Reviewerly/exHarmony huggingface dataset:
Digital Computer Modulation Signal Classification Based on Neural Network

Computer is one of the indispensable tools in the human world, and human needs for it are increasing, so the emergence and application of more advanced computers are needed. The current computers do not respond intelligently, and it is difficult to meet people’s needs for information processing in the era of big data. In order to solve these problems, this paper proposes the application of a neural network-based data classification algorithm in computers, aiming to study the practical application of the algorithm in computers. The research method of this paper is to introduce the BP neural network, select the appropriate method of classification features, and then study the data classification algorithm. The function of the research method is to compare the classification error and convergence speed of the BP network composed of different hidden layer nodes, to study whether a certain feature item of the data exists and the difference in the amount of information classification of the entire document, and to select high efficiency, accuracy, and scalability algorithm. This paper compares the forward reasoning time of the model before and after cutting through experiments based on neural network model design, algorithm design, and man-machine dialogue model design. The results show that, in terms of computing speed, the adaptive model compression method based on the accuracy and redundancy ratio compresses the model after the forward reasoning time is greatly reduced, and the reasoning time becomes 35% of the original, and in terms of calculation accuracy, the absolute error after using the SOM method in this article has not reached 0.5.
An RPG Game Design for English Learning using ADDIE Methods

Technology makes easier to study English language easier by social media, websites, books, or even seminars. RPG game is a type of game that tell the story and information by virtual and interactive path. Most students that struggling learning English have faced complexity and difficulty in learning them. This study aims to create a RPG game that can help learning English. The method used for this study is ADDIE (Analysis, Design, Development, Implement, Evaluate) method and the software required for this study are Unity and Visual Studio 2019. ADDIE method is a learning model that instructional designers and training developers have used to create methods of learning effectiveness learning, this learning model is an acronym for a five-step process which are: Analysis, Design, Development, Implementation, and evaluation. And therefore the software used to develop the game are Unity. The result of this research is a RPG game that can be used for learning English. With this RPG game, it is hoped that the game can help with students who struggle learning English.
MEASURING DIGITAL LITERACY SKILLS AMONG STUDENTS IN SENIOR HIGH SCHOOL

Various digital-based learning media strongly support the quality of online learning. Digital media is essential in the learning process because it can package the material into more contextual, interesting audiovisual, reduces verbalism, and is more interactive. The primary purpose of this research was to measure the differences in digital literacy by comparing the digital learning media and PowerPoint media in online learning. The design of this study was a pretest-posttest group with a quasi-experimental method. Overall, 56 students in a senior high school in Senior High School 2 Percut Sei Tuan, Medan City, Sumatera Utara Province, served as participants in this study. Data were collected by questionnaires and structured interviews for digital literacy. Data analyses were mixed with the quantitative and qualitative methods. Quantitative data were analyzed statistically through independent samples t-test, and qualitative data were analyzed descriptively. Digital literacy data were analyzed using SPSS version 24 software. The results showed that the students' digital literacy using digital learning media was superior to those prepared using PowerPoint. Based on each aspect of digital literacy, namely, internet searching, understanding, and utilizing digital devices, hypertextual navigation, content evaluation, creating digital content, and communicating information, there were significant differences between groups on digital literacy competencies. All aspects of digital literacy in the experimental class were higher than in the control classKeywords: Digital Literacy, E-Learning, Learning Media
Sparse-Coding-Based Autoencoder and Its Application for Cancer Survivability Prediction

Cancer-survivability prediction is one of the popular research topics, that attracted great attention from both the health service providers and academia. However, one remaining question comes from how to make full use of a large number of available factors (or features). This paper, accordingly, presents a novel autoencoder algorithm based on the concept of sparse coding to address this problem. The main contribution is twofold: the utilization of sparsity coding for input feature selection and a subsequent classification using latent information. Precisely, a typical autoencoder architecture is employed for reconstructing the original input. Then the sparse coding technique is applied to optimize the network structure, with the aim of selecting optimal features and enhancing the generalization capability. In addition, the refined latent information is further cast as alternative features for training a sparse classifier. To evaluate the performance of the proposed autoencoder architecture, we present a comprehensive analysis using a publicly available data repository (i.e., Surveillance, Epidemiology, and End Results, SEER). Experimental study shows that the proposed approach has the ability of extracting important features from high-dimensional inputs and achieves competitive performance than other state-of-the-art classification techniques.
Feature based Entailment Recognition for Malayalam Language Texts

