Reviewerly/exHarmony huggingface dataset:
COGNITIVE INDEPENDENCE AS A PEDAGOGICAL PHENOMENON

The methodological foundations of the organization of the learning process at the present stage are undergoing significant changes. This is due to the introduction of new standards and digital technologies into the educational process. In this regard, the phenomenon of “cognitive independence of students” is also undergoing transformation. Schoolchildren and students today have new digital tools in their hands, they do not need to search for new information bit by bit, painstakingly and carefully. There is a need to develop a new approach to understanding the traditional understanding of the phenomenon of “cognitive independence”. The article presents an analysis of the concept of “cognitive independence” from the point of view of the genesis of the problem under consideration. The main characteristics and signs of cognitive independence, factors influencing its formation and development are revealed. The results of an intermediate experiment on the study of cognitive independence in the student environment are given.
Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

Being an efficient image reconstruction and recognition algorithm, two-dimensional PCA (2DPCA) has an obvious disadvantage in that it treats the rows and columns of images unequally. To exploit the other lateral information of images, alternative 2DPCA (A2DPCA) and a series of bilateral 2DPCA algorithms have been proposed. This paper proposes a new algorithm named direct bilateral 2DPCA (DB2DPCA) by fusing bilateral information from images directly—that is, we concatenate the projection results of 2DPCA and A2DPCA together as the projection result of DB2DPCA and we average between the reconstruction results of 2DPCA and A2DPCA as the reconstruction result of DB2DPCA. The relationships between DB2DPCA and related algorithms are discussed under some extreme conditions when images are reshaped. To test the proposed algorithm, we conduct experiments of image reconstruction and recognition on two face databases, a handwritten character database and a palmprint database. The performances of different algorithms are evaluated by reconstruction errors and classification accuracies. Experimental results show that DB2DPCA generally outperforms competing algorithms both in image reconstruction and recognition. Additional experiments on reordered and reshaped databases further demonstrate the superiority of the proposed algorithm. In conclusion, DB2DPCA is a rather simple but highly effective algorithm for image reconstruction and recognition.
Efficient CP-ABE Scheme Resistant to Key Leakage for Secure Cloud-Fog Computing

&lt;p&gt;With the evolution of the Internet, people&amp;rsquo;s lives are growing increasingly intelligent, and massive amounts of data are also generated. The combination of cloud computing and fog computing can store and process it efficiently. However, a new and urgent challenge has emerged about how to ensure that data in cloud and fog nodes are accessed securely. In order to address this issue, we propose an efficient anti-key leakage CP-ABE (EAL-CP-ABE) scheme based on cloud-fog computing. In our scheme, we achieve the identity tracing of the authorized user who leaked the private key by means of white-box tracing and the maximum number of user accesses. Meanwhile, we design a reward and punishment mechanism which rewards those with a good reputation and revokes authorized users who have exceeded the access limit or sold private keys to further reduce the possibility of key leakage. Due to limited local resources, we use fog nodes to achieve outsourced decryption of ciphertext. In addition, the detailed security analysis is provided to prove that the EAL-CP-ABE scheme is IND-CPA secure and traceable based on the hardness assumptions. Finally, we demonstrate that the EAL-CP-ABE scheme is efficient through the performance analysis.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
EDUKASI PENCEGAHAN PENYEBARAN INFORMASI HOAKS MELALUI MEDIA SOSIAL BAGI SISWA DI SMAN 1 PALU

Social media can be understood that media is a tool used in communication activities. The dissemination of learning information is something that must be considered so that learning activities become more optimal and the objectives of learning can be achieved. The dissemination of information has so far experienced obstacles due to limited communication space. Various social media features used by the community, especially student students, to get learning materials in online learning activities. Fake news or hoaxes are now widely encountered on a daily basis and often even use provocative sensational titles. Based on that description, the problem is what efforts must be made in order to prevent the spread of hoax information with the knowledge and understanding of high school students in filtering information through social media. This service activity provides education to high school students about the importance of knowledge and understanding in preventing the spread of hoax information through social media for everyone. The service location will be carried out at SMA Negeri 1 Palu with the target of school students. The method of implementation is by lectures and discussions.
The Impact of Artificial Intelligence on Chatbot Design

Artificial intelligence is transforming the way chatbots are created and used. The recent boom of artificial intelligence development is creating a whole new generation of intelligent approaches that enable a more efficient and effective design of chatbots. On the other hand, the increasing need and interest from the industry in artificial intelligence based solutions, is guaranteeing the necessary investment and applicational know-how that is pushing such solutions to a new dimension. Some relevant examples are e-commerce, health or education, which is the main focus of this work. This paper studies and analyses the impact that artificial intelligence models and solutions is having on the design and development of chatbots, when compared to the previously used approaches. Some of the most relevant current and future challenges in this domain are highlighted, which include language learning, sentiment interpretation, integration with other services, or data security and privacy issues.
A Rule Based Procedural Content Generation System

