Reviewerly/exHarmony huggingface dataset:
Recommendation learning algorithm driven by knowledge map and deep learning and their application in lifelong learning

With the rapid development of social economy, lifelong learning has become one of people’s main tasks. How to recommend appropriate learning resources according to learners’ preferences has become a concern by more and more researchers. This paper takes the personalized recommendation of learning resources as the research object. Firstly, the semantic relationship of learning resources is extracted by knowledge map to realize the vectorization representation of learning resources. Based on this training convolutional neural network model, learning resources recommendation list is generated. Secondly, user-based recommendation algorithm is adopted in user behavior analysis with the purpose of generating learning resource recommendation list. Finally, the above recommendation list is gathered to realize the personality of learning resources, which can realize information filtering to the greatest extent, reduce the user information load. The results show that when the fusion ratio is close to 0.5, the algorithm recommendation accuracy and recall rate are higher, and the user’s preferences and interests have a higher degree of coincidence; the deviation between the recommended learning resources download amount and the user’s actual learning resources download amount is smaller; the proportion of recommended learning resources in the total learning resources is increased; and some unexpected learning resources can be found and recommended to the customers. When the sparsity of experimental data set is smaller, the accuracy, recall rate, coverage rate and MAE index of the personalized learning resource recommendation algorithm proposed in this paper are significantly higher.
A sample survey study of poly-semantic neurons in deep CNNs

Although deep CNN networks have excellent image classification performance, they do not provide interpretability, and furthermore existing work reveals that these models have complex internals, for example, mysterious polysemantic neurons activate to multiple features. In this work, we analyze the intermediate data of the network dissection paper made by Bau et al. to understand to what extent polysemantic neurons exist. We divide the polysemantic neurons into five types and calculate the percentage of each type by sampling. We find that above 50% neurons identify one concept but there are a quite proportion of neurons that recognize two or more features. This can explain the high classification accuracy and some capacity saving of a deep CNN. By case studies, we draw some conclusions and hypotheses: First, unlike the human visual system, a CNN cannot distinguish detailed features (metaphor: a CNN is like a nearsighted eye). Second, the reason that the CNN is prone to adversarial attacks may be partially due to the polysemantic neurons. Third, polysemantic neurons may partially explain why people wrongly visualize one thing as another in neuroscience.
Integrating users’ global and local interest for session-based recommendation

Conventional recommendation systems mostly recommend based on users’ global stable interest, while SR focuses on learning local active interest from the current session. Relevant research points out that integrating users’ global and local interest can improve model accuracy. However, most SR models are deficient in interest mining and fusion, and do not fully utilize the time interval content between sessions, result in inaccurate recommendations. In the paper, we propose a new session-based recommendation method called Global and Local Interest Integration Model (GLI-GNN). GLI-GNN introduces the concept of session time and attention mechanism to obtain global preferences from users’ previous sessions; it uses GNN to obtain local interest from current sessions; it uses attention mechanism to integrate both. The results indicate that GLI-GNN realizes new optimal performance in next-shot interactive recommendation. Experimental results show that GLI-GNN is superior to the existing SR models.
Hierarchical activation of quantum nonlocality: Stronger than local indistinguishability

The quantum state discrimination primitive becomes highly nontrivial in the limited measurement setting and leads to different classes of impossibility, viz., indistinguishability, unmarkability, irreducibility, etc. These phenomena, often referred to as another nonlocal aspect of quantum theory, have utmost importance in the domain of data hiding, secret sharing, etc. Motivated by this, recently a significant effort has been devoted to activate local indistinguishability from locally distinguishable nontrivial sets of quantum states. In the present work, we introduce other stronger notions of quantum nonlocality and depict a series of hierarchical nonlocality activation from nontrivial sets of locally distinguishable entangled states. Moreover, we appropriately moderate the strict condition of nontriviality, introduced in earlier literature, and come up with interesting examples in support of our claim.
Agent-based Intelligent Tutoring System for Arabic Grammar

an Intelligent Tutoring System (ITS) gives tailored teaching to students based on their learning preferences and/or skills to address the problems of learning. These include the difficulty of providing quality learning to students in remote areas, which may be hard to reach by students and/or teachers. Additionally, tracking the progress of each individual student and providing suitable learning material and exercises is not an easy task. One of the best candidates for such systems is Arabic grammar, which is a quite complex subject. Nevertheless, due to this complexity it hasn’t been adequately handled by researchers. Also, those systems have generally been developed for elementary-level grammar courses.
Student preparation program to apply the Egel-Isoft: Case of Bachelor of Computer Systems of the Autonomous University of Nayarit

