Reviewerly/exHarmony huggingface dataset:
Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation

Video Panoptic Segmentation (VPS) aims at assigning a class label to each pixel, uniquely segmenting and identifying all object instances consistently across all frames. Classic solutions usually decompose the VPS task into several subtasks and utilize multiple surrogates (e.g. boxes and masks, centers and offsets) to represent objects. However, this divide-and-conquer strategy requires complex post-processing in both spatial and temporal domains and is vulnerable to failures from surrogate tasks. In this paper, inspired by object-centric learning which learns compact and robust object representations, we present Slot- VPS, the first end-to-end framework for this task. We encode all panoptic entities in a video, including both foreground instances and background semantics, with a unified representation called panoptic slots. The coherent spatio-temporal object's information is retrieved and encoded into the panoptic slots by the proposed Video Panoptic Retriever, enabling to localize, segment, differentiate, and associate objects in a unified manner. Finally, the output panoptic slots can be directly converted into the class, mask, and object ID of panoptic objects in the video. We conduct extensive ablation studies and demonstrate the effectiveness of our approach on two benchmark datasets, Cityscapes- VP S (val and test sets) and VIPER (val set), achieving new state-of-the-art performance of 63.7, 63.3 and 56.2 VPQ, respectively.
BenchPilot: Repeatable &amp; Reproducible Benchmarking for Edge Micro-DCs

Micro-Datacenters (DCs) are emerging as key en-ablers for Edge computing and 5G mobile networks by pro-viding processing power closer to IoT devices to extract timely analytic insights. However, the performance evaluation of data stream processing on micro-DCs is a daunting task due to difficulties raised by the time-consuming setup, configuration and heterogeneity of the underlying environment. To address these challenges, we introduce BenchPilot, a modular and highly customizable benchmarking framework for edge micro-DCs. BenchPilot provides a high-level declarative model for describing experiment testbeds and scenarios that automates the bench-marking process on Streaming Distributed Processing Engines (SDPEs). The latter enables users to focus on performance analysis instead of dealing with the complex and time-consuming setup. BenchPilot instantiates the underlying cluster, performs repeatable experimentation, and provides a unified monitoring stack in heterogeneous Micro-DCs. To highlight the usability of BenchPilot, we conduct experiments on two popular streaming engines, namely Apache Storm and Flink. Our experiments compare the engines based on performance, CPU utilization, energy consumption, temperature, and network I/O.
Evaluation of Synthetic Data Generation Techniques in the Domain of Anonymous Traffic Classification

Anonymous network traffic is more pervasive than ever due to the accessibility of services such as virtual private networks (VPN) and The Onion Router (Tor). To address the need to identify and classify this traffic, machine and deep learning solutions have become the standard. However, high-performing classifiers often scale poorly when applied to real-world traffic classification due to the heavily skewed nature of network traffic data. Prior research has found synthetic data generation to be effective at alleviating concerns surrounding class imbalance, though a limited number of these techniques have been applied to the domain of anonymous network traffic detection. This work compares the ability of a Conditional Tabular Generative Adversarial Network (CTGAN), Copula Generative Adversarial Network (CopulaGAN), Variational Autoencoder (VAE), and Synthetic Minority Over-sampling Technique (SMOTE) to create viable synthetic anonymous network traffic samples. Moreover, we evaluate the performance of several shallow boosting and bagging classifiers as well as deep learning models on the synthetic data. Ultimately, we amalgamate the data generated by the GANs, VAE, and SMOTE into a comprehensive dataset dubbed CMU-SynTraffic-2022 for future research on this topic. Our findings show that SMOTE consistently outperformed the other upsampling techniques, improving classifiers’ F1-scores over the control by ~7.5% for application type characterization. Among the tested classifiers, Light Gradient Boosting Machine achieved the highest F1-score of 90.3% on eight application types.
M2FNet: Multi-granularity Feature Fusion Network for Medical Visual Question Answering

Medical Vision Question Answer (VQA) is a combination of medical artificial intelligence and visual question answering, which is a complex multimodal task. The purpose is to obtain accurate answers based on images and questions to assist patients in understanding their personal situations as well as to provide doctors with decision-making options. Although CV and NLP have driven great progress in medical VQA, challenges still exist in medical VQA due to the characteristics of the medical domain. First, the use of a meta-learning model for image feature extraction can accelerate the convergence of medical VQA models, but it will contain different degrees of noise, which will degrade the effectiveness of feature fusion in medical VQA, thereby affecting the accuracy of the model. Second, the currently existing medical VQA methods only mine the relation between medical images and questions from a single granularity or focus on the relation within the question, which leads to an inability to comprehensively understand the relation between medical images and questions. Thus, we propose a novel multi-granularity medical VQA model. On the one hand, we apply multiple meta-learning models and a convolutional denoising autoencoder for image feature extraction, and then optimize it using an attention mechanism. On the other hand, we propose to represent the question features at three granularities of words, phrases, and sentences, while a keyword filtering module is proposed to obtain keywords from word granularity, and then the stacked attention module with different granularities is used to fuse the question features with the image features to mine the relation from multiple granularities. Experimental results on the VQA-RAD dataset demonstrate that the proposed method outperforms the currently existing meta-learning medical VQA methods, with an overall accuracy improvement of 1.8% compared to MMQ, and it has more advantages for long questions.
Exploring the Open World Using Incremental Extreme Value Machines

Dynamic environments require adaptive applications. One particular machine learning problem in dynamic environments is open world recognition. It characterizes a continuously changing domain where only some classes are seen in one batch of the training data and such batches can only be learned incrementally. Open world recognition is a demanding task that is, to the best of our knowledge, addressed by only a few methods. This work introduces a modification of the widely known Extreme Value Machine (EVM) to enable open world recognition. Our proposed method extends the EVM with a partial model fitting function by neglecting unaffected space during an update. This reduces the training time by a factor of 28. In addition, we provide a modified model reduction using weighted maximum K-set cover to strictly bound the model complexity and reduce the computational effort by a factor of 3.5 from 2.1 s to 0.6 s. In our experiments, we rigorously evaluate openness with two novel evaluation protocols. The proposed method achieves superior accuracy of about 12 % and computational efficiency in the tasks of image classification and face recognition.
Modeling Cyberattack Propagation and Impacts on Cyber-Physical System Safety: An Experiment

In this article, we present an experiment we conducted with discrete event simulations to analyze the effects of multi-step cyberattacