Reviewerly/exHarmony huggingface dataset:
Digital educational history as a component of the digital student's profile in the context of education transformation

Introduction. The recent active development of digital educational infrastructure allows educational institutions to collect and store large amounts of data related to the learning process. In this regard, the task of describing, structuring and combining various data about the student into a single digital profile, seems relevant as it will make possible applying an integrated approach to solving the problems of data-driven management of the educational process. Materials and methods. A comparative analysis of the literature to analyze the existing terminology in the field of digital transformation of education and methods for modeling objects and processes in education, as well as to identify the components of the digital student's profile has been carried out. To assess the intensity of activity performed by students in the digital learning environment (DLE) of the university, we have conducted the survey of the 172 students of Siberian Federal University majoring in several areas of training: Computer Science and Computer Engineering, Applied Computer Science, Information Security, and Metallurgy. Results. We proposed the so-called student digitalization pyramid, which includes the following hierarchy levels: digital footprint, digital profile, digital model, digital twin. We described the structure of the digital profile of a student which includes two components – a digital personality portrait and a digital educational history and mentioned possible links between them. We introduced a classification of its components and identified the sources of obtaining these components from the university EIEE. We introduced and described in detail the concept of digital educational history which is a multidimensional structured dynamically changing data on the educational activities of the student and his current learning outcomes. We indicated the problem of completeness of digital educational history data, the presence of which is confirmed by the results of the survey. Three most frequently used sources of educational materials in addition to the university e-learning courses were identified as video hosting (used by 85.5% of respondents), educational interaction in small groups (62.2%) and learning forums (45.3%). At the same time, obtaining educational history data from these sources is a serious problem which is difficult to overcome. Conclusions. In the future, digital educational history data will become the basis for modeling a student (their learning process) and developing their digital twin. Nevertheless, there are a number of problems which concern the collection and verification of the related data, the ethics of their use and applicability for solving learning management problems. We propose possible solutions to these problems
Detection of anaemia using medical images: A comparative study of machine learning algorithms – A systematic literature review

Anaemia is a global public health challenge that affects children and pregnant women. Anaemia develops when the body's supply of red blood cells declines or when the structure of the cells is weakened. The clinical diagnosis of anaemia has several challenges in practice, including insufficient funding for medical tests, inadequate medical personnel and resources in remote areas, and client reluctance that results in abstinence. Several machine learning techniques for anaemia detection have been developed due to their affordability, simplicity of use, and non-invasive nature as compared to the invasive approach. We perform a systematic review and examine current trends and concepts of machine learning in healthcare services to identify viable approaches to detect anaemia. We compare the most successful machine learning algorithms currently in use regarding machine learning, evaluation metrics, image augmentation, and the origin and size of the dataset used. The result of this study is a clear indication that non-invasive methods such as the use of machine learning algorithms to detect anaemia are affordable, and provides results on time. This systematic review provide scientific evidence, with the results describing how effective machine learning could make anaemia detection feasible. Machine and deep learning algorithms are introduced and used to make a wide-ranging analysis of images, diagnosis, and clinical analysis in the disciplines of medical fields such as anaemia detection.
A Debiasing Autoencoder for Recommender System

The deep neural network (DNN)-based recommender system (RS) has drawn much attention recently and provided state-of-the-art results. Although many DNN-based RSs have been achieved, most focus on inventing a sophisticated DNN with a single-metric loss function to fit user behavior data. However, user behavior data are commonly collected from numerous users in complex scenarios, making various biases and outliers (outliers can be seen as the special bias) widely exist in the data. Unfortunately, prior DNN-based RSs only considered rather fragmented biases and lacked a comprehensive solution. To fill this gap, this paper proposes an AutoRec++ model to comprehensively address the various biases existed in user behavior data. Its main idea is to employ different combinations of preprocessing bias (PB) and training bias (TB) as well as L1-norm and L2-norm to form a multi-metric loss function-oriented Autoencoder. As such, AutoRec++ possesses the multi-merits of the PB’s and TB’s debiasing ability, the L1-norm’s robustness, and the L2-norm’s stability. By conducting extensive experiments on five benchmark datasets, we demonstrate that: 1) the incorporation of PB and TB can significantly boost Autoencoder’s prediction accuracy and computational efficiency without structural change, and 2) our AutoRec++ achieves better prediction accuracy and robustness than both DNN-based and non-DNN-based state-of-the-art models. Besides, our AutoRec++ is more effective in processing sparser user behavior data. Our code is available at the link: https://github.com/wudi1989/AutoRec_Plus.
Research on fusion model of BERT and CNN-BiLSTM for short text classification