Certification is public recognition that a person has the knowledge and skills necessary to perform a profession. Some universities have adopted the CENEVAL EGEL-ISOFT to assess the knowledge and skills of their graduates, and at the same time allow them to graduate from a score that is established by the universities that have adopted this type of exam. In the present work, the experience of the implementation of the EGEL-ISOFT as a degree option in the Bachelor of Computer Systems of the Autonomous University of Nayarit is exposed, as well as a set of actions aimed at improving the degree index of the graduates of this degree.
Analyzing and Unifying Robustness Measures for Excitation Transfer Control in Spin Networks

Recent achievements in quantum control have resulted in advanced techniques for designing controllers for applications in quantum communication, computing, and sensing. However, the susceptibility of such systems to noise and uncertainties necessitates robust controllers that perform effectively under these conditions to realize the full potential of quantum devices. The time-domain log-sensitivity and a recently introduced robustness infidelity measure (RIM) are two means to quantify controller robustness in quantum systems. The former can be found analytically, while the latter requires Monte-Carlo sampling. In this work, the correlation between the log-sensitivity and the RIM for evaluating the robustness of single excitation transfer fidelity in spin chains and rings in the presence of dephasing is investigated. We show that the expected differential sensitivity of the error agrees with the differential sensitivity of the RIM, where the expectation is over the error probability distribution. Statistical analysis also demonstrates that the log-sensitivity and the RIM are linked via the differential sensitivity, and that the differential sensitivity and RIM are highly concordant. This unification of two means (one analytic and one via sampling) to assess controller robustness in a variety of realistic scenarios provides a first step in unifying various tools to model and assess robustness of quantum controllers.
Image-based Calligraphy Information Extraction and Manipulator Copying

Chinese calligraphy is a unique form of art which plays a great role in the inheritance and development of Chinese traditional culture. The manipulator is able to imitate the action of human arms and can be used to accomplish the task of writing Chinese calligraphy. Taking official script as example, the following tasks are completed in this paper: extracting and improving the information of calligraphic Chinese characters based on image recognition algorithm; determining the hardware of the manipulator, modeling using the Robotics Toolbox toolkit in MATLAB, and proposing a three-dimensional writing path generation method based on the two-dimensional trajectory of Chinese characters and the depth of brush movement; realizing the software development and design of the upper computer, debugging of the hardware and software system, and realizing the copying of official Chinese characters. The actual test results show that the manipulator copying system designed in this paper can accurately recognize the official Chinese character images and extract calligraphy information from them. The manipulator can nicely finish the task of copying Chinese characters, and the system runs stably and reliably.
Attention Guided Multi-task Network for Joint CFO and Channel Estimation in OFDM Systems

The existence of high carrier frequency offset (CFO) and fading channels degrades the performance of communication systems in high mobility environments significantly. To address these challenges, in this paper, we propose an attention guided multi-task network for joint CFO and channel estimation in orthogonal frequency division multiplexing (OFDM) systems. Specifically, considering the correlation between CFO estimation and channel estimation, we construct a multi-task neural network framework which consists of a shared branch (SB), a CFO estimation branch (CFOEB) and a channel estimation branch (CEB). Among them, the SB extracts common features of two tasks, and the CFOEB and CEB make more detailed CFO and channel estimation, respectively. To further suppress the influence of CFO estimation error on channel estimation, we introduce an attention guided module (AGM) which completes the re-calibration of the channel dimension on the basis of different CFO characteristics. We train the whole network by an end-to-end paradigm and construct a suitable multi-task loss function to balance the losses between different tasks. Simulation results demonstrate that the proposed attention guided multi-task learning based joint CFO and channel estimation scheme outperforms the conventional methods and has greater robustness under various conditions.
Editorial for the Special Issue “Environment and Geochemistry of Sediments”

The geochemical characteristics of environments can be determined via geochemical studies of sedimentary rocks [...]
Overlap of Youth and Student Online Communities