Textual entailment is a relationship between two text fragments, namely, text/premise and hypothesis.It has applications in question answering systems, multi-document summarization, information retrieval systems, and social network analysis.In the era of the digital world, recognizing semantic variability is important in understanding inferences in texts.The texts are either in the form of sentences, posts, tweets, or user experiences.Hence understanding inferences from customer experiences helps companies in customer segmentation.The availability of digital information is ever-growing with textual data in almost all languages, including low resource languages.This work deals with various machine learning approaches applied to textual entailment recognition or natural language inference for Malayalam, a South Indian low resource language.A performance-based analysis using machine learning classification techniques such as Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, AdaBoost, and Naive Bayes is done for the MaNLI (Malayalam Natural Language Inference) dataset.Different lexical and surface-level features are used for this binary and multiclass classification.With the increasing size of the dataset, there is a drop in the performance of feature-based classification.A comparison of feature-based models with deep learning approaches highlights this inference.The main focus here is the feature-based analysis with 14 different features and its comparison, essential to any NLP classification problem.
What Should I Notice? Using Algorithmic Information Theory to Evaluate the Memorability of Events in Smart Homes

With the increasing number of connected devices, complex systems such as smart homes record a multitude of events of various types, magnitude and characteristics. Current systems struggle to identify which events can be considered more memorable than others. In contrast, humans are able to quickly categorize some events as being more "memorable" than others. They do so without relying on knowledge of the system's inner working or large previous datasets. Having this ability would allow the system to: (i) identify and summarize a situation to the user by presenting only memorable events; (ii) suggest the most memorable events as possible hypotheses in an abductive inference process. Our proposal is to use Algorithmic Information Theory to define a "memorability" score by retrieving events using predicative filters. We use smart-home examples to illustrate how our theoretical approach can be implemented in practice.
Preventing Differential Cryptanalysis Attacks Using a KDM Function and the 32-Bit Output S-Boxes on AES Algorithm Found on the Internet of Things Devices

Many Internet of Things (IoT) devices use an Advanced Encryption Standard (AES) algorithm to secure data stored and transmitted during the communication process. The AES algorithm often suffers DC (DC) attacks. Little has been done to prevent DC attacks, particularly on an AES algorithm. This study focuses on preventing Differential Cryptanalysis attacks. DC attacks are practiced on an AES algorithm that is found on IoT devices. The novel approach of using a Khumbelo Difference Muthavine (KDM) function and changing the 8 × 8 S-Boxes to be the 8 × 32 S-Boxes successfully prevents DC attacks on an AES algorithm. A KDM function is a newly mathematically developed function, coined and used purposely in this study. A KDM function was never produced, defined, or utilized before by any researcher except for in this study. A KDM function makes a new 32-Bit S-Box suitable for the new Modified AES algorithm and confuses the attacker since it comprises many mathematical modulo operators. Additionally, these mathematical modulo operators are irreversible. The study managed to prevent the DC attack of a minimum of 70% on AES and a maximum of 100% on a Simplified DES. The attack on the new Modified AES Algorithm is 0% since no S-Box is used as a building block.
Processing of Clinical Notes for Efficient Diagnosis with Dual LSTM

Clinical records contain patient information such as laboratory values, doctor notes, or medications. However, clinical notes are underutilized because notes are complex, high-dimensional, and sparse. However, these clinical records may play an essential role in modeling clinical decision support systems. The study aimed to develop an effective predictive learning model that can process these sparse data and extract useful information to benefit the clinical decision support system for the effective diagnosis. The proposed system conducts phase-wise data modeling, and suitable text data treatment is carried out for data preparation. The study further utilized the Natutal Language Processing (NLP) mechanism where word2vec with Autoencoder is used as a clustering scheme for the topic modeling. Another significant contribution of the proposed work is that a novel approach of learning mechanism is devised by integrating Long Short Term Memory (LSTM) and Convolution Neural Network (CNN) to learn the inter-dependencies of the data sequences to predict diagnosis and patient testimony as output for the clinical decision. The development of the proposed system is carried out using the Python programming language. The study outcome based on the comparative analysis exhibits the effectiveness of the proposed method.
Feature distribution alignments for object detection in the thermal domain

