Reviewerly/exHarmony huggingface dataset:
Enhancing Cross-lingual Biomedical Concept Normalization Using Deep Neural Network Pretrained Language Models

Abstract In this study, we propose a new approach for cross-lingual biomedical concept normalization, the process of mapping text in non-English documents to English concepts of a knowledge base. The resulting mappings, named as semantic annotations, enhance data integration and interoperability of documents in different languages. The US FDA (Food and Drug Administration), therefore, requires all submitted medical forms to be semantically annotated. These standardized medical forms are used in health care practice and biomedical research and are translated/adapted into various languages. Mapping them to the same concepts (normally in English) facilitates the comparison of multiple medical studies even cross-lingually. However, the translation and adaptation of these forms can cause them to deviate from its original text syntactically and in wording. This leads the conventional string matching methods to produce low-quality annotation results. Therefore, our new approach incorporates semantics into the cross-lingual concept normalization process. This is done using sentence embeddings generated by BERT-based pretrained language models. We evaluate the new approach by annotating entire questions of German medical forms with concepts in English, as required by the FDA. The new approach achieves an improvement of 136% in recall, 52% in precision and 66% in F-measure compared to the conventional string matching methods.
Data Governance Architecture of Digital Grid Based on Blockchain Technology and Nanomaterial Technology

As the traditional grid data architecture develops with enterprises, its security risks will become greater and greater, and it will not be able to satisfy customers' differentiated services. Predecessors' research on the application of blockchains and nanomaterials is still insufficient. To solve the various shortcomings of the traditional power grid data governance architecture, a new type of sensor designed using blockchain technology and nanomaterials is designed through new technical means. It is applicable to the current digital grid data management architecture. The study found that the scale of the system gradually increased from 4 nodes to 16 nodes, and the TPS entered the smoothing stage after about 10, 50, 70, and 100 s, and stabilized at a small fluctuation point. We found that for every 4 nodes increase in the cluster size, the throughput drops by about 8%. Clusters of different sizes have entered a state of stable throughput. Although TPS has shown an overall downward trend of 8%, the peak situation of TPS has shown a certain different trend. This shows that the digital grid data governance network framework designed through blockchain technology and nanomaterial technology can meet the various needs of modern digital grid data centers for data center networks, such as: business scalability, reliability, security, etc.
A New Hybrid Multiclass Approach Based on KNN and SVM

Support vector machine (SVM) is a machine learning method widely used in solving binary data classification problems due to its performance. Nevertheless, in practical problems of classification, there are often cases of the presence of more than two classes of objects in the original dataset. The paper considers a solution to the problem of SVM multiclass with the aim to increase the data classification quality based on a new way of hybridisation between SVM and [Formula: see text]-nearest neighbour (KNN) algorithms. The first phase of the approach is called the filtering phase. At this level, the feature space is split into two classes separated by a hyperplane. In the next step called review, we generate a second hyperplane, then we calculate the distance between each test pattern and the second hyperplane in the feature space using e.g. the KNN function. The result of the two phases is three classes instead of two produced by the conventional SVM. For evaluation purposes, dataset experiments are conducted on seven benchmark datasets that have high dimensionality and large size. Numerical experiments show that the 3SVM approach can improve not only the accuracy compared to other multiclass SVM approaches, but also the precision, recall, and [Formula: see text]-score.
Phase unwrapping algorithm using breadth-first-search and multi-level segmentation of phase quality interval in digital holography

A quality-guided phase unwrapping algorithm is proposed based on the breadth-first-search and multi-level segmentation of the phase quality interval. In the proposed method, the pixels having phase quality values below a threshold associated with a given segment are unwrapped based on the breadth-first-search strategy implemented in a recursive manner. The pixel selection based on the multi-level phase quality interval segmentation allows to perform noise-robust phase unwrapping and the recursive breadth-first-search technique offers computational efficiency in the implementation of proposed algorithm. Three segmentation strategies are investigated in reference to the phase unwrapping accuracy. Simulation and experimental results indicate that the proposed algorithm offers desirable trade-off between the phase unwrapping accuracy and computation time.
TriECCC: Trilingual Corpus of the Extraordinary Chambers in the Courts of Cambodia for Speech Recognition and Translation Studies

