Reviewerly/exHarmony huggingface dataset:
Fast Multi-Object Tracking with Feature Pyramid and Region Proposal Networks

Many computer vision applications require real-time processing speeds, which prevents them from running an object detector on all frames of the sequence. In such circumstances, it is necessary to resort to motion estimation techniques in order to maintain the identity of the targets. This can be carried out by instantiating multiple single object trackers, if there are few targets, or through methods that globally extract the frame features, in order to share computations. The problem with the latter is that they yield features with limited semantic information and detect changes in the scene by performing multi-scale tests, which is inefficient and prone to errors. To solve these problems and provide accurate tracking for multiple objects in real-time, we propose SiamFAST. SiamFAST includes: a feature-pyramid-based region-of-interest extractor that produces quality features for both object exemplars and search areas; a pairwise depthwise region proposal network to compute fast similarities for several dozens of objects; and a multi-object penalization module in order to suppress the effect of distractors. SiamFAST has been validated on three public benchmarks, achieving leading performance against current state-of-the-art trackers.
On the Hardness of Computing the Edit Distance of Shallow Trees

We consider the edit distance problem on rooted ordered trees parameterized by the trees’ depth. For two trees of size at most n and depth at most d, the state-of-the-art solutions of Zhang and Shasha [SICOMP 1989] and Demaine et al. [TALG 2009] have runtimes $$\mathcal {O}(n^2d^2)$$ and $$\mathcal {O}(n^3)$$ , respectively, and are based on so-called decomposition algorithms. It has been recently shown by Bringmann et al. [TALG 2020] that, when $$d=\varTheta (n)$$ , one cannot compute the edit distance of two trees in $$\mathcal {O}(n^{3-\epsilon })$$ time (for any constant $$\epsilon >0$$ ) under the APSP hypothesis. However, for small values of d, it is not known whether the $$\mathcal {O}(n^2d^2)$$ upper bound of Zhang and Shasha is optimal. We make the following twofold contribution. First, we show that under the APSP hypothesis there is no algorithm with runtime $$\mathcal {O}(n^2d^{1-\epsilon })$$ (for any constant $$\epsilon >0$$ ) when $$d=\mathrm poly(n)$$ . Second, we show that there is no decomposition algorithm that runs in time $$o(\min \{n^2d^2, n^3\})$$ .
A Multimodal Deep Fusion Network for Mobile Traffic Classification

With the explosive growth of mobile traffic and the demand for privacy protection and network security, mainstream mobile applications use encryption protocols (mostly TLS), so identifying mobile encrypted traffic has become critical. Machine learning methods are based on hand-designed features that are unreliable in the face of complex traffic data. Deep learning currently performs well on this task, but most of them only describe traffic data from one view, ignoring the heterogeneous nature of traffic. In this paper, we apply multimodal Transformers to mobile encrypted traffic classification and propose a novel model (DF-Net) with a deep fusion mechanism. The key point of deep fusion is that a learnable modal-type embedding enables the model to perform early and unconstrained fusion and interaction of cross-modal information to achieve performance improvements. On the premise of ensuring performance, DF-Net adopts lightweight design and the parallel mechanism to improve the overall efficiency of the model. To verify the performance and efficiency of DF-Net, we implement an automated traffic collection framework to collect a real-world traffic dataset that covers 48 popular apps. Experiments show that DF-Net not only achieves excellent performance but also more efficient compared to state-of-the-art methods.
Latent Pattern Identification Using Orthogonal-Constraint Coupled Nonnegative Matrix Factorization

The recent advancements and developments in Intelligent Transportation Systems (ITS) lead to the generation of abundant spatio-temporal traffic data. Identifying or understanding the latent patterns present in these spatio-temporal traffic data is very much essential and also challenging due to the fact that there is a chance of obtaining duplicate or similar patterns during the process of common pattern identification. This paper proposes an Orthogonal-Constraint Coupled Nonnegative Matrix Factorization (OC-CNMF) method and studies how to effectively identify the common as well as distinctive patterns that are hidden in the spatio-temporal traffic-related datasets. The distinctiveness of the patterns is achieved by the imposition of the orthogonality constraint in CNMF during the process of factorization. The imposition of the orthogonal constraint helps to ignore similar/duplicate patterns among the identification of the common patterns. We have shown that imposing orthogonality constraint in CNMF improves the convergence performance of the model and is able to identify common as well as distinctive patterns. Also, the performance of the OC-CNMF model is evaluated by comparing it with various performance evaluation measures.
Preserve Knowledge with Auxiliary Feature Extractor for Class Incremental Learning

