Reviewerly/exHarmony huggingface dataset:
The Relationship Between the Intensity of Using Instagram and Learning Motivation for Students at Muhammadiyah University of Sidoarjo

This study aims to see whether there is a relationship between the intensity of Instagram use and student motivation. The data collection method in this research uses correlational quantitative methods. The variables of this research are the intensity of the use of Instagram as an independent variable and learning motivation as the dependent variable. The population of this research is all even semester students of the 2019/2020 academic year at Muhammadiyah University of Sidoarjo, totaling 10,182 students. The sample used for the study amounted to 347 subjects using random sampling technique. Data collection techniques in this study used a psychological scale of learning motivation and an intensity scale with a Likert scale model. There is a negative relationship between the intensity of using Instagram and learning motivation for students at the Muhammadiyah University of Sidoarjo. The correlation coefficient value is 0.361 with a significance of 0.000 &lt; from 0.05. Then based on the test results the coefficient of determination shows 0.183 which indicates that the intensity of the use of Instagram provides an effective contribution to learning motivation and the rest is influenced by other variables.
Applications of Cultural Algorithms in Engineering

Since the emergence of CAs, they have been successfully extended and widely employed to solve a wide variety of problems in different engineering disciplines, including civil engineering, mechanical engineering, chemical engineering, electrical engineering, and industrial engineering. This chapter focuses on the applications of CAs in these engineering disciplines and provides the overall mathematical formulations for different optimisation problems.
Machine Learning Fundamentals: Unsupervised Learning part 2 - Data &amp;amp; AI Reskilling Seminar slides

These slides are part of the Data & AI Reskilling seminar that presents ML and AI for banking. This lecture presents machine learning methods for dimensionality reduction
Product Recommendation System Using Machine Learning

Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems – content-based recommending and collaborative filtering (CF). This study focuses on improving the performance of recommender systems by using data mining techniques. This paper proposes an SVM based recommender system. We conducted an experiment to evaluate the proposed model on the each type of product data set such as Grocery, Beauty, Cold drinks, watches, lockers, Gourmet Foods etc. and compared them with the traditional algorithm. The results show the proposed model is improvement in making recommendations.
Quality

In the previous chapter, we discussed the importance of hiring good engineers and how to find them. Furthermore, we outlined the attributes of good and bad software engineers, and gave practical tips on how to identify them. Now it is time to talk about how you ensure that your good hires do good, quality work. That is, we will discuss how to create a quality-centric environment that allows people to execute to the best of their abilities.
Shadow processing with color model adjustment and texture analysis

Object detection is a fundamental task in computer vision. In most cases, this motive is often corrupted by the shadows in an image. These scenarios consequence a great need of shadow processing. Along with this, the method of detecting and removing shadow is used to improve computer vision applications such as image segmentation, object recognition and tracking. The prime objective of this paper is to detect and remove shadow from an image by analyzing color models and background texture pattern. Initially, shadow boundaries are detected from a given foreground region by adjusting color models. Then the similarity between texture features of shadow and neighboring non-shadow region is measured. Finally, based on these similarities, texture pattern of non-shadow region is projected onto the shadow region to get a shadow free image. However, it is noteworthy that Local Binary Pattern (LBP) is used here to measure the texture feature as it is simple and efficient. In addition, this simple methodology has achieved a good detection rate of 87.81 % and presented a high PSNR (22.41), SSIM (0.9432) value and low RMSE (3.48) value after shadow removal.
Establishing a Knowledge Base of an Expert System for Criminal Investigation