Infrared imaging has recently played an important role in a wide range of applications including video surveillance, robotics and night vision. However, the manufacturing cost of high-resolution infrared cameras is more expensive regarding similar quality in visible cameras. This could explain the fact that thermal databases are less available compared to visible ones. In this paper, we mainly emphasis the need for aligning features from visible and thermal domains for object detection in order to ensure effective results in both domains without the need to retrain data and to perform additional annotations. To address that, we incorporate feature distribution alignments into faster R-CNN architecture at different levels. The resulting proposed adaptive detector has the advantage of covering different aspects of the domain shift in order to improve the overall performance. Using KAIST and FLIR ADAS datasets, the effectiveness of the proposed detector is assessed and better results are obtained compared to the baseline detector and to the obtained results by other existing works. Our code is available at https://github.com/AmineMarnissi/UDAT .
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message Passing

Graph neural networks (GNNs) are emerging machine learning models on graphs. Permutation-equivariance and proximity-awareness are two important properties highly desirable for GNNs. Both properties are needed to tackle some challenging graph problems, such as finding communities and leaders. In this paper, we first analytically show that the existing GNNs, mostly based on the message-passing mechanism, cannot simultaneously preserve the two properties. Then, we propose Stochastic Message Passing (SMP) model, a general and simple GNN to maintain both proximity-awareness and permutation-equivariance. In order to preserve node proximities, we augment the existing GNNs with stochastic node representations. We theoretically prove that the mechanism can enable GNNs to preserve node proximities, and at the same time, maintain permutation-equivariance with certain parametrization. We report extensive experimental results on ten datasets and demonstrate the effectiveness and efficiency of SMP for various typical graph mining tasks, including graph reconstruction, node classification, and link prediction.
Weather Forecasting Pridiction using Mamdani Fuzzifier

A climate expectation display is under study in view of the neural system and fuzzy surmising framework, and after that apply it to anticipate every day fuzzy precipitation given meteorological premises for testing. A "fuzzy ranked based neural system", which reenacts successive relations among fuzzy sets utilizing the manufactured neural system. It is outstanding that the requirement for exact climate expectation is clear while thinking about the advantages. Nonetheless, the over the top quest for exactness in climate expectation makes a portion of the "precise" forecast comes about pointless and the numerical forecast show is regularly intricate and tedious.
Emotion Recognition Algorithm Application Financial Development and Economic Growth Status and Development Trend

Financial market and economic growth and development trends can be regarded as an extremely complex system, and the in-depth study and prediction of this complex system has always been the focus of attention of economists and other scholars. Emotion recognition algorithm is a pattern recognition technology that integrates a number of emerging science and technology, and has good non-linear system fitting capabilities. However, using emotion recognition algorithm models to analyze and predict financial market and economic growth and development trends can yield more accurate prediction results. This article first gives a detailed introduction to the existing financial development and economic growth status and development trend forecasting problems, and then gives a brief overview of the concept of emotion recognition algorithms. Then, it describes the emotion recognition methods, including statistical emotion recognition methods, mixed emotion recognition methods, and emotion recognition methods based on knowledge technology, and conducts in-depth research on the three algorithm models of statistical emotion recognition methods, they are the support vector machine algorithm model, the artificial neural network algorithm model, and the long and short-term memory network algorithm model. Finally, these three algorithm models are applied to the financial market and economic growth and development trend prediction experiments. Experimental results show that the average absolute error of the three algorithms is below 25, which verifies that the emotion recognition algorithm has good operability and feasibility for the prediction of financial market and economic growth and development trends.
Анализ моделей экосистем в ракурсе социальной деполяризации в обществе

Today, a technocratic approach to the term of ecosystem has become entrenched in the consciousness of society, which is associated with the image of a set of technological platforms, usually digital, providing products and services to the client in a one-stop shop format. However, the natural origin of the concept of ecosystem dictates the use of a different approach based on the interaction within the ecosystem of the subsystem of living and inanimate nature, including humans, and the inorganic environment in the form of a socio-economic subsystem, as a result of which the ecosystem is able to provide society with so-called ecosystem services. Developing the hypothesis of the nature-like essence of social processes in society, the authors put forward the idea that the highest form of ecosystem organization will be a model of a socio-political super ecosystem, or ecosystem of ecosystems, in which the transition to a self-organizing, self-regulating, self-developing ecosystem is realized, created according to the model of a political network. Interdependent ecosystems, organizations and groups of citizens with the coordinating role of the parent provider organization, interact with each other to ensure the achievement of a socially significant strategic priority. In such a super ecosystem, a unified state goal-setting, common moral, ethical and value attitudes of ecosystem actors, along with the possibility of achieving mutual benefit of participants and citizens from socially significant ecosystem activities&nbsp; will allow maintaining a low level of social tension in society, and thereby prevent the emergence of foci of group social polarization.
A SMOTE-based quadratic surface support vector machine for imbalanced classification with mislabeled information