With respect to short texts with high information content, unstructured and non-standard, a text classification model (BERT-CNN-BiLSTM) based on the fusion of BERT model and BiLSTM network with convolutional neural network is proposed. To improve data processing efficiency and classification precision, word vectors are trained in BERT and used as the embedding layer of the model. The embedding layer is utilized to retain semantic information and the semantic representation of words is enhanced. CNN is applied to extract the local semantics of text. Meanwhile, gated linear unit (GLU) is used to optimise the CNN, and gradient dispersion is reduced. BiLSTM is designed to acquire contextual information about the text. Text classification is better implemented. The experimental results show that better results are obtained by BERT training data as word vectors. The BERT-CNN-BiLSTM has significantly improved in terms of classification precision, recall and F1 than the CNN, the BERT-CNN, et al. Precision, recall and F1 values are improved by at least 1.44%, 1.66% and 1.69%, respectively.
Key frame extraction method for lecture videos based on spatio-temporal subtitles

Affected by the Corona Virus Disease 2019 (COVID-19), online lecture videos have witnessed an explosive growth. In the face of massive videos, this paper proposes a method for extracting key frames of lecture videos based on spatio-temporal subtitles, which can efficiently and quickly obtain effective information. Firstly, the spatio-temporal slices of subtitle area of the video sequence are extracted and spliced along the time axis to construct the video spatio-temporal subtitle. Then, the video spatio-temporal subtitle is processed in binarization, and the projection method is used to construct the SSPA curve of the video spatio-temporal subtitle. Finally, a selection method for steady-state key frame is designed, that is, the key frame extraction is realized by combining curve edge detection and subtitle existence threshold, which ensures the robustness of the proposed method. The test results of 8 videos show that the average value of the comprehensive index F1-score of the key frame extracted by the algorithm can reach 0.97, the average precision is 0.97, and the average recall rate is 0.98. It can effectively extract the key frames in lecture videos, and compared with other algorithms, the average running time is reduced to 0.072 of the original, which is helpful to extract video information quickly and accurately.
A Deterministic Finite-State Morphological Analyzer for Urdu Nominal System

The morphological analyzer is a computational process that combines lemmas with other linguistic features to produce new lexical word forms. This paper investigates the processing of a nominal system in the Urdu language. It focuses on the inflections of noun forms and studies number, gender, person, and case representations, using a Finite State Machine (FSM) to analyze and create all the possible forms of the standardized registers. The application of the analysis using this tool provides and displays all the possible structures and their declensions. This study adds all the necessary features and values to the lexical concatenating nouns according to their patterns. The accuracy score of the output is 92.7, where the actual output depends on the detailed design of the FSM and the specific morphological processes provided to the finite state tools.
FDS_2D: rethinking magnitude-phase features for DeepFake detection

To reduce the harm of forged information, more and more detection methods use frequency domain information. They mostly take spectra as clues to identify fake content. However, the current work tends to use only one of the magnitude and phase spectra for learning. In this paper, we notice that the magnitude and phase spectrum contain different image information. Only one spectrum is easily disturbed by noise, and the robustness of the method is difficult to guarantee. Therefore, we propose the Frequency Domain Separable DeepFake Detection (FDS_2D), which is a multi-branch network to obtain features in different frequency spectra. In FDS_2D, the spectral information is divided into three categories: the magnitude spectrum, the phase spectrum, and the relationship between the two spectra. According to their characteristics, we design independent modules for feature extraction from them. Moreover, to improve the utilization efficiency of multi-features, we propose a multi-input multi-output attention mechanism for information interaction between branches. The experimental results show that each part of FDS_2D effectively extracts and applies spectral information; The comprehensive performance of our model is verified on FaceForensic + + , Celeb-DF, and DFDC. It proves that the ability of FDS_2D to detect DeepFake is not inferior to existing models.
State-of-the-Art in Language Technology and Language-centric Artificial Intelligence

Abstract This chapter landscapes the field of Language Technology (LT) and language- centric AI by assembling a comprehensive state-of-the-art of basic and applied research in the area. It sketches all recent advances in AI, including the most recent deep learning neural technologies. The chapter brings to light not only where language-centric AI as a whole stands, but also where the required resources should be allocated to place European LT at the forefront of the AI revolution. We identify key research areas and gaps that need to be addressed to ensure LT can overcome the current inequalities.
Leveraging Rust Types for Program Synthesis