In the information era, knowledge is becoming increasingly significant for all industries, especially criminal investigation that deeply relies on intelligence and strategies. Therefore, there is an urgent need for effective management and utilization of criminal investigation knowledge. As an important branch of knowledge engineering, the expert system can simulate the thinking pattern of an expert, proposing strategies and solutions based on the knowledge stored in the knowledge base. A crucial step in building the expert system is to construct the knowledge base, which determines the function and capability of the expert system. This paper establishes a practical knowledge base for criminal investigation, combining the technologies of cloud computing with traditional method of manual entry to acquire and process knowledge. The knowledge base covers data information and expert knowledge with detailed classification of rules and cases, providing answers through comparison and reasoning. The knowledge becomes more accurate and reliable after repeated inspection and verification by human experts.
SoCS 2013 Organization

List of organizers of the Sixth International Symposium on Combinatorial Search.
A Bit-Plane Encryption Algorithm for Rgb Image Based on Modular Negabinary Code and Chaotic System

This paper proposes a new color image encryption algorithm based on modular negabinary code (MNBC-IEA), which treats a color pixel as one 24-bit unsigned integer rather than three 8-bit unsigned integers, and it makes the 24-bit-planes dependent on each other. We first provide a new coding scheme of unsigned integers called Modular NegaBinary Code (MNBC), and we construct its encoding and decoding algorithm. Next, we use MNBC to design the substitution algorithm for color image encryption. MNBC-IEA consists of the enhanced MNBC, a random cyclic shift (RCS), and a diffusion procedure. The RCS procedure is composed of the RCSs in three directions, and it treats a color image as a cuboid. Hence, the permutation process can cause the 24-bit-planes to affect each other in addition to scrambling the bit positions. The enhanced MNBC can change the grayscale values via a process that encodes a 28-bit unsigned integer consisting of a 24-bit pixel value and a 4-bit key into the MNBC. The diffusion procedure includes exclusive OR and modulo arithmetic such that plaintext pixels, ciphertext pixels, and keystreams are related to each other. The final experiments indicate that our algorithm can encrypt a color image into a random binary sequence and resist various statistical, differential, and brute-force attacks.
Almost Optimal Proper Learning and Testing Polynomials

We give the first almost optimal polynomial-time proper learning algorithm of Boolean sparse multivariate polynomial under the uniform distribution. For s-sparse polynomial over n variables and $$\epsilon =1/s^\beta $$ , $$\beta >1$$ , our algorithm makes $$q_U=\left( \frac{s}{\epsilon }\right) ^{\frac{\log \beta }{\beta }+O(\frac{1}{\beta })}+ \tilde{O}\left( s\right) \left( \log \frac{1}{\epsilon }\right) \log n$$ queries. Notice that our query complexity is sublinear in $$1/\epsilon $$ and almost linear in s. All previous algorithms have query complexity at least quadratic in s and linear in $$1/\epsilon $$ . We then prove the almost tight lower bound $$q_L=\left( \frac{s}{\epsilon }\right) ^{\frac{\log \beta }{\beta }+{\varOmega }(\frac{1}{\beta })}+ {\varOmega }\left( s\right) \left( \log \frac{1}{\epsilon }\right) \log n,$$ Applying the reduction in [9] with the above algorithm, we give the first almost optimal polynomial-time tester for s-sparse polynomial. Our tester, for $$\beta >3.404$$ , makes $$\tilde{O}\left( \frac{s}{\epsilon }\right) $$ queries.
A new three-hull unmanned ship is designed by integrating lateral power and ship

This project designs a new type of three-hull unmanned ship with high maneuverability, which is characterized by a propeller under each of its two side bodies, and the propeller behind the hull is canceled at the same time. Compared with traditional ships, the three-hull unmanned ship designed in this project can obtain larger torque and better steering ability based on its structural characteristics on the premise of using the same type of propulsion, and the propulsors on both sides have the same power. Under the condition of reverse rotation, the unmanned ship can achieve in-situ steering. Compared with other ships, the propeller of the trimaran is not disturbed by the hull, so as to obtain a trimaran unmanned ship with high maneuverability.
DIGITAL TOOLS FOR COMPUTER PRESENTATIONS USED IN TEACHERS’ TRAINING

The following work is devoted to the appropriate digital tools for interactive computer presentations that could be used in the pedagogical specialists’ training. It is known that in last two years educational systems around the world have switched to online learning. This has led to a need for retraining of pedagogical specialists in the field of preparation of e-learning content. This is the main reason why it is important for teachers to know modern digital tools that provide various services related to cloud storage, video conferencing, creating presentations, building web content, creating mind maps, collecting and providing information, creating digital notebooks, working with a whiteboard, creating tests for control and assessment of knowledge and much more. This work presents two digital tools for innovative and attractive computer presentations and their practical application in teachers’ training. The described digital tools are used in the teachers’ training in bachelor’s and master’s programs of FMI at the University of Plovdiv „Paisii Hilendarski“, as well as in various qualification courses conducted by the Department of Qualification and Professional Development of pedagogical specialists at the same university.
The Comprehensiveness of Thinking

Looking back over the last hundred years of the field of cognitive research, we can see that the study of thinking has followed a winding road. Nevertheless, just as the ancient Chinese poem goes, “After endless mountains and rivers that leave doubt whether there is a path out, suddenly one encounters the shade of a willow, bright flowers, and a lovely village”.
Hardware Implementation of Low Complexity High-speed Perceptron Block

Perceptron is the basic computation unit of neural network architectures. This work proposes a resource-efficient and fast hardware for perceptron and Multi-layer Perceptron (MLP) network. The inner product computation unit and activation function unit is designed using Offset Binary Coding (OBC) and Co-ordinate Rotation Digital Computer (CORDIC) respectively. The proposed hardware is implemented on Field Programmable Logic Array (FPGA) and synthesized on 65 nm Application Specific Integrated Chips (ASIC). It achieved a speed-up of at least $21 \times$ as compared to software. The total area and power consumed was $0.085662 mm^{2}$ and 2.28 mW respectively @200 MHz.
Integrating Agile Development Approaches and Business Analysis Foundational Knowledge into a Project Management Course

The project management profession has evolved significantly in the past few years and a new standard that reflects the full range of changes has been introduced. In this paper we address our approach to update the curriculum in project management. We made structural changes to the traditional project management Team Project to introduce business analysis core competencies to students. The group project integrates predictive plan-based and agile development approaches and introduces business analysis and requirements elicitation topics. In this paper we introduce the changes in the new standards and illustrate implementation using an exemplar project. Our conclusions of job task analysis statements reveal that students mastered traditional, hybrid, adaptive development approaches as business analysis core concepts successfully.
Data Enhancement for Generating Counter Network Indicator Chart Based on Auxiliary Classification

When using generative adversarial network to enhance the fault data of small sample pumping units, the traditional network generates poor sample quality under the condition of high sample similarity. Moreover, in the process of training, the network also extracts the features that are not useful in the sample, resulting in inaccurate extracted information. To solve these problems, we propose a data enhancement method based on the Auxiliary Classification Generative Adversarial Network (ACGAN). First, we add the Squeeze-and-congestion Networks (SEnet) module into the network. During the training, we can extract more useful features from the diagram, and suppress the useless features. Secondly, the loss function - focusing loss function (FLoss) in the network is added to make the network have better affinity for training samples with high similarity. Experiments verify that this method improves the quality and diversity of generated samples.
Mould Squares in Simplicial Sets

In this chapter we embark on the study of the effective Kan fibrations in simplicial sets defined using the dominance and symmetric Moore structure on simplicial sets that we established in the previous chapters. The main result of this chapter is that these effective Kan fibrations are cofibrantly generated by a small triple category.
Penerapan Metode Read Continued pada Teks Eksplanasi Siswa Kelas VIII MTS Hasyim Asy'ari Kota Batu

The purpose of this study was to: (1) Apply the read continued method to the explanatory text of the VIII grade students of MTs. Hasyim Asy'ari Batu City. The research method used is a qualitative approach. The results of the study were: (1) The ability to read the explanatory text of class VIII MTs students. Hasyim Asy'ari Kota Batu before applying the read continued method; (2) The ability to read the explanatory text of class VIII MTs students. Hasyim Asy'ari Kota Batu after the read continued method was applied; (3) Supporting and inhibiting factors for the application of the read continued method. The conclusion of this study is the ability to read the explanatory text of class VIII MTs students. Hasyim Asy'ari Kota Batu after the read continued method was applied: (1) the students were able to concentrate more; (2) students are able to be calm in the classroom.
Systematic Monotonicity and Consistency for Adversarial Natural Language Inference

Natural Language Inference is a fundamental task required for understanding natural language. With the introduction of large Natural Language Inference (NLI) benchmark datasets such as SNLI and MultiNLI, NLI has seen an uptake in models achieving near-human accuracy. Deeper analyses through adversarial methods performed on these models however have cast doubts on their ability to actually understand the inference process. In this work, we attempt to define a principled way to generate adversarial attacks based on monotonic reasoning and consistency to examine their language understanding abilities. We show that the language models trained for general tasks have a poor understanding of monotonic reasoning. For this purpose, we provide methods to generate an adversarial dataset from any NLI dataset based on monotonicity and consistency principles and conduct extensive experiments to support our hypothesis. Our adversarial datasets preserve these crucial aspects of monotonicity, consistency and semantic similarity and are still able to fool a model finetuned on SNLI 79% of the time while preserving semantic similarity to a much greater extent than previous methods.
Several Adaptable Robot engagements planning with blending Conflict and plan resolution

When it comes to multi-robot planning, the objective is to compute plans for a group of robots to fulfill their personal objectives while keeping the total cost to a minimum. Each plan is represented as a series of activities. A team of agents may work together to design strategies and coordinate efforts in order to attain shared objectives. In order to reduce overall planning complexity, methods are available for breaking down planning challenges into discrete tasks and distributing the plan synthesis process among several tasks. Our goal is to reduce the amount of jobs that fail at the conclusion of the operating horizon to a bare skeleton crew minimum. Decoupling the basic computing concerns of particular choice over unpredictability and multi-agent cooperation and solving them in a systematic order is the focus of this study. Bottom level guidelines are computed for individual agents using optimization techniques and tree search whereas the top layer handles disputes among specific plans to produce a proper multi-agent allocation. A novel technique, Speculative Conflict-Based Distribution, was devised that is both optimal in anticipation and completed if a few criteria are satisfied.The SCA algorithm is computationally efficient enough in reality to allow for the online interleaving of planning and execution. If success is measured by task completion, SCA regularly beats many baseline approaches and exhibits good competitive performance when measured against an oracle with full look ahead, among other metrics. It also scales effectively in terms of the amount of tasks and agents assigned to each task.
HLS-based dataflow hardware architecture for Support Vector Machine in FPGA

Implementing fast and accurate Support Vector Machine (SVM) classifiers in embedded systems with limited compute and memory capacity and in applications with real-time constraints, such as continuous medical monitoring for anomaly detection, can be challenging and calls for low cost, low power and resource efficient hardware accelerators. In this paper, we propose a flexible FPGA-based SVM accelerator highly optimized through a dataflow architecture. Thanks to High Level Synthesis (HLS) and the dataflow method, our design is scalable and can be used for large data dimensions when there is limited on-chip memory. The hardware parallelism is adjustable and can be specified according to the available FPGA resources. The performance of different SVM kernels are evaluated in hardware. In addition, an efficient fixed-point implementation is proposed to improve the speed. We compared our design with recent SVM accelerators and achieved a minimum of $10\times$ speed-up compared to other HLS-based and $4.4\times$ compared to HDL-based designs.
Research on Practical Teaching Evaluation Model Based on Organization and End-User Calculation

How to evaluate the teaching quality of teachers in practical teaching has become a key issue in the development of online teaching. This paper uses data mining technology and neural network to design evaluation algorithms to build a practical teaching evaluation system based on organization and end-user calculations. As the training-test ratio increases, the accuracy rate continues to improve, reaching a maximum of 92.99% when all data is training. It can be seen that the size of the training set has an important impact on the accuracy of classification. The larger the training set, the higher the accuracy. This paper selects 4004 pieces of sample data, the first 2002 pieces are training sample data, and the last 2002 pieces are test sample data. Under the default parameters of RBF, the accuracy rate is only 87.31%. The prediction results and expert prediction results after network training, the simulation prediction results of the 5 test sets.
Report on Blockchain Technology &amp;amp; Legitimacy

The report investigates the role of legitimacy in blockchain systems from descriptive, conceptual, and normative perspectives. It summarizes the discussions and provides recommendations concerning the role of legitimacy in blockchain systems. The structure of the report is as follows: the introduction highlights the importance of addressing the endogenous legitimacy of blockchain systems, presents the concepts of blockchain technology, blockchain systems and distributed governance, and outlines the academic building blocks behind the notion of legitimacy. The second section is further split into three subsections. The first and second subsections review the academic literature on legitimacy in centralized and decentered (or polycentric) systems, respectively. As much has been written about legitimacy, our literature selection aims to explore legitimacy descriptively, conceptually, and normatively in light of Hirschman’s exit, voice, and loyalty. The third subsection analyzes the extent to which blockchain systems are, in fact, polycentric. The third section rethinks the notion of legitimacy in blockchain systems. It presents the views of renowned blockchain practitioners and it explores legitimacy in light of specific characteristics of blockchain systems, including Schelling points, forking, MEV, credible neutrality, community participation, politics, and trust and confidence. This section sheds light on our preliminary hypotheses, namely how (1) loyalty is indicative of legitimacy, (2) legitimacy is a prerequisite for loyalty, (3) previous academic literature on legitimacy cannot be readily transposed to understand these novel socio-technical systems.
Data mining-based analysis of the human activity in healthy subjects using smart phones

Human-activity-recognition (HAR) ascertains the nature of interaction with the surrounding environment. The present work quantifies the activities such as those of walking (WA), walking-upstairs (WU), walking-downstairs (WD), sitting (SI), standing (ST) and laying (LA) of 30 healthy subjects of age 19-48 years using accelerometer and gyroscope sensors embedded in Samsung Galaxy-S2 smartphone using data mining methods. Support vector machine (SVM), multiple layer perceptron (MLP), decision tree (DT), extra tree (ET), K-nearest neighbour (KNN), random forest (RF) and gradient boosting machine (GBM) techniques are used with and without linear discriminant analysis (LDA) for dimension reduction. The accuracy is seen to be higher with LDA. SVM (with C = 10, gamma = 0.001 with RBF kernel) provided the highest accuracy for both cases (SVM without LDA = SVM with LDA = 96%). However, the highest variation based on LDA was seen in DT (DT without LDA = 85% and DT with LDA = 95%). Such approaches can be extended in rehabilitative applications and virtual reality in the near future.
Quantum image chaos encryption method based on multi-level wavelet transform and Fourier transform

In this paper, a new quantum image compression and encryption algorithm is proposed by combining quantum discrete cosine transform and hyperchaotic system. The algorithm performs the quantum discrete cosine transform on the quantum grayscale image, gets the spectrum of quantum discrete cosine transform, then compresses the quantum grayscale image, and finally uses the hyperchaotic system to encrypt the compressed quantum grayscale image to get the ciphertext image. Multi-spectrum, continuous images make it difficult and burdensome to store, process and transmit image data. Image compression technology takes advantage of the redundancy of image information and the defects of human vision and other characteristics to solve the problem of image information redundancy. The combination of quantum image encryption and quantum image compression not only improves the security of quantum images but also saves network transmission bandwidth.