Reviewerly/exHarmony huggingface dataset:
Design Thinking As a Method of Organising Group Work

The relevance of the study is due to the fact that the tasks facing managers and staff today are unprecedented and do not have ready-made solutions. For this reason, group formats of management, development and work are becoming more important: the team first of all allows you to find social environment in which you can quickly generate and filter solutions, learn from each other. The aim of the study is to confirm the hypothesis of the group work effectiveness, to assess how all team members have mutual influence. This study presents methods and techniques for organising group work at various stages of project management, how to find a balance between an individual and group activity. The scientific novelty lies in the fact that the author presents the mechanism of design thinking as a methodology that will allow exploring the surrounding reality, overcoming stereotypes and testing hypotheses in practice, and as a tool, it puts the client at the centre. Design thinking, as a practice-oriented methodology, helps to solve specific problems, to determine specific metrics, to check through data whether the solution satisfies the needs and is feasible. The strength of the design thinking methodology is allowing “playing” as you like with a variety of tools, methods and techniques. Design thinking, through the process of generating ideas and constantly testing hypotheses, leads to disclosing the group artistic potential and creates a special atmosphere of freedom and creative search.
An Object Detection Method Based on Feature Uncertainty Domain Adaptation for Autonomous Driving

The environment perception algorithm in autonomous driving is trained in the source domain, leading to domain drift and reduced detection accuracy in the target domain due to shifts in background feature distribution. To address this issue, a domain adaptive object detection algorithm based on feature uncertainty is proposed, which can improve the detection performance of object detection algorithms in unlabeled data. Firstly, a local alignment module based on channel information is proposed, which can obtain the model’s uncertainty about different domain data based on the feature channels obtained through the feature extraction network, achieving adaptive dynamic local alignment. Secondly, an instance-level alignment module guided by local feature uncertainty is proposed, which can obtain the corresponding instance-level uncertainty through ROI mapping. To improve the domain invariance of bounding box regression, a multi-class, multi-regression instance-level uncertainty alignment module is proposed, which can achieve spatial decoupling of classification and regression tasks, further improving the model’s domain adaptive ability. Finally, the effectiveness of the proposed algorithm is validated on Cityscapes, KITTI, and real vehicle data.
Methods for Summing up the Work of the Main Structural Divisions of the University

Summing up the work results of an educational institution is an important component of the learning activities of universities and colleges. The paper considers the method of summing up the results of the university main structural subdivisions, namely the chair, faculty, departments and divisions, including the criteria and indicators for their assessment and the employees’ activities evaluation. The methods allow determining the educational institution achievements, shortcomings in the work of structural units and their causes. The methodology also makes it possible to specify the tasks aimed at improving educational activities, and to develop organisational and methodological measures aimed at improving the efficiency of the university structural units and the graduates’ quality of education. An important component of summing up is also the employees’ motivation to achieve high results in educational activities.
A voice search engine for military symbols to enhance the drafting of operational plan documents on digital map

The process of searching for information to serve the construction of operational plan documents on a digital map is still being done manually and needs to be automated in order to improve efficiency. Speech recognition and natural language processing technologies, commonly used in chatbots, virtual assistants, voice commands, and voice search, could be promising tools to overcome this problem. This paper proposes a framework for deploying a voice search engine that uses Whisper, a deep learning-based automatic speech recognition model, and combines TF-IDF, N-gram, and Truncated SVD as feature extraction approaches to search for text ground truth in a dictionary of military symbols using Cosine similarity. Despite the small size of a custom dataset, the experiments show promising results, achieving an accuracy of 82.00%. Our achievement surpasses that of several traditional statistical methods and classification models.
Path planning for multi-copter UAVs using tutorial training and self learning inspired teaching-learning-based optimization

Route preparation for drones is a complex method to achieve an optimal path and meet constraints following specific tasks. This paper addresses the problem of a planning method for a multi-copter unmanned aerial vehicle (UAV) to examine ground surfaces. A multi-objective route planning algorithm, named the tutorial training and self learning inspired teaching learning-based optimization (TS-TLBO), is then introduced to create a feasible and flyable path while avoiding obstacles. Here, we first select a joint cost function that includes different constraints to improve operational safety, at the same time, to meet task requirements. The path-tracking scheme is then applied in the quadcopter to verify the proposed approach. Experiment results show the workability of our proposed path planning process.
Multilingual Text Summarization for German Texts Using Transformer Models

The tremendous increase in documents available on the Web has turned finding the relevant pieces of information into a challenging, tedious, and time-consuming activity. Text summarization is an important natural language processing (NLP) task used to reduce the reading requirements of text. Automatic text summarization is an NLP task that consists of creating a shorter version of a text document which is coherent and maintains the most relevant information of the original text. In recent years, automatic text summarization has received significant attention, as it can be applied to a wide range of applications such as the extraction of highlights from scientific papers or the generation of summaries of news articles. In this research project, we are focused mainly on abstractive text summarization that extracts the most important contents from a text in a rephrased form. The main purpose of this project is to summarize texts in German. Unfortunately, most pretrained models are only available for English. We therefore focused on the German BERT multilingual model and the BART monolingual model for English, with a consideration of translation possibilities. As the source of the experiment setup, took the German Wikipedia article dataset and compared how well the multilingual model performed for German text summarization when compared to using machine-translated text summaries from monolingual English language models. We used the ROUGE-1 metric to analyze the quality of the text summarization.
Thinking inside the box: An evaluation of a novel search‐assisting tool for supporting (meta)cognition during exploratory search

Abstract Exploratory searches involve significant cognitively demanding aiming at learning and investigation. However, users gain little support from search engines for their cognitive and metacognitive activities (e.g., discovery, synthesis, planning, transformation, monitoring, and reflection) during exploratory searches. To better support the exploratory search process, we designed a new search assistance tool called OrgBox. OrgBox allows users to drag‐and‐drop information they find during searches into “boxes” and “items” that can be created, labeled, and rearranged on a canvas. We conducted a controlled, within‐subjects user study with 24 participants to evaluate the OrgBox versus a baseline tool called the OrgDoc that supported rich‐text features. Our findings show that participants perceived the OrgBox tool to provide more support for grouping and reorganizing information, tracking thought processes, planning and monitoring search and task processes, and gaining a visual overview of the collected information. The usability test reveals users' preferences for simplicity, familiarity, and flexibility of the design of OrgBox, along with technical problems such as delay of response and restrictions of use. Our results have implications for the design of search‐assisting systems that encourage cognitive and metacognitive activities during exploratory search processes.
Iannis Xenakis in Argentina: Reception, Dialogues, and Exchanges

Abstract Iannis Xenakis visited Argentina as a professor of the Centro Latinoamericano de Altos Estudios Musicales (CLAEM) of the Torcuato Di Tella Institute in 1966 where he discussed his interdisciplinary interests – mathematics, architecture, and computer-aided composition. Using archival resources and oral history, this article explores three aspects of Xenakis's trip to Argentina. First, the way the media framed the visit of the European composer and the reception of the events organized around it under a modernist discourse. Second, the direct impact that this visit had on some of the composers at CLAEM, particularly Graciela Paraskevaídis. Finally, the exchanges that Xenakis had with engineer Fernando von Reichenbach, who had been working on transforming graphic material into sound, something that crystallized with the development of Reichenbach's Convertidor Gráfico Analógico (1969), and Xenakis's Unité Polyagogique Informatique CEMAMu (UPIC 1977).
Image Resizing for Object Detection: A Learnable Downsampler–Upsampler Pair with Differentiable Image Entropy Estimation

In recent years, super-resolution neural networks have achieved good results in restoring super-resolution images from low-resolution ones. However, most subsequent tasks based on super-resolution images such as object detection are done by the computer. Considering this situation, we propose a learnable downsampler–upsampler pair, which can realize both the downscaling process and the upscaling process by neural networks, and is jointly trained with the YOLOV5 network to optimize the object detection task. Thus, different from existing super-resolution networks, the entire downsampler–upsampler pair is optimized for machine perception. In addition, to further reduce the size of the downsampled images, we also propose a differentiable method for estimating image entropy and add it to the loss function. We verify the effectiveness of our method on the pothole dataset and use scale factors 2× and 4× to prove that our method is capable of diverse resizing levels. The experimental results show that using our learnable downsampler–upsampler pair as a resizing method can highly improve the detection performance compared with other resizing techniques.
A Wavelet Basis ANN and 5-Class Decision Factor AI Algorithm