This paper presents a new method of procedural content generation (PCG) for natural environments and objects (such as terrain, biomes, river, and vegetation), with special attention to game design and visual aesthetic. Our solution allows us to define a set of rules to control the content generation to meet the requirements of game design and level design teams.
A Quantum Approach to the Problem of Charging Electric Cars on a Motorway

In this paper, the problem of charging electric motor vehicles on a motorway is considered. Charging points are located alongside the motorway. It is assumed that there are a number of vehicles on a given section of a motorway. In the motorway, there are several nodes, and for each vehicle, the entering and the leaving nodes are known, as well as the time of entrance. For each vehicle, we know the total capacity of its battery, and the current amount of energy in the battery when entering the motorway. It is also assumed that for each vehicle, there is a finite set of speeds it can use when traveling the motorway. The speed is chosen when entering the motorway, and cannot be changed before reaching the charging station. For each speed, there is given a corresponding power usage; the higher the speed, the larger the power usage. Each vehicle can only use one charger, and when its battery is full, the amount of energy is sufficient for reaching the outgoing node. We look for a feasible solution to the problem, i.e., a solution in which no vehicle has to wait for a charger. The problem is formulated as a problem of scheduling independent, nonpreemptable jobs in parallel, unrelated machines under an additional doubly constrained resource, which is power. Quantum approaches to solve the defined problem are proposed. They use the quantum approximate optimization algorithm and the quantum annealing technique. A computational experiment is presented and discussed. Some conclusions and directions for future research are given.
A Critical Overview of the Implementation of Language-Immersion Through the Use of Mobile Apps

The use of m-learning, also known as mobile learning, allows the new generation of people to have better communication and activities regardless of location and time. The purpose of this research is to understand more about how students learn English using their mobile devices. Mobile technologies are increasingly being used in language teaching and learning. MALL (Mobile Assisted Language Learning) allows anybody to study regardless of their location, job, studies, and time constraints. In mobile learning, smartphones, iPods, tablets, computers, and iPads are utilised to facilitate language acquisition. There are various applications available for persons studying English as a second language. This research looks at how mobile apps are classified for elementary, secondary, and tertiary learners. This research focuses on the implementation, analysis, and assessment of language learning mobile apps. The concept, technique, theoretical, and pedagogical characteristics that drive modern mobile applications are also discussed in this article. Because these applications are designed to help learners improve their language abilities, the focus should be on learning the four important language skills of listening, speaking, reading, and writing using mobile technology. It also reveals that apps are more effective at teaching listening and speaking skills than traditional learning techniques.
Digitalization of Educational Organizations: Evaluation and Improvement Based on DigCompOrg Model

The digitalization of educational organizations is a political and social priority at European level and the model which is the basis for the analysis is DigCompOrg as part of the European Framework of Competences. This article summarizes the results of a longitudinal evaluative research (from 2018 until 2022) around the digitalization process of a compulsory secondary education center. We have applied a mixed method and an evaluative research design based on the use of questionnaires, focus groups and a research diary. This article is focused on data from the last evaluation (2021–2022) where the participants are 26 members of the management team, 46 teachers and 374 students. Our results show that progress has been made in the digitalization process, especially in some areas such as leadership, infrastructure/equipment and pedagogy/support/resources, which have obtained high scores. On the other hand, the data also show other areas where there is more scope for improvement, such as collaboration, digital networks and also innovative assessment practices. This research can be valuable as an example of a good practice around the digitalization of institutions of formal education.
Improved Multi-Label Image Classification Performance using Supervised CNN-LSTM Deep Neural Network