The Rust type system guarantees memory safety and data-race freedom. However, to satisfy Rust's type rules, many familiar implementation patterns must be adapted substantially. These necessary adaptations complicate programming and might hinder language adoption. In this paper, we demonstrate that, in contrast to manual programming, automatic synthesis is not complicated by Rust's type system, but rather benefits in two major ways. First, a Rust synthesizer can get away with significantly simpler specifications. While in more traditional imperative languages, synthesizers often require lengthy annotations in a complex logic to describe the shape of data structures, aliasing, and potential side effects, in Rust, all this information can be inferred from the types, letting the user focus on specifying functional properties using a slight extension of Rust expressions. Second, the Rust type system reduces the search space for synthesis, which improves performance. In this work, we present the first approach to automatically synthesizing correct-by-construction programs in safe Rust. The key ingredient of our synthesis procedure is Synthetic Ownership Logic, a new program logic for deriving programs that are guaranteed to satisfy both a user-provided functional specification and, importantly, Rust's intricate type system. We implement this logic in a new tool called RusSOL. Our evaluation shows the effectiveness of RusSOL, both in terms of annotation burden and performance, in synthesizing provably correct solutions to common problems faced by new Rust developers.
A hyperchaotic image encryption algorithm based on LSTM neural network and lifting wavelet transform

Abstract In order to solve the problems of simple permutation-diffusion structure, low encryption efficiency and small chaos range of existing chaotic systems, this paper proposes a hyperchaotic image encryption algorithm based on LSTM neural network and lifting wavelet transform. By building upon the Lorenz chaotic system, we construct a new hyperchaotic system in this paper, which has more complex dynamic characteristics and higher Lyapunov exponent. Then, the image is encrypted by lifting wavelet transform and zigzag conversion algorithm. Finally, the trained LSTM neural network is used to process the Henon chaotic sequence to obtain the second key for encryption, which can effectively resist chosen-plaintext attack. The experimental findings indicate that the proposed encryption algorithm has good performance in key security, differential attack, statistical analysis and operation efficiency, which indicates that the algorithm has high security.
Unsupervised video anomaly detection in UAVs: a new approach based on learning and inference

In this paper, an innovative approach to detecting anomalous occurrences in video data without supervision is introduced, leveraging contextual data derived from visual characteristics and effectively addressing the semantic discrepancy that exists between visual information and the interpretation of atypical incidents. Our work incorporates Unmanned Aerial Vehicles (UAVs) to capture video data from a different perspective and to provide a unique set of visual features. Specifically, we put forward a technique for discerning context through scene comprehension, which entails the construction of a spatio-temporal contextual graph to represent various aspects of visual information. These aspects encompass the manifestation of objects, their interrelations within the spatio-temporal domain, and the categorization of the scenes captured by UAVs. To encode context information, we utilize Transformer with message passing for updating the graph's nodes and edges. Furthermore, we have designed a graph-oriented deep Variational Autoencoder (VAE) approach for unsupervised categorization of scenes, enabling the extraction of the spatio-temporal context graph across diverse settings. In conclusion, by utilizing contextual data, we ascertain anomaly scores at the frame-level to identify atypical occurrences. We assessed the efficacy of the suggested approach by employing it on a trio of intricate data collections, specifically, the UCF-Crime, Avenue, and ShanghaiTech datasets, which provided substantial evidence of the method's successful performance.
Spin-motion coupling in a circular-Rydberg-state quantum simulator: Case of two atoms

Rydberg atoms are remarkable tools for the quantum simulation of spin arrays. Circular Rydberg atoms open the way to simulations over very long time scales, using a combination of laser trapping of the atoms and spontaneous-emission inhibition, as shown in the proposal of a XXZ spin-array simulator based on chains of trapped circular atoms [T.L. Nguyen $\textit{et al.}$, Phys. Rev. X 8, 011032 (2018)]. Such simulators could reach regimes (thermalization, glassy dynamics) that are out of the reach of those based on ordinary, low-angular-momentum short-lived Rydberg atoms. Over the promised long time scales, the unavoidable coupling of the spin dynamics with the atomic motion in the traps may play an important role. We study here the interplay between the spin exchange and motional dynamics in the simple case of two interacting circular Rydberg atoms confined in harmonic traps. The time evolution is solved exactly when the position dependence of the dipole-dipole interaction terms can be linearized over the extension of the atomic motion. We present numerical simulations in more complex cases, using the realistic parameters of the simulator proposal. We discuss three applications. First, we show that realistic experimental parameters lead to a regime in which atomic and spin dynamics become fully entangled, generating interesting non-classical motional states. We also show that, in other parameter regions, the spin dynamics notably depends on the initial temperature of the atoms in the trap, providing a sensitive motional thermometry method. Last, and most importantly, we discuss the range of parameters in which the motion has negligible influence over the spin dynamics.
Small-target water-floating garbage detection and recognition based on UNet-YOLOv5s