The accuracy and reliability of continuous space curve estimation is the key to global exploration. An improved artificial intelligence algorithm is proposed for continuous space analysis. First, a wavelet basis ANN algorithm is proposed to determine the discretization strategy in continuous space. The hidden layer node transfer function in a BP neural network is replaced by a wavelet basis function, and the modified BP neural network is composed of a wavelet neural network. Second, an improved wolf algorithm is established. The core wolf system ensures the precision of the whole exploration task. Finally, main and auxiliary double cores and a five-class decision factor are used to establish a population classification model to solve the convergence of the algorithm.
An experimental investigation of P/Case‑drop in Korean gapping

Abstract This study examines two syntactic analyses of P/Case-drop in Korean gapping ( aka right-node-raising or right-peripheral ellipsis): LF copying ( Abe &amp; Hoshi 1997 ) and PF deletion ( Kim 1997 ). We employ two online acceptability rating experiments to investigate to what extent the distribution of P/Case-drop is controlled by grammatical and extra-grammatical constraints. The experimental findings suggest that (a) linear non-parallelism elicits a processing cost for gapping and (b) P/Case-drop is a costly operation, which results in higher frequencies of PP fragments (in relation to NP fragments) and Case-marked NP fragments (in relation to Case-less NP fragments). We argue that the parallelism effect follows from the parser’s general preference to keep the structure of each conjunct maximally parallel in a coordination structure ( Kim et al. 2020 ). Given this, we conclude that P/Case-drop phenomena in Korean gapping are better explained by a PF deletion analysis, supplemented with extra deletion ( An 2016 , 2019 ; Erschler 2022 ) and ellipsis parallelism ( Frazier, Munn &amp; Clifton 2000 ; Kehler 2000 ; Frazier &amp; Clifton 2001 ; Carlson 2002 ), rather than by an LF copying analysis.
Mathematical Modeling of Mass Transfer in Complex Engineering and Biomedical Systems

We consider various analytic methods intended for the evaluation of operating parameters of the process of gas motion in the main technological objects of gas transportation systems. On the basis of these methods, we develop the mathematical models aimed at determining the parameters of the operation of the underground gas storages. In view of the similarity of hydro-, gas-, and hemodynamic processes, we extend the accumulated results to the case of modeling of the process of blood circulation in large blood vessels and soft tissues of the living organisms.
A backdoor attack against quantum neural networks with limited information

Backdoor attacks are emerging security threats to deep neural networks. In these attacks, adversaries manipulate the network by constructing training samples embedded with backdoor triggers. The backdoored model performs as expected on clean test samples but consistently misclassifies samples containing the backdoor trigger as a specific target label. While quantum neural networks (QNNs) have shown promise in surpassing their classical counterparts in certain machine learning tasks, they are also susceptible to backdoor attacks. However, current attacks on QNNs are constrained by the adversary’s understanding of the model structure and specific encoding methods. Given the diversity of encoding methods and model structures in QNNs, the effectiveness of such backdoor attacks remains uncertain. In this paper, we propose an algorithm that leverages dataset-based optimization to initiate backdoor attacks. A malicious adversary can embed backdoor triggers into a QNN model by poisoning only a small portion of the data. The victim QNN maintains high accuracy on clean test samples without the trigger but outputs the target label set by the adversary when predicting samples with the trigger. Furthermore, our proposed attack cannot be easily resisted by existing backdoor detection methods.
Self‐supervised depth completion with multi‐view geometric constraints

Abstract Self‐supervised learning‐based depth completion is a cost‐effective way for 3D environment perception. However, it is also a challenging task because sparse depth may deactivate neural networks. In this paper, a novel Sparse‐Dense Depth Consistency Loss (SDDCL) is proposed to penalize not only the estimated depth map with sparse input points but also consecutive completed dense depth maps. Combined with the pose consistency loss, a new self‐supervised learning scheme is developed, using multi‐view geometric constraints, to achieve more accurate depth completion results. Moreover, to tackle the sparsity issue of input depth, a Quasi Dense Representations (QDR) module with triplet branches for spatial pyramid pooling is proposed to produce more dense feature maps. Extensive experimental results on VOID, NYUv2, and KITTI datasets show that the method outperforms state‐of‐the‐art self‐supervised depth completion methods.
Model Compression Techniques in Deep Neural Networks