Deep Convolutional Neural Network (CNN) classification of single-object image labels has shown high efficiency.However, the great bulk of actual application data comprises of multiple label object images that belong to a variety of scenes, objects, and actions in a single image.Most of the recent research studies on multiple object label classification rely on individual classifiers for each label category and use probability ranking for final classification.These methods already in place work better, but they cannot find the dependencies between multiple labels in an image.In this paper, we use deep CNN architecture and long short-term memory (LSTM) to solve the problem of label dependence.Our proposed CNN-LSTM methodology learns the embeddings of object label to depict semantic object label dependence and image label association using a robust multi-label classifier cost function (RMLC), which is a ramp loss function.The feature extraction is carried out by a convolution neural network (CNN) pipeline; whereas multi-object label correlation is identified by LSTM using object labels and features extracted from input images.We use the loss function to make sure that correlated labels and corresponding features map close to each other, limiting the high value updation of weights for the images with improper labels, and the object label prediction progresses every time, which helps to improve the multi-label learning task.Experiments conducted using the proposed framework on benchmark visual recognition datasets such as CIFAR-10, STL-10, PASCAL VOC 2007, MS-COCO, and NUS-WIDE provided performance comparatively better than many existing methods in terms of accuracy and mean average precision.The CNN-LSTM + RMLC achieve the best test accuracy of 95.56 % on the STL dataset, which is 4% higher than the existing method, and the best mean average precision (mAP) of 82.6 on the MS-COCO dataset, which shows the feasibility and usefulness of our suggested framework on multiple label image classification.
IMPLEMENTASI K-MEDOIDS DAN MODEL WEIGHTED-LENGTH RECENCY FREQUENCY MONETARY (W-LRFM) UNTUK SEGMENTASI PELANGGAN DILENGKAPI GUI R

The k-medoids algorithm is a partition-based clustering algorithm that groups n objects as much as k clusters. The algorithm uses medoids as the center point (partition) of the cluster. Medoids are actual objects that are randomly selected as the most centered object in a cluster so that the k-medoids algorithm is robust against outliers. Grouping objects in cluster analysis based on similarities between objects. Measurement of similarity between objects can use the euclidean and manhattan distances. The use of distance in cluster analysis can affect cluster results. Validation of cluster results using internal validation, namely the silhouette index. The Weighted-Length Recency Frequency Monetary (W-LRFM) model is a model that applies the relative importance (weight) of the LRFM model according to the importance of each variable in the LRFM model. LRFM model is a model used for customer segmentation based on customer behavior which consists of variables length, recency, frequency, and monetary. The relative importance (weight) of the W-LFRM model uses the Analytics Hierarchical Process (AHP) method. The W-LRFM model is used to calculate the Customer Lifetime Value (CLV) of each cluster. The implementation of k-medoids and the W-LFRM model in this study are used for customer segmentation based on the length, recency frequency, and monetary variable. The formation of these variables is the result of transformation of customer behavior data such as transaction id, date of purchase, and a total amount of 41,073 rows into variable length, recency, frequency, and monetary as much as 5,108 rows. The criteria of the best cluster formed are k = 2 using the manhattan distance with the average of coefficient values = 0.62. The weights on the W-LRFM model produced based on the AHP method are 0.16, 0.29, 0.47, and 0.08 for the variable length, recency, frequency, and monetary. CLV formed from two clusters, namely 0.158 and 0.499. CLV in the second cluster is bigger so that the second cluster becomes the main priority in the marketing strategy. The second cluster has the characteristics 0.29, 0.47, and 0.08 for the variable length, recency, frequency, and monetary. The second cluster has the characteristics means a loyal customer group. The first cluster has characteristics means a potential customer group. This research is assisted by using Graphical User Interface (GUI) R to facilitate analysis
Towards Low-Latency Big Data Infrastructure at Sangfor

As a top cybersecurity vendor, Sangfor needs collects log streams from thousands of endpoint detection devices such as NTA, STA, EDR and identifies security threats in real-time way everyday. The discovery and disposal of network security incidents are highly real-time in nature with seconds or even milliseconds response time to prevent possible cyber attacks and data leaks. In order to extract more valuable information, the log streams are analyzed using stream processing with pattern matching like CEP (Complex Event Processing) in memory, and then stored in a persistent storage systems such as a data warehouse system or a search engine system for data scientists and network security engineers to do OLAP (Online Analytical Processing). Sangfor needs to build a low-latency big data platform to meet the challenges of massive logs. More and more open source systems are proposed to solve the problem of data processing in a certain aspect. Many decisions must be made to balance the benefits when designing a real-time big data infrastructure. What's more, how to architecture these systems and construct a one-stack unified big data platform have been the key obstacles for big data analytics. In this paper, we present the overall architecture of our low-latency big data infrastructure and identify four important design decisions i.e. message queue, stream processing, OLAP, and data lake. We analyze the advantages and disadvantages of existing open source system and clarify the reason behind our choices. We also describe the improvements and optimizations to make the open-source stacks fit in Sangfor's environments, including designing a real-time development platform based on Flink and re-architecting Apache Kylin, Clickhouse and Presto as a HOLAP system. Then we highlight two important use cases to verify the rationality of our infrastructure.
Maximizing the validity of the Gaussian approximation for the biphoton state from parametric down-conversion