This paper presents an extended work on the trilingual spoken language translation corpus of the Extraordinary Chambers in the Courts of Cambodia (ECCC), namely TriECCC. TriECCC is a simultaneously spoken language translation corpus with parallel resources of speech and text in three languages: Khmer, English, and French. This corpus has approximately [Formula: see text] thousand utterances, approximately [Formula: see text], [Formula: see text], and [Formula: see text] h in length of speech, and [Formula: see text], [Formula: see text] and [Formula: see text] million words in text, in Khmer, English, and French, respectively. We first report the baseline results of machine translation (MT), and speech translation (ST) systems, which show reasonable performance. We then investigate the use of the ROVER method to combine multiple MT outputs and fine-tune the pre-trained English–French MT models to enhance the Khmer MT systems. Experimental results show that the ROVER is effective for combining English-to-Khmer and French-to-Khmer systems. Fine-tuning from both single and multiple parents shows the effective improvement on the BLEU scores for Khmer-to-English/French and English/French-to-Khmer MT systems.
Aligning Image Semantics and Label Concepts for Image Multi-Label Classification

Image multi-label classification task is mainly to correctly predict multiple object categories in the images. To capture the correlation between labels, graph convolution network based methods have to manually count the label co-occurrence probability from training data to construct a pre-defined graph as the input of graph network, which is inflexible and may degrade model generalizability. Moreover, most of the current methods cannot effectively align the learned salient object features with the label concepts, so that the predicted results of model may not be consistent with the image content. Therefore, how to learn the salient semantic features of images and capture the correlation between labels, and then effectively align them is one of the key to improve the performance of image multi-label classification task. To this end, we propose a novel image multi-label classification framework which aims to align I mage S emantics with L abel C oncepts ( ISLC ). Specifically, we propose a residual encoder to learn salient object features in the images, and exploit the self-attention layer in aligned decoder to automatically capture the correlation between labels. Then, we leverage the cross-attention layers in aligned decoder to align image semantic features with label concepts, so as to make the labels predicted by model more consistent with image content. Finally, the output features of the last layer of residual encoder and aligned decoder are fused to obtain the final output feature for classification. The proposed ISLC model achieves good performance on various prevalent multi-label image datasets such as MS-COCO 2014, PASCAL VOC 2007, VG-500, and NUS-WIDE with 87.2%, 96.9%, 39.4%, and 64.2%, respectively.
Disentanglement and a nonlinear Schrödinger equation

Abstract We explore a nonlinear term that can be added to the Schrödinger equation without violating unitarity of the time evolution. We find that the added term suppresses entanglement, without affecting the evolution of any product state. The dynamics generated by the modified Schrödinger equation is explored for the case of a two-spin 1/2 system.
Sign Language Gesture Recognition with Convolutional-Type Features on Ensemble Classifiers and Hybrid Artificial Neural Network

The proposed research deals with constructing a sign gesture recognition system to enable improved interaction between sign and non-sign users. With respect to this goal, five types of features are utilized—hand coordinates, convolutional features, convolutional features with finger angles, convolutional features on hand edges and convolutional features on binary robust invariant scalable keypoints—and trained on ensemble classifiers to accurately predict the label of the sign image provided as input. In addition, a hybrid artificial neural network is also fabricated that takes two of the aforementioned features, namely convolutional features and convolutional features on hand edges to precisely locate the hand region of the sign gesture under consideration in an attempt for classification. Experiments are also performed with convolutional neural networks on those benchmark datasets which are not accurately classified by the previous two methods. Overall, the proposed methodologies are able to handle a diverse variety of images that include labyrinthine backgrounds, user-specific distinctions, minuscule discrepancies between classes and image alterations. As a result, they are able to produce accuracies comparable with state-of-the-art literature.
Gradecast in Synchrony and Reliable Broadcast in Asynchrony with Optimal Resilience, Efficiency, and Unconditional Security

We revisit Gradecast (Feldman and Micali, STOC'88) in Synchrony and Reliable Broadcast (Bracha, Information and Computation'87) in Asynchrony. For both tasks ,we provide new protocols that have three desirable properties: (1) optimal resilience, tolerating t < n/3 malicious parties; (2) are communication-efficient, where honest parties send just O(nL) bits for a sender with a message of L = Ω(n logn) bits; (3) and are unconditionally secure, without needing to rely on any computational or setup assumptions (while having a statistical error probability). To the best of our knowledge, no previous work obtains all three properties simultaneously.
Knowledge-Based Scene Graph Generation with Visual Contextual Dependency

