is a computer technology being used in a variety of applications that identifies human faces in digital images. At this time, face recognition can be used to find out how crowded a public transport is. Face recognition can be used to calculate how many people are in a public transport. Nowadays, we have been assisted by the existence of Closed-Circuit Television (CCTV). The video, images from the CCTV footage can be used to detect crowds in a public transport. Our aim in making this paper is to compare which method is most suitable for use in detecting crowds in a public transport. The writer used various different research papers in the period of 2015–2021. From our analysis, there are many methods that can be used to check the crowd in a public transport, such as CNN, RCNN, YOLO, Viola Jones, and still many more. From our research, we found that the CNN method has the highest accuracy rate than the other methods.
Consensus Algorithms in Blockchain Technology

Many industries implement the blockchain for their application as it is a distributed ledger with a trust. The first application of blockchain is Bitcoin. Depending on the applicability of blockchain we have to consider different parameters and accordingly, we have to select the consensus algorithm. Consensus algorithm is the crucial part while designing any blockchain for the desired application. To confirm any transaction different consensus algorithms are used. In this paper, we will discuss different consensus algorithms. In addition to this, we will compare different algorithms concerning different parameters like time required to confirm the transaction, throughput, network used for the implementation. This article summarizes the factors to be considered while implementing the blockchain.
On robust strong‐non‐interferent low‐latency multiplications

The overarching goal of this work is to present new theoretical and practical tools to implement r o b u s t − t−probing security. In this work, a low-latency multiplication gadget that is secure against probing attacks that exploit logic glitches in the circuit is presented. The gadget is the first of its kind to present a 1-cycle input-to-output latency while belonging to the class of probing security by optimized composition gadgets [6]. In particular, the authors show that it is possible to construct robust- t-strong-non-interferent gadgets without compromising on latency with a moderate increase in area. The authors provide a theoretical proof for the robustness of the gadget and show that, for t ≤ 4, the amount of randomness required can even be reduced without compromising on robustness.
Stepwise Refactoring Tools

As part of software change tasks, software developers frequently make repetitive and error-prone code changes. Many of these changes can be automated by refactoring tools that are available in mainstream development environments, but developers seldom choose to use them, in large part due to the all-or-nothing approach of the tools. In this paper, we propose an alternative interaction model in which a refactoring tool instead generates a set of code change entities that the developer can inspect, edit, discard, or apply at will. Between applications, the tool supports manual edits or even starting a new refactoring, thus enabling stepwise and recursive refactoring in a way current tools can not. Our model addresses multiple usability problems with refactoring tools. In this paper, we motivate and describe the proposed model and discuss challenges related to its implementation.
THEMIS: Context-Sensitive Similarity Analysis for Wound Imagery Using Mathematical Model of Meaning

Even when generated with the same weapon, a wound's appearance would be affected by its use and the assaulter's physique. In contrast, even if wounds look similar in shape and/or color, they could have a different wounding history. In this work, we strive to extract the features of shape and color from a given wound image and build on the mathematical model of meaning to create the associated semantics of the criminal act. We demonstrate that a system called THEMIS (theoretical estimation of the meaning of insults) provides a context-sensitive visual similarity analysis for wound imagery in computational forensics. The THEMIS system can help forensic doctors and e-court stakeholders potentially determine important aspects of a case through a comparison with wounds of corpses in past cases, in a way that is not currently possible.
Just How Hard Are Rotations of $$\mathbb {Z}^n$$? Algorithms and Cryptography with the Simplest Lattice

We study the computational problem of finding a shortest non-zero vector in a rotation of $$\mathbb {Z}^n$$ , which we call $$\mathbb {Z}$$ SVP. It has been a long-standing open problem to determine if a polynomial-time algorithm for $$\mathbb {Z}$$ SVP exists, and there is by now a beautiful line of work showing how to solve it efficiently in certain very special cases. However, despite all of this work, the fastest known algorithm that is proven to solve $$\mathbb {Z}$$ SVP is still simply the fastest known algorithm for solving SVP (i.e., the problem of finding shortest non-zero vectors in arbitrary lattices), which runs in $$2^{n + o(n)}$$ time. We therefore set aside the (perhaps impossible) goal of finding an efficient algorithm for $$\mathbb {Z}$$ SVP and instead ask what else we can say about the problem. E.g., can we find any non-trivial speedup over the best known SVP algorithm? And, if $$\mathbb {Z}$$ SVP actually is hard, then what consequences would follow? Our results are as follows.
Central Bank Digital Currencies under Blockchain Technology: Discussion of Digital Bank Runs and Design Principles