Spontaneous parametric down-conversion (SPDC) is widely used in quantum applications based on photonic entanglement. The efficiency of photon pair generation is often characterized by means of a $\mathrm{sinc}(L\mathrm{\ensuremath{\Delta}}k/2)$ function, where $L$ is the length of the nonlinear medium and $\mathrm{\ensuremath{\Delta}}k$ is the phase mismatch between the pump and down-converted fields. In theoretical investigations, the $\mathrm{sinc}$ behavior of the phase mismatch has often been approximated by a Gaussian function $exp(\ensuremath{-}\ensuremath{\alpha}{x}^{2})$ in order to derive analytical expressions for the SPDC process. Different values have been chosen in the literature for the optimization factor $\ensuremath{\alpha}$, for instance, by comparing the widths of $\mathrm{sinc}$ and Gaussian functions or the momentum of down-converted photons. As a consequence, different values of $\ensuremath{\alpha}$ provide different theoretical predictions for the same setup. Therefore an informed and unique choice of this parameter is necessary. In this paper, we present a choice of $\ensuremath{\alpha}$ which maximizes the validity of the Gaussian approximation. Moreover, we also discuss the so-called super-Gaussian and cosine-Gaussian approximations as practical alternatives with improved predictive power for experiments.
Anomaly detection in quasi-periodic energy consumption data series: a comparison of algorithms

Abstract The diffusion of domotics solutions and of smart appliances and meters enables the monitoring of energy consumption at a very fine level and the development of forecasting and diagnostic applications. Anomaly detection (AD) in energy consumption data streams helps identify data points or intervals in which the behavior of an appliance deviates from normality and may prevent energy losses and break downs. Many statistical and learning approaches have been applied to the task, but the need remains of comparing their performances with data sets of different characteristics. This paper focuses on anomaly detection on quasi-periodic energy consumption data series and contrasts 12 statistical and machine learning algorithms tested in 144 different configurations on 3 data sets containing the power consumption signals of fridges. The assessment also evaluates the impact of the length of the series used for training and of the size of the sliding window employed to detect the anomalies. The generalization ability of the top five methods is also evaluated by applying them to an appliance different from that used for training. The results show that classical machine learning methods (Isolation Forest, One-Class SVM and Local Outlier Factor) outperform the best neural methods (GRU/LSTM autoencoder and multistep methods) and generalize better when applied to detect the anomalies of an appliance different from the one used for training.
Evolving Node Embeddings for Dynamic Exploration of Network Topologies

Static node embedding algorithms applied to snapshots of real-world applications graphs are unable to capture their evolving process. As a result, the absence of information about the dynamics in these node representations can harm the accuracy and increase processing time of machine learning tasks related to these applications. We propose a biased random walk method named Evolving Node Embedding (EVNE), which leverages the sequential relationship of graph snapshots by incorporating historic information when generating embeddings for the next snapshot. EVNE learns node representations through a neural network, but differs from existing methods as it: (i) incorporates previously run walks at each step; (ii) starts the optimization of the current embedding from the parameters obtained in the previous iteration; and (iii) uses two time-varying parameters to regulate the behavior of the biased random walks over the process of graph exploration. Through a wide set of experiments we show that our approach generates better embeddings, outperforming baselines in a downstream node classification task.
A Case Study on Learning Strategies of an English Normal Student in Applied Universities under the Background of Professional Certification

This study takes an English normal major student as the research object, adopting the methods of questionnaire survey, interview and learning diary to investigate the students' learning strategies from three aspects: metacognitive strategy, cognitive strategy and resource management strategy. Through the research, the problems existing in the process of using learning strategies are found and suggestions are put forward.
A Robust Hybrid Digital Watermarking Approach Using DCT-Fast RSVD For Medical and Non-Medical Applications

Medical image watermarking (MIW) is a widely used method today to confirm the validity of medical data. This paper proposes a hybrid, robust, semi-blind and imperceptible MIW system using the Discrete Cosine System (DCT) and Fast Randomized Singular Value Decomposition (FRSVD) with less computational complexity and computational time; relevant to both medical and non-medical images. Multiple scans and assessment matrices are used to evaluate the efficacy of the suggested technique, and it is compared to cutting-edge methodologies. The procedure obtained a maximum Peak Signal to Noise Ratio (PSNR) of 81.71 dB, Structural Similarity Index (SSIM) of 1, and Normalized Coefficient (NC) value of 1 for all the attacks. The suggested scheme achieved a meager embedding time and extraction time; 0.1638 seconds and 0.0955 seconds, respectively.