The aim of the study is to identify overlap and audience interrelations of various youth and student online communities in the VKontakte. 23 youth and student online communities of 4 regions (in which are located universities of the consortium “Upper Volga - the Territory of Innovation”) and 4 online communities of federal authorities and all-Russian youth organizations on the VKontakte were selected for the research. Through the Target Hunter platform, primary information about VKontakte communities was collected (number of subscribers; type of community; openness of communities). Later, using the “audience intersection” function, it was possible to understand how many subscribers from different VKontakte online communities are in them at the same time. Having received the number of overlapping users for each community, a general sociomatrix was compiled, in which the proportion of overlapping subscribers from the total number of subscribers of a single online community was revealed. The analysis of audience overlap reveals the degree of unified information space formation in the student environment and the ability to broadcast the agenda to the target audience on the Internet and organization of effective communication.
Automated videogame mechanics generation with XVGDL

The automatic generation of game mechanics is nowadays one of the most complex challenges within procedural content generation (PCG), even being considered by itself as automatic game generation in the literature. Previous works have contributed with research papers related to PCG in general, and, in particular, the generation of game mechanics from a specification written in a game description language (GDL). One primary reference is the Ludii general game system that allows to generate new mechanics for two-player combinatorial (board) games. Ludii manages game specifications written in a GDL that can be directly represented in a tree structure. These structures are then evolved by means of genetic programming (GP), a well-known bio-inspired optimization technique, to produce new game rules. Now, this paper extends the approach centered on 2-player games and proposed in the Ludii general game system to n-player videogames. The paper describes a system to automatically generate videogame mechanics. The starting point is a videogame specification written in the XML-based videogame description language (XVGDL). Similarly to Ludii, this videogame specification can be directly translated to rule tree structures that can be evolved by GP. The viability of this approach is demonstrated by experimentation with practical examples. The experiments show how it is possible to automatically produce and evaluate a number of distinct versions (that differ in their mechanics) of a videogame that was originally specified in XVGDL.
Product Recommendation System Using Deep Learning Techniques: CNN and NLP

There are several Websites today that compare products. However, the majority of them only use textual data. Through the creation of content-based image retrieval (CBIR), the visual software for product photos presented in this work provides a revolutionary technique for visually locating products. Consumers’ comparison-purchasing strategy is influenced by product value, complexity, and durability. Comparing prices is frequently used to describe contrast searches as a whole. Contrast searching is expanding beyond just finding the cheapest greenest goods online, though. Nowadays, a vast variety of things are available online. Customers can access various records about the products that they are interested in by using production advice structures. The core of this problem from a computer and technology perspective is to extract information from the items so that it can be utilized to match the related products. In order to fit a collection of goods gathered in a database, this work provides a method that merges records from items and, as a result, the outline of the products (textual format). With the help of the comprehension offered by the usage of CNN technique, the definition of the products is integrated. A simple weighting technique is used to integrate photo and text content information. Cosine similarity measurement is used to execute the matching. Online retailers provided the information. The processing of the product reviews has been done using NLP. Android app to compare product prices while using reviews and pricing as help.
Studi Fenomenologi Tentang Behavior Pattern Mahasiswa Akuntansi: Serupa atau Tak Samakah?

The COVID-19 pandemic has significantly altered the order of human life. One of the aspects affected by the COVID-19 pandemic is education. The original face-to-face learning system has been converted to a distance learning system (online) as a result of the implementation of Large-Scale Social Restrictions (PSBB) to encourage people to avoid crowds. This pandemic period, however, has passed, and Indonesia was declared endemic by the Coordinating Minister for the Economy at the end of December 2022. As a result, Indonesia is said to have entered a new normal era in which human life has returned to pre-pandemic levels while adhering to health protocols. The goal of this research is to look at how students' lifestyles changed after the COVID-19 pandemic ended, as well as the potential for changes in Accounting students' behavior patterns. This study investigates and explains the behavioral changes of Accounting students at the Faculty of Economics and Business, University of Tanjungpura, during the COVID-19 pandemic (FEB UNTAN). This is a qualitative study that takes a phenomenological approach. In this study, data was collected through observation and in-depth interviews with several students from the Bachelor of Accounting Study Program (informants). According to the findings of this study, accounting students experienced changes in their behavior patterns and life orientation following the pandemic. Students become more involved in various organizational activities on and off campus, and they are more motivated to learn and achieve their goals.Keywords: Accounting Student, Behavior, COVID-19 Pandemi
Image fusion dehazing algorithm based on multi-logarithmic transform