With the current set-up, the success of Deep Neural Network models is highly tied to their size. Although this property might help them improve their performance, it makes them difficult to train, deploy them on resource-constrained machines, and iterate on experiments. There is also a growing concern about their environmental and economic impacts. Model compression is a set of techniques that are applied to reduce the size of Neural Network (NN) models while maintaining their performance. This survey presents state-of-the-art model compression methods, discusses how they are hybridized, highlights the changes they could cause beyond size reduction, and puts forward open research problems for further study.
SE Block-Assisted ResNet for Channel Estimation in OFDM System

Channel estimation (CE) is an important part of wireless communication system, which has a significant impact on the quality of wireless communication. Considering a single-input single-output (SISO) downlink scenario, this paper proposes a “squeeze and excitation” (SE) block combined with residual neural network (SE-ResNet) method to improve the CE performance of the orthogonal frequency division multiplexing (OFDM) system. The SE-ResNet is inputted into a CSI matrix of the pilot position obtained by the least squares (LS) method, and a raw feature matrix is learned by the convolutional layer and a partial residual layer. And the global information of the original feature matrix in each channel is compressed into a descriptor through the squeeze operation in the “SE” block. Then attention map is obtained from information aggregated in the descriptors by an excitation operation which fully captures channel-wise dependencies. The attention map is multiplied by the original feature matrix to get a new feature matrix, which is resized by an interpolation layer to obtain CSI of entire frame. New feature matrix is helpful for interpolation layer to get more accurate complete CSI. To further improve CE performance and optimize network model, this paper adopts methods of network tailoring and Concrete Autoencoder (Concrete AE) for pilot design. Simulation results show that the proposed SE-ResNet is superior to the traditional LS and minimum mean squared error (MMSE) methods in various practical wireless environments. Network pruning will reduce the number of parameters of the network within an acceptable loss range, which will reduce computational costs. Using the pilot scheme designed by Concrete AE for CE will have better performance.
Morpheme Based Amharic-Kistanigna Bi-directional Machine Translation Using Deep Learning

Machine translation is a natural language processing application that can be used to translate text from one natural language to other natural language. This study attempted to create bidirectional machine translation for Amharic-Kistanigna language. It is critical to develop machine translation between Kistanigna and Amharic languages in order to increase the number of language users, address issues concerning the endangered Kistanigna language, and expand the language’s web contentṄeural Machine Translation is a new approach to machine translation that has achieved a translation quality. The experiments are carried out using LSTM, Bi-LSTM, LSTM + attention, CNN + attention, and Transformer models. We have used 9,225 parallel sentences with morpheme based translation unit. To segment our morpheme data we used the morfessor tool. We considered training time, memory usage, and BLEU score when proposing an optimal model. Finally, we proposed morpheme-based bi-directional machine translation using Transformer with BLEU scores of 21.31 and 22.40 for Amharic-Kistanigna and Kistanigna-Amharic translation respectively. The study’s main weakness is the lack of sufficient datasets to conduct a comprehensive experiment. As a result, parallel corpora are required for conducting similar research.
Using Generative Adversarial Networks for Single Image Super-Resolution

In digital image processing, retrieving a high-resolution image from its low-resolution version is considered to be a major topic. Super-resolution SR is a problem that has direct applications in numerous disciplines like medical diagnosis, satellite imagery, face recognition and surveillance. The choice of the optimization function has been a major factor in previous super-resolution approaches. Optimizing metrics that are determined based primarily on pixel-level variance is the most common objective for supervised super-resolution algorithms. Nevertheless, these approaches do not output perceptually satisfactory images. This paper adopts an idea in which depending on only pixel-space similarity is avoided. Instead, the major goal is to utilize a content loss based on perceptual resemblance using feature maps of the VGG network in conjunction with Generative Adversarial Networks GAN. This depends on training two networks: a generator and a discriminator. In an adversarial game, they compete to outperform each other with an ultimate objective of producing super-resolution images that are identical to the real high-resolution images that already exist in the dataset. This paper’s main contribution is a comparison of the effects of taking the VGG-19 content loss from various layers. On public benchmarks, super-resolution GAN was successful in recovering detailed textures from highly downsampled images. SRGAN reveals large gains in perceptual quality in a mean opinion score MOS test.