Class incremental learning (CIL) aims to achieve the ability to learn knowledge from the data of novel classes that arrive incrementally. To this end, the exemplar-based method stores a small number of samples of old classes and has been proven to be effective yet it causes the severe data imbalance issue. An approach named SS-IL solves the issue effectively and achieves strong state-of-the-art on large-scale CIL benchmark datasets while behaving badly on small ones. In this paper, we observe that the poor performance of SS-IL on small datasets could stem from not fully stimulating the potentiality of the learned representation of old classes, especially the initial classes. We propose an auxiliary Weight Scaling Feature Extractor (aWSFE) to better maintain and exploit the essential semantics of old classes. This auxiliary extractor is used as a plug-in module with the main classification network based on SS-IL in parallel. We perform a special design for the two branches so that the feature vectors from the main and auxiliary extractor can be integrated easily without an additional aggregation process. After obtaining the updated representations, we finetuning the classifier based on a balanced subset of training data to further promote performance. We conduct extensive experiments on two small-scale CIL benchmark datasets: CIFAR-100 and ImageNet-Sub. Results show that the proposed method effectively alleviates the forgetting of old knowledge and significantly improves the performance of SS-IL on small datasets.
Digital University – University 4.0

The article discusses how digital transformation and the creation of a Digital University, also known as a University 4.0, can update the educational system. The major obstacles to creating a new educational system are examined, as well as solutions.The key tasks in building a new digital university are the introduction of new procedures for the design, production, systematization, storage, and transmission of educational information, as well as the modernization of the management and administration of the educational process. The integration of e-learning into the university system’s overall corporate information system and business architecture is based on integrated organizational and functional structures, IT architectures with their own rules, responsibilities, people, processes, systems of interaction and relationships, i.e. a new business structure built into the university system’s overall corporate information system and business architecture.The article explains how the university’s business procedures, services, and IT infrastructure enable the seamless running of University 4.0.
An Analysis of Ecolexicons Found in Selected Petatah Petitih in Nagari Gunuang

Human use language in their interaction in daily life to convey or deliver their thoughts and communicate with each other. This research belonged to the descriptive qualitative research to find out ecolexicons and the description of each ecolexicon based on the knowledge of community in Nagari Gunuang using triple dimension of social praxis. Based on data analysis and discussion found 30 words belonged to ecolexicons. Afterward, the data were classified into three categories biotic flora, biotic fauna, and abiotic. The researcher found 28 biotic lexicons, 22 of them are categorized as flora lexicons and 6 of them are categorized as fauna lexicons. On the other hand, abiotic lexicon was found only 2 by the researcher. Lexicons about biotic flora are considered as the most dominant type of ecolexicons that found. Among 22 biotic flora lexicons, 4 of them are classified as death words because it no longer used in daily life and unrecognized by the informant or society.
Generalized 3-Valued Belief States in Conformant Planning

The high complexity of planning with partial observability has motivated to find compact representations of belief state (sets of states) that reduce their size exponentially, including the 3-valued literal-based approximations by Baral et al. and tag-based approximations by Palacios and Geffner. We present a generalization of 3-valued literal-based approximations, and an algorithm that analyzes a succinctly represented planning problem to derive a set of formulas the truth of which accurately represents any reachable belief state. This set is not limited to literals and can contain arbitrary formulas. We demonstrate that a factored representation of belief states based on this analysis enables fully automated reduction of conformant planning problems to classical planning, bypassing some of the limitations of earlier approaches.
Jointly Trained Conversion Model With LPCNet for Any-to-One Voice Conversion Using Speaker-Independent Linguistic Features

We propose a joint training scheme of an any-to-one voice conversion (VC) system with LPCNet to improve the speech naturalness, speaker similarity, and intelligibility of the converted speech. Recent advancements in neural-based vocoders, such as LPCNet, have enabled the production of more natural and clear speech. However, other components in typical VC systems are often designed independently, such as the conversion model. Hence, separate training strategies are used for each component that is not in direct correlation to the training objective of the vocoder preventing exploitation of the full potential of LPCNet. This problem is addressed by proposing a jointly trained conversion model and LPCNet. To accurately capture the linguistic contents of the given utterance, we use speaker-independent (SI) features derived from an automatic speech recognition (ASR) model trained using a mixed-language speech corpus. Subsequently, a conversion model maps the SI features to the acoustic representations used as input features to LPCNet. The possibility to synthesize cross-language speech using the proposed approach is also explored in this paper. Experimental results show that the proposed model can achieve real-time VC, unlocking the full potential of LPCNet and outperforming the state of the art.
Research on Rebar Counting Based on YOLO-Rebar Model

Smart site is a general trend in the development of the construction industry, aiming to design and manage the site in a controllable, visualized and data-oriented way, which is of great significance to strengthen the safety and civilized construction management of the site. An improved YOLO-Rebar model is proposed by this paper for counting rebar based on the fact that there are not ground true boxes located at YOLOv3 13*13-sized channel for detecting large objects in images. The 26*26-sized and 52*52-sized channels for predicting medium and small objects of images are reserved in YOLO-Rebar, so that convolution layers are reduced by 17. Trainable parameters is reduced by 41,657,874. Moreover, the average training time of different epochs decrease by 30%, GPU memory consumption decreases by 43% and the size of model weight files cut down by 68%. The experiments show that, when epoch > 30, YOLO-Rebar achieved the same Rebar counting performance of YOLOv3 with less training time, lower GPU memory occupation and smaller model weight file size.
Impact of Nonbinary Input Vectors on Security of Tree Parity Machine

Key agreement protocol is an essential step for establishing a secure connection. The inevitable advancements in quantum computing technologies pose a huge threat to the key agreement protocol in use today. Neural cryptography is an alternative key agreement protocol that is not susceptible to any known quantum algorithm. Since the invention of mutual learning of TPM, many improvements have been proposed. One of them was the usage of nonbinary input vectors. This study verifies the impact of nonbinary input vectors on TPM security features. A number of iterations, similarity to intruder’s TPM and effective key length were taken into account. The results show that the choice between fast synchronization times and higher security of the final key must be performed with perfect care.
Amalgamation of Embeddings With Model Explainability for Sentiment Analysis

Regarding the ubiquity of digitalization and electronic processing, an automated review processing system, also known as sentiment analysis, is crucial. There were many architectures and word embeddings employed for effective sentiment analysis. Deep learning is now-a-days becoming prominent for solving these problems as huge amounts of data get generated per second. In deep learning, word embedding acts as a feature representative and plays an important role. This paper proposed a novel deep learning architecture which represents hybrid embedding techniques that address polysemy, semantic and syntactic issues of a language model, along with justifying the model prediction. The model is evaluated on sentiment identification tasks, obtaining the result as F1-score 0.9254 and F1-score 0.88, for MR and Kindle dataset respectively. The proposed model outperforms many current techniques for both tasks in experiments, suggesting that combining context-free and context-dependent text representations potentially capture complementary features of word meaning. The model decisions justified with the help of visualization techniques such as t-SNE.
Event Generalization Based on Chinese Event Corpus

With the continuous update and development of the Internet industry, a large number of news event information of communication has emerged, and the news event information in the objective world is also increasing by hundreds of millions every day. People usually take events as a unit to carry out thinking activities. However, the increase in the amount of event information makes it difficult for people to accurately and quickly find the rules between events. Offered to solve these problems, event classes for the study of the basic unit of knowledge representation, the trigger word, class from the event object elements and environment were put forward based on synonym word event trigger word candidates found, Lin found based on big words Lin event class object elements and is based on the analysis of the interdependence syntactic event classes environmental elements found method. The experimental results show that the proposed methods for the three elements are effective. It provides a basis for theoretical research on events, and also contributes to the construction of event knowledge graph for practical application in the later stage.
Multi-scale Adaptive Learning Network with Double Connection Mechanism for Super-resolution on Agricultural Pest Images

Accurate recognition of pests can effectively reduce the negative impact of pests on the agricultural economy. The unprofessional shooting ways result in low-resolution images, which seriously influences the accuracy of pest recognition. The super-resolution reconstruction method can transform low-resolution images into high-resolution images. The existing super-resolution reconstruction networks have the problems such as feature missing, low feature utilization, and a large amount of computation. The Multi-scale Adaptive Learning Network (MALNet) proposed in this paper is based on residual and dense connections and consists of four modules: Rear-Compensation Enlargement Module (RCEM), Low-Frequency Feature Building Module (LFFBM), Multi-scale Details Representation Module (MsDRM), and Feature Channels Correction Module (FCCM). First, the low-frequency contour feature is built by LFFBM, and MsDRM extracts local detail features of the pest. After that, FCCM enhances important features and finally fuses with the enlarged feature map generated by RCEM. The experimental results show that, compared with other reconstruction networks, MALNet achieves an average improvement of 0.20dB and 0.011 in Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) on Te-set and public benchmark datasets Set5, Set14, BSDS100, and Urban100. It overcomes the shortcomings of the existing networks and provides strong support for the efficient implementation of pest recognition work.
Fitness Space Design Based on Mixed Reality Technology Research

In this paper, we study the design characteristics of fitness spaces based on virtual reality technology. Based on the theory of virtual reality, this study analyzes the characteristics of interaction design in fitness space and explores novel interaction designs with good user experience. Fitness space application of virtual reality technology has changed the traditional design concept, has broken the constraints of time and space, enriched the forms of fitness space, improved the enthusiasm of the user at the same time points out the diversity of interaction design will be the future development direction of fitness space, and to increase urban residents' sports enthusiasm to improve sub-health plays a vital role.
Deep Learning for Porous Media Classification Based on Micro-CT Images

Deep convolutional neural networks have strong data structure mining ability and can be successfully applied to facilitate the feature detection process when applied to porous media analysis. One of the encountered limitations is the lack of large data when applying deep learning algorithms. This work proposes a novel approach to overcome this problem by generating highly representative datasets based on real CT-images. The method uses an original morphological concept for the determination of pore size distribution to provide input data that is to be fed to a designed neural network. The generated data was employed for the classification of soil aggregates that differ in their pore size distribution. The image classification results were achieved by exploiting well-known pre-trained deep learning models: VGG-16, ResNet50, InceptionV3, Dense-Net121, and MobileNet. We applied k-fold cross-validation for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> equal to five to validate the results. The average accuracy for the validating data achieved for the MobileNet, VGG-16, InceptionV3 and DenseNet121 ranged from 90% to 95% and were 5% higher compared to ResNet50. The deep learning approach has demonstrated great promise for analyzing very complex and irregular structures based on micro-CT images.
Active Aging with Smart Technologies: What Designers Can Ask When Designing Smart Products for Older Users

This paper presents a study of the engagement of active ageing users with social robots. Advanced digital technologies bring new challenges and opportunities for designers and users. The ways in which older generations interact with smart products deserve a closer examination. The purpose of this work is to develop insights and promote empathic reflections on active ageing users’ interaction with technology to inform the design of future smart products. Influenced by ontological realism and social constructionism, this research applies a Multimodal Extended User Research approach to unpack the social and emotional aspects of active ageing users’ experiences with new technologies. Three data collection stages included two rounds of interviews, a usability test, and an extended user experience of fifteen participants in their home environments. The study recommends designers to step away from the stereotyped views on active ageing users and recognises this user group as a knowledgeable and reflective demographic with ample expertise with disruptive technological changes. We argue that design practices need to account for social and subjective experiences rather than only focusing on users’ emotional ratings of the experience. Lastly, this research advocates for a critical analysis of companion technologies and recommends design practices to avoid generalising what “companionship” may mean to users.
MULTI-THREAD PARALLELIZING OF CELL CHARACTERISTICS OF BIOMEDICAL IMAGES

An approach to the parallelization of the process of calculating the quantitative characteristics of cell nuclei on biomedical images (cytological, histological, immunohistochemical) is proposed, which will speed up the process of making a diagnosis. The relevance of this task lies in the fact that there are a large number of micro-objects in the image that need to be investigated, and optimization of time and rational distribution of resources will speed up the stage of calculating the area of cell nuclei and their average brightness level. In the future, these data are stored in the database for further use as a dataset for the tasks of classification, clustering, and intellectual analysis. Modern means of data classification and intellectual analysis are used to make a diagnosis. When using convolutional neural networks, the input data to the classifier are images in the format .jpg, .png, .bmp, etc. Alternative algorithms and data processing tools in most cases require quantitative characteristics. In the case of using biomedical images, the quantitative characteristics are the area, perimeter, circumference, length, and major and lateral axes of the cell nucleus. The area and other characteristics of cell nuclei characterize the normal state or the presence of pathologies. Calculating quantitative characteristics on immunohistochemical and cytological images is time-consuming because the number of cell nuclei in one image can be in the range of 10-20 units. To create a dataset of quantitative characteristics of cell nuclei, it is necessary to perform calculations on a large number of images, which in turn requires significant resources, at a particular time. The parallelization of calculating the biomedical image characteristics is implemented on the basis of computer vision algorithms to select the necessary objects and means of software parallelization of tasks at the thread level to speed up the process of calculating the cell nucleus characteristics. It was established that the existing systems of automated microscopy and diagnostic systems based on images do not have the presence of a large number of characteristics of cell nuclei and do not have mechanisms for parallelizing the process of their calculation. The proposed approach makes it possible to speed up the process of calculating the quantitative characteristics of cell nuclei by 25 %. The relevance of the problem of parallelization is due to the need to process a large amount of data for their further reduction and classification. Thread-level parallelization improves image processing speed and does not require specialized hardware.