Scene graph generation is the basis of various computer vision applications, including image retrieval, visual question answering, and image captioning. Previous studies have relied on visual features or incorporated auxiliary information to predict object relationships. However, the rich semantics of external knowledge have not yet been fully utilized, and the combination of visual and auxiliary information can lead to visual dependencies, which impacts relationship prediction among objects. Therefore, we propose a novel knowledge-based model with adjustable visual contextual dependency. Our model has three key components. The first module extracts the visual features and bounding boxes in the input image. The second module uses two encoders to fully integrate visual information and external knowledge. Finally, visual context loss and visual relationship loss are introduced to adjust the visual dependency of the model. The difference between the initial prediction results and the visual dependency results is calculated to generate the dependency-corrected results. The proposed model can obtain better global and contextual information for predicting object relationships, and the visual dependencies can be adjusted through the two loss functions. The results of extensive experiments show that our model outperforms most existing methods.
CNN-Based Automatic Modulation Classification in OFDM Systems

Convolutional neural network (CNN)-based modulation classification schemes for orthogonal frequency division multiplexing (OFDM) signals have recently been reported. In this paper, we examine the effect of hyperparameters in a CNN model on classification performance and present improved performance of automatic modulation classification for OFDM signals. To do this, we first set a baseline CNN model for OFDM signal modulation classification and then conduct experiments by varying the hyperparameters, such as the size and number of convolution kernels, and the number of fully connected neurons, through computer simulations. We show that the kernel size has a dominant effect on the classification accuracy and should be large enough within an appropriate range to achieve high classification accuracy for a given in-phase and quadrature data set. Finally, we show that the tuned model outperforms the conventional work in terms of classification accuracy.
Towards Decentralized Identity Management in Multi-stakeholder 6G Networks

Trust-building mechanisms among network entities of different administrative domains will gain significant importance in 6G because a future mobile network will be operated cooperatively by a variety of different stakeholders rather than by a single mobile network operator. The use of trusted third party issued certificates for initial trust establishment in multi-stakeholder 6G networks is only advisable to a limited extent, as trusted third parties not only represent single point of failures or attacks, but they also cannot guarantee global independence due to national legislation and regulatory or political influence. This article proposes to decentralize identity management in 6G networks to enable secure mutual authentication between network entities of different trust domains without relying on a trusted third party and to empower network entities with the ability to shape and strengthen cross-domain trust relationships by the exchange of verifiable credentials. A reference model for decentralized identity management in 6G is given as an initial guide for the fundamental design of a common identity management system whose operation and governance are distributed equally across multiple trust domains of interconnected and multi-stakeholder 6G ecosystems.
Interactive design of physics learning media: The role of teachers and students in a teaching innovation

Abstract This paper reports how potential users are involved in the design of interactive learning media. This paper describes the responses of teachers and students about how learning media are applied in schools, a detailed description of the desired learning media and how learning media contribute to improve student learning outcomes. This research used quantitative and qualitative methods. The instruments were questionnaires and interview sheets for teachers and students. The data from research instruments of students included learning styles, responses to using learning media in the classroom, and responses to HOTS (Higher Order Thinking Skill) questions. The data from the teachers included their activities in the opening, core, and closure activity. In general, the media was expected to increase the enthusiasm and motivation of students in learning. For this reason, learning media that contains challenging questions and multimedia that attract students’ attention are developed based on media development indicators.
Good Practices in Implementing Energy Efficiency Measures in "Angel Kanchev" University of Ruse

The article presents the results of the change in the consumption of electricity and heat after the implementation of measures to improve energy efficiency at the University of Ruse. It has been proven that the set indicative targets have been met in advance. The nature of electricity and heat consumption for typical periods and months during the year is analyzed.
SUPPORTING ACADEMIC ENGAGEMENT THROUGH IMMERSIVE TECHNOLOGIES

"Academic engagement refers to the overall quality of students’ involvement with schooling, including their activities and goals, as well as their connections with peers and educators. Much research has examined the facilitation and support of students’ academic engagement within physical classroom settings. However, the field of education has been experiencing a shift from the status quo modus operandi of face-to-face instruction to online synchronous/asynchronous instruction, which has impacted students’ engagement. This change has increased the demand to develop and adapt digital technologies that can support the engagement of students throughout online learning processes and their adjustment to the new educational norm. Fundamental research on the development and implementation of immersive technologies could provide a way forward, however we maintain that the development of such technologies needs to be guided by current pedagogical and psychological theories. Hence, in this paper, first we examine empirically substantiated frameworks of engagement and identify aspects that require consideration when developing new immersive technologies. Then, we present a succinct review of the technology-enhanced learning environments literature to determine how engagement has (or has not) been supported through immersive technologies, i.e., virtual reality (VR), augmented reality (AR), and 3D volumetric video. Finally, having embarked on the development of our in-house technology, an immersive 3D video prototype, we present the technology setup alongside the co-creation process that we are implementing to guide its development. Based on pedagogical and psychological research, we highlight several vital factors substantiating students’ engagement, including the significance of the teacher’s role and the importance of teacher-student and student-student interactions. These factors serve to guide our qualitative data collection during co-creation sessions to uncover students’ and teachers’ new perspectives of engagement in relation to the affordances that immersive technologies should offer. Our work presents insights to educators, technology designers and researchers about important educational frameworks and considerations directing our development of immersive technologies in support of academic engagement."
The Unique Dressing of Transactions: Wasabi CoinJoin Transaction Detection