In hazy scenarios, suspended particles in the atmosphere will absorb and scatter the transmitted natural light, resulting in a serious degradation of image quality obtained by imaging equipment, which greatly affects the visual perception of images. Aiming at the problems such as low contrast, color distortion and lack of detail information in areas with high haze concentration in images acquired by image acquisition equipment in hazy days, this paper proposes a dehazing algorithm based on exposure image fusion based on multi-logarithmic transform, which improves image quality while effectively dehazing images and avoids the edge effect in the sky part of images after dehazing. Firstly, the original hazy image was transformed by logarithmic multiple times to produce multiple images with different exposure to be fused. Then, all the input images with logarithmic transformation and weight graphs were fused by multi-scale pyramid fusion method to obtain the dehazing image. In order to verify the effectiveness of the algorithm in this paper, the results of the proposed algorithm and six mainstream image dehazing algorithms are compared in two aspects: subjective evaluation and objective evaluation. Experimental results show that the image processed by the proposed algorithm presents better visual effects than that processed by other algorithms. The proposed algorithm can effectively improve image contrast, improve image distortion, improve the visibility of detail information in areas with high hazy concentration, and the scenery color is natural. Good results are obtained under two objective evaluation indexes of image quality, namely peak signal-to-noise ratio and structural similarity, which further proves that the algorithm proposed in this paper has good dehazing performance, can effectively improve the image visibility, and has a good overall color preservation degree of the image.
Bank Records Storage System Through Blockchain

This research puts forth a system that utilizes the integration of blockchain, a database for storage, and cryptography to preserve the confidentiality and security of bank records. The blockchain ensures the protection and dependability of data storage through its use, while smart contracts regulate the way the data is stored and shared. The original bank records are kept in a secured, encrypted format on a separate database, with only their hash values recorded on the blockchain. To further secure the data, the off-chain records are frequently connected with the hash information on the blockchain. The utilization of cryptography aids in the encryption of documents and the digital signing of messages. The system has a WebApp interface that enables parties involved in the transaction to communicate in a decentralized manner.
A Performance Comparison of State-of-the-Art Imputation and Classification Strategies on Insurance Fraud Detection

Annually, around one trillion in premiums are gathered by the insurance market, which engages over a thousand enterprises globally. Insurance fraud is when a person or group presents bogus insurance claims to receive compensation or privileges that they are not obligated to, and it costs the insurance business tens of billions. As a corollary, the insurance industry faces a challenging burden in detecting insurance fraud. Examining and identifying fraudulent elements is a common existing approach to detecting fraud, but it requires a long time and is tedious since it might lead to inaccurate results. The focus of this research is to develop an automated machine learning classification framework with the best imputation technique for detecting fraud claims. As a result, the logistic regression-based iterative imputer coupled with XGBoost classifier achieved the highest accuracy of 90% in this comparison research.
Fake Currency Detection

Indian is a developing country, Production, and printing of Fake notes of Rs.100, 500 and 1000 were already there but after the demonetization, the counterfeit notes of new Rs.50,200,500,2000 have also come to the light in very short time and which effects the country’s economic growth. From last few years due to technological advancement in color printing, duplicating, and scanning, counterfeiting problems are coming into the picture. In this article, recognition and verification of paper currency with the help of digital image processing techniques is described. The characteristics extraction is performed on the image of the currency and it is compared with the characteristics of the genuine currency. The currency will be recognized and verified by using image processing techniques. The approach consists of a number of components including image processing, edge detection, image segmentation and characteristic extraction and comparing images. The desired results will be the text and voice output of the currency recognized and verified.
Temporal differences of rainfall in Erbil province based on remote sensing data and meteorological station data between 1981-2020

Abstract Climate and changing its elements are very important because they affect water resources, agriculture, the environment and many other aspects of human life. This study aims to show the amount of monthly and annual rainfall and to verify spatiotemporal differences in the Province of Erbil. It also aims to compare satellite data with annual data from meteorological stations in the study area. The methods used in this research are comparison, analysis and statistical methods to show the direction of rain changes such as Kernel and Kriging. The study used meteorological station data between 2001 and 2020 and remote sensing data (CHIRPS) from 1981 to 2020. The study found that the average annual rainfall between 1981 and 2020 was 609 ± 122 mm, as well as a 40-year increase in rainfall by only 1.88 mm, according to CHIRPS data. Data from Erbil governorate stations confirm that rainfall increased by 106 mm from 2001 to 2020, but the change is not significant statistically (p-value = 0.72), with the exception of Shorsh and Koya stations in the province, where rainfall decreased in the mentioned period. With regard to the comparison between remote sensing data and annual data from meteorological stations in the study area between 2001 and 2020, there appears to be an important strong positive relationship between them, because the R2 is (0.7), (p-value) is (0.001) and the error scale (RMSE) is (83.04) mm.