&lt;p style='text-indent:20px;'&gt;Recently, Synthetic Minority Over-Sampling Technique (SMOTE) has been widely used to handle the imbalanced classification. To address the issues of existing benchmark methods, we propose a novel scheme of SMOTE based on the K-means and Intuitionistic Fuzzy Set theory to assign proper weights to the existing points and generate new synthetic points from them. Besides, we introduce the state-of-the-art kernel-free fuzzy quadratic surface support vector machine (QSSVM) to do the classification. Finally, the numerical experiments on various artificial and real data sets strongly demonstrate the validity and applicability of our proposed method, especially in the presence of mislabeled information.&lt;/p&gt;
Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model

A high-stakes decision requires deep thought to understand the complex factors that stop a situation from becoming worse. Such decisions are carried out under high pressure, with a lack of information, and in limited time. This research applies Causal Artificial Intelligence to high-stakes decisions, aiming to encode causal assumptions based on human-like intelligence, and thereby produce interpretable and argumentative knowledge. We develop a Causal Bayesian Networks model based on causal science using d-separation and do-operations to discover the causal graph aligned with cognitive understanding. Causal odd ratios are used to measure the causal assumptions integrated with the real-world data to prove the proposed causal model compatibility. Causal effect relationships in the model are verified based on causal P-values and causal confident intervals and approved less than 1% by random chance. It shows that the causal model can encode cognitive understanding as precise, robust relationships. The concept of model design allows software agents to imitate human intelligence by inferring potential knowledge and be employed in high-stakes decision applications.
Basketball Training Posture Monitoring Based on Intelligent Wearable Device

Aiming at the problems of low monitoring accuracy, long time, and poor effect in the current basketball training posture monitoring method, a basketball training posture monitoring method based on intelligent wearable devices is proposed. By analyzing the concept and classification of intelligent wearable devices, the attitude monitoring technology based on intelligent wearable devices is studied. A two-stage Kalman filter is used to correct the error caused by the drift of the gyroscope signal in the intelligent wearable device by constructing an adaptive acceleration error covariance matrix. The time sequence of collecting acceleration and angular velocity signals is segmented, and the characteristics of basketball training posture are extracted from the sensor signals of the intelligent wearable device. The SVM classification algorithm is used to monitor the basketball training posture and realize the basketball training posture monitoring. The experimental results show that the basketball training posture monitoring effect of the proposed method is better, which can effectively improve the monitoring accuracy and shorten the monitoring time.
Reflections on the Experimental Evaluation of a Binary-Level Symbolic Analyzer for Spectre

execution.A well-known analysis technique that scales well on binary code is symbolic execution (SE) [4], [5].In order to analyze speculative constant-time, it must be adapted to additionally consider transient execution introduced by the speculative semantics.However, modeling these new behaviors explicitly does not scale because it quickly leads to state explosion.Therefore, the challenge is to optimize this exploration in order to make the analysis applicable to real code.Proposal.In our paper, "Hunting the Haunter-Efficient Relational Symbolic Execution for Spectre with Haunted RelSE" [6], we proposed a novel technique, Haunted RelSE, to model speculative behaviors more efficiently, and detect Spectre-PHT and Spectre-STL vulnerabilities.We implemented it in a new static analyzer for binary-code, called BINSEC/HAUNTED and evaluated it on small examples and on real-world cryptographic code.In summary, we proposed the following contributions:• We design a dedicated technique on top of relational symbolic execution, named Haunted RelSE, which key idea is to model transient and sequential (in order) behavior at the same time; • We propose a verification tool, BINSEC/HAUNTED, implementing Haunted RelSE and perform an experimental evaluation on 1) a well-known litmus tests (small test cases) for Spectre-PHT, 2) a new set of litmus tests for Spectre-STL that we propose, 3) on real-world cryptographic code.We also compare against two state of the art tools, KLEESpectre [7] and Pitchfork [3]; • Finally, we report new Spectre-STL violations concerning index-masking-a countermeasure against Spectre-PHT, and PIC options [8] from the gcc compiler.In this paper, we detail and reflect on the experimental aspects of our work and draw general takeaways.
A Mathematical Model for Risk Assessment of Social Engineering Attacks

Social engineering is a very common type of malicious activity conducted on cyberspace that targets both individuals and companies in order to gain access to information or systems. It is part of the broader domain of cybersecurity and the first step to mitigate this type of attack is to know its attack vectors. This way, the risk of becoming a victim of this type of attack can be reduced by technical means, proper security culture and procedural solutions – if organizations are referred to.