As Bitcoin’s blockchain allows third-party entities to track and analyse the transaction activity of Bitcoin users, external Privacy-Enhancing Technology services are created to satisfy the user demand for transaction privacy. These services are predominantly utilised as instruments in illegal activities. In this paper, we investigate one of the most prominent Privacy-Enhancing Technology services, Wasabi Wallet and its CoinJoin mixing function. We obtained Wasabi CoinJoin transactions for different time periods via published sources to observe the transaction patterns and changes in the obscuring mechanism. We propose a detection method derived from the discovered sets of transaction patterns. The results of our experiment show that the Wasabi Wallet has not substantially changed its CoinJoin mixing mechanism since the start of its operation and the majority of the Wasabi CoinJoin transactions are identifiable with significantly high precision. We also test the application of our detection method on several well-known cryptocurrency theft incidents and discover instances where the stolen Bitcoins directly reach the detected Wasabi CoinJoin Transactions.
Human-Computable OTP Generator as an Alternative of the Two-Factor Authentication

This work presents an improved alternative of the Two-Factor Authentication (2FA) standard. It eliminates the limitations concerning the necessity for additional gadgets, devices or theft-sensitive biometric data, by substituting it with direct human-computer authentication optionally enhanced by cognitive biometrics. This approach remains secure also in untrusted systems. On the other hand, it only permits one secret to be used as a universal private key for all attainable online accounts. This is an innovative challenge-response protocol for human-generated One-Time Passwords (OTP) based on a hard lattice problem with noise introduced by our new method which we call Learning with Options (LWO). The secret of this protocol possesses topographical properties of a single self-designed picture and a brief set of rules defining how these passwords are calculated. It is a heavy digital equivalent of a handwritten autograph. This paper demonstrates a simple and fast method to generate the OTP and hash-based digital signature, offline on paper documents usable as well, with an acceptable level of security and usability meeting the requirements for post-quantum symmetric ciphers and commercial implementation also in the field of IoT.
Improving LSTMs’ under-performance in Authorship Attribution for short texts

We present a novel approach for conducting authorship attribution over tweets using Long-Short Term Memory networks (LSTMs). Vanilla LSTMs use the last hidden state for prediction. Our strategy introduces a mechanism based on Max Pooling to process all the hidden states simultaneously, which helps the model to better detect authors’ stylometry. We obtain a 4% accuracy improvement with respect to vanilla LSTMs.
Controllability of an unmanned aerial vehicle

The article deals with the problem of analyzing the controllability of an unmanned aerial vehicle based on interval analysis, computer algebra. On the basis of interval mathematics, a criterion for the controllability of the system under study was obtained. When developing the software, the library of interval mathematics was used. The developed software that implements the introduced interval arithmetic operations can be used in the study of the dynamic properties of automatic control systems, energy, economic and other nonlinear systems.
ReSplit: Improving the Structure of Jupyter Notebooks by Re-Splitting Their Cells

Jupyter notebooks represent a unique format for programming - a combination of code and Markdown with rich formatting, separated into individual cells. We propose to perceive a Jupyter Notebook cell as a simplified and raw version of a programming function. Similar to functions, Jupyter cells should strive to contain singular, self-contained actions. At the same time, research shows that real-world notebooks fail to do so and suffer from the lack of proper structure. To combat this, we propose ReSplit, an algorithm for an automatic re-splitting of cells in Jupyter notebooks. The algorithm analyzes definition-usage chains in the notebook and consists of two parts - merging and splitting the cells. We ran the algorithm on a large corpus of notebooks to evaluate its performance and its overall effect on notebooks, and evaluated it by human experts: we showed them several notebooks in their original and the re-split form. In 29.5&#x0025; of cases, the re-split notebook was selected as the preferred way of perceiving the code. We analyze what influenced this decision and describe several individual cases in detail.