The rising of digital currencies and the technology behind them, "blockchain," draws much attention in many areas.According to the 2021 report of BIS, 86% of the participant central banks are actively researching the potential in this technology.This study attempts to shed light on possible expansion in monetary policy tools by the issuance of CBDC.Thus, it suggests a partial substitution of paper currency.Four design principles have been stated and discussed for a monetary system that includes CBDC and possible solutions regarding the digital bank run.
Fuzzy cognitive modeling of agricultural land productivity in the context of food security

Abstract The article deals with the problems of fuzzy cognitive modeling and evaluation of the productivity of reclaimed soils, taking into account the combination of natural-climatic, soil and environmental factors. To construct a fuzzy model, the parameters to be modeled were the coefficient of bioclimatic productivity with a range of variation of 0.5-1.5, and the yield of grain crops, which varied within 10…45 dt/ha for different natural and climatic zones. The theoretical basis for the development of a model of land productivity is the theory of fuzzy inference based on the fuzzy-multiple approach. The main stages of fuzzy modeling using the Mamdani algorithm in interactive mode are presented. An algorithmic representation of the dependence of the integral indicator of the productivity of agricultural land on the value of the yield of grain crops, and the coefficient of bioclimatic productivity is obtained. The constructed fuzzy model allows to obtain estimates of the generalized indicator of agricultural land productivity based on the yield values for the range of values of the bioclimatic coefficient. The constructed model can be used as a part of a system for predicting the level of food security.
Measuring the Symmetry of Digital Ink Chinese Characters Handwritten by CFL Beginners Based on Knowledge Base

Symmetry is a structural feature of Chinese characters which influences the aesthetics of the whole character. Beginners to Chinese as a foreign language (CFL) often tend to write asymmetrical characters due to insufficient knowledge of Chinese character orthography and a lack of skill in the strokes. Symmetry deviations in the handwriting characters are usually assessed manually. However, this task is time-consuming and somewhat subjective, with potentially inconsistent results when evaluated by different people. The aim of this paper is to propose a bilateral symmetry measurement approach that uses the center of gravity of Chinese characters (CGCC) and convex hull vertices, both of which are obtained by digital ink technology, to construct the symmetry axis (SA) and detect symmetry point pairs (SPPs) of Chinese characters, computing the symmetry score (SS) of Chinese characters from the horizontal and vertical symmetry of SPPs. In addition, a symmetry information knowledge base is constructed to label the SS of standard reference characters and to measure the symmetry of handwritten characters by comparing the differences between the SS of them and that of the reference characters in the knowledge base. Experiments with digital ink character data from CFL beginners have proven that the proposed method is effective.
TangibleTouch: A Toolkit for Designing Surface-based Gestures for Tangible Interfaces

This pictorial introduces TangibleTouch, a toolkit to design and build interactive tangible cubes using capacitive sensing. This toolkit enables designers to quickly prototype and explore tangible cubes with exchangeable capacitive panels that allows touch-based gestures and interactions that can be used for tangible input in VR, AR, or physical computing. We introduce a design space for TangibleTouch, and present the technical implementation, fabrication approaches, and software support for designers. We evaluate our toolkit by demonstrating its use and application for 3 different case studies: a media controller, a platform game, and a 3D model inspection tool. The contribution of our work is a novel toolkit method for constructing and fabricating a cube-based interactive tangible user interface.
A Simple Heuristic for Expressing a Truth Table as a Quadratic Pseudo-Boolean Function

Quadratic pseudo-Boolean functions (i.e., quadratic functions with real-valued coefficients and two-valued variables) are the native input to quantum annealers and also a common problem format to optimize with the QAOA algorithm on circuitmodel quantum computers. In both cases, large, complex optimization problems can be expressed in terms of combinations of simpler problems. A key challenge in problem set-up lies in finding the coefficients for the constituent sub-problems. These need to be chosen such that the function is minimized on valid inputs and is higher elsewhere. One difficulty is that it is not possible in general to solve for a sub-problem’s coefficients without introducing ancillary variables, the number of which and their corresponding per-row values being unknown a priori. These unknowns lead to solving for a sub-problem’s coefficients being an NP-complete problem.In this paper we present a simple heuristic that considers a prioritized subset of the exponential number of possibilities for each per-row ancillary variable. Using this heuristic, coefficients can be found substantially faster than would be possible using a brute-force search and with less software complexity than is required by prior approaches.
Face recognition based on sparse coding using support vector machine classifier