To address the problem of low recognition accuracy caused by the interference of illumination, water waves and complex backgrounds in the process of detecting and recognizing small-target of water-floating garbage, a water-floating garbage recognition algorithm that fuses image semantic segmentation network and target detection network is proposed. Firstly, using UNet network, the water floating garbage is segmented from the complex background and the image is cropped into a uniform size image. Then, a dark channel prior algorithm is used for noise removal, which cuts down the interference of illumination and water waves. Finally, eight categories of water floating garbage are defined from the perspective of the relationship between the degree of contamination and recyclability of water-floating garbage, and according to this classification, the improved YOLOv5s network is trained to achieve the detection and recognition of water-floating garbage. The results show that the proposed algorithm has 89.80% detection and recognition accuracy, 85.76% recall and 87.73% F1score, which can effectively improve the detection and recognition accuracy of water-floating garbage and provide a new and effective method for the management of water-floating garbage.
Entanglement classification from a topological perspective

Classification of entanglement is an important problem in quantum resource theory. In this paper we discuss an embedding of this problem in the context of topological quantum field theories (TQFTs). This approach allows classifying entanglement patterns in terms of topological equivalence classes. In the bipartite case a classification equivalent to the one by stochastic local operations and classical communication (SLOCC) is constructed by restricting to a simple class of connectivity diagrams. Such diagrams characterize quantum states of TQFT up to braiding and tangling of the ``connectome.'' In the multipartite case the same restricted topological classification only captures a part of the SLOCC classes, in particular, it does not see the $W$ entanglement of three qubits. Nonlocal braiding of connections may solve the problem, but no finite classification is attempted in this case. Despite incompleteness, the connectome classification has a straightforward generalization to any number and dimension of parties and has a very intuitive interpretation, which might be useful for understanding specific properties of entanglement and for design of new quantum resources.
A Few-Shot Approach to Resume Information Extraction via Prompts

Prompt learning’s near fine-tune performance on text classification tasks has attracted the NLP community. This paper applies it to resume information extraction, improving existing methods for this task. We created manual templates and verbalizers tailored to resume texts and compared the performance of Masked Language Model (MLM) and Seq2Seq PLMs. Also, we enhanced the verbalizer design for Knowledgeable Prompt-tuning, contributing to prompt template design across NLP tasks. We present the Manual Knowledgeable Verbalizer (MKV), a rule for constructing verbalizers for specific applications. Our tests show that MKV rules yield more effective, robust templates and verbalizers than existing methods. Our MKV approach resolved sample imbalance, surpassing current automatic prompt methods. This study underscores the value of tailored prompt learning for resume extraction, stressing the importance of custom-designed templates and verbalizers.
Gamified flipped classroom as a pedagogical strategy in higher education: From a systematic vision

The gamified flipped classroom is a pedagogical strategy that combines the principles of the flipped classroom model with gamification elements to improve the learning experience in higher education. From a systematic vision, this strategy is based on careful planning and the integration of different components to achieve a motivating and effective learning environment. First of all, the flipped classroom model implies that students acquire basic concepts outside of the classroom, through resources such as videos, readings or interactive materials. Then, in the classroom, time is spent applying those concepts through hands-on activities, discussion, and collaboration. This investment of class time allows students to work more actively and deeply with the contents, favoring the understanding and application of knowledge. On the other hand, gamification refers to the application of game elements and principles in the educational context. This includes the use of challenges, rewards, competitions, and immediate feedback to motivate students and encourage their engagement in learning. Gamification also promotes active participation, collaboration, and problem solving, which improves knowledge retention and the learning experience. By combining the flipped classroom with gamification, a dynamic and stimulating learning environment is created in higher education. Students have the opportunity to explore concepts on their own, before class, allowing them to arrive prepared and ready to more actively participate in classroom activities. In addition, gamification adds a playful and competitive element that motivates students to push themselves and improve themselves. From a systematic view, the gamified flipped classroom requires careful content planning, the appropriate selection of digital tools, and the design of challenging and meaningful activities. It also involves continuous assessment to measure student progress and adjust strategies accordingly.