In this paper, a system for face detection based on the generalized BOW method is proposed. We have utilized the space pyramid matching (SPM) method to overcome the neglected problem of space order of BOW. In the feature extraction stage, we have used SIFT method which is resistant against local variations. Sparse presentations usually are linearly separable; hence in the proposed system, we have utilized the sparse codding method in the feature learning stage. In the polling stage, we have used maximum polling operation to reach a unified vector from multiple descriptor vectors. Finally, a support vector machine classifier is used to classify face descriptor vectors. Simulation results show high accuracy of classification (ACC=0.9952) and its resistivity against previous methods.
Ripensare la ricerca italiana in linguistica educativa

This is not a research essay, it is a survey of the characteristics of present-day research essays in Italian educational linguistics. Many weak points are pointed out, most of which concern ‘borders’, such as the fuzzy epistemological borders between educational linguistics and other scientific areas; the frequent overlapping of different research typologies, which require different methodologies and have different rhetorical structures; confusion as far as implied readers are concerned. The consequence is that sometimes edulinguistic research allows amateurish trips outside educational linguistics, and allows for non educational linguists to take amateurish trips (and sometimes jobs) in the field of educational linguistics. A tentative list of parameters that can be used by referees, editors and evaluators is provided, in order to define the quality of studies before they are accepted in journals and conferences.
Keeping the Momentum: Driving Continuous Improvement After the Large-Scale Agile Transformation

The Scaled Agile Framework (SAFe) is currently the most popular framework to scale agile development to large projects and organisations. An organisational transformation to SAFe is usually driven by a Lean-Agile Centre of Excellence (LACE). What happens to the LACE after the initial transformation is over? How does the organisation keep improving? In this single-case study we investigated how the volunteer-driven LACE in a Nordic bank, Nordea, drives continuous improvement long after the organisation’s transition to SAFe. We collected data by 10 semi-structured interviews and several observations. We found that the LACE at Nordea drives continuous improvements by working in a Scrum-like fashion: it uses Product Owners, maintains a backlog of improvement features, works in sub-teams to identify and solve issues, and meets once a week to coordinate and share between the sub-teams. The LACE consists of volunteers, which is an advantage as changes are identified and implemented by the same practitioners who experience the need for them. However, this volunteering model is not without challenges: the LACE lacks the formal mandate to implement the needed changes and other work takes priority for the participants.
A Proactive Management Assistant Chatbot for Software Engineering Teams: Prototype and Preliminary Evaluation

This work presents a prototype and preliminary evaluation experiments of a management conversational assistant for software engineering teams. Such teams commonly rely on text chat for communications using specialized collaboration platforms such as Slack or Microsoft Teams. A conversational assistant integrated into these platforms can help in several tasks related to project and team management, thus benefiting both managers and software developers. The proposed design of the conversational assistant is described in terms of its architectural design and interaction design, and the important design decisions are explained. Preliminary evaluation shows that the proposed chatbot can be used effectively to interact with users reactively and proactively over public and private channels. However, for better results, a specific intent classifier should be used for each combination of user roles and channel types. In addition, interacting to messages with unspecified target over public channels is affected by the relatively limited intent classifier accuracy and still requires improvement.
Research on Teaching Management Based on Smart Campus Platform and Smart Classroom

With the development of educational informationization, "wisdom" has been widely used in the field of education, among which "smart campus" is particularly prominent and has become the product of economic globalization, technological innovation and knowledge explosion. As a new form of new teaching environment construction under the background of educational informationization, the construction of smart classroom can realize the creation and accumulation of teaching resources, provide a hardware platform for the new intelligent teaching mode, realize the timely interaction between teachers and students, and provide environmental support for the implementation of integrated teaching methods in colleges and universities. According to the requirements of teaching management in colleges and universities, this paper analyzes the control and management functions of smart classroom, and focuses on the teacher control module, smart classroom system, campus payment system, visual teaching design and intelligent control module of smart classroom. Suggestions on intelligent control hardware environment of smart classroom are put forward, and the intelligent control management framework mode of smart classroom is established, which provides reference for the construction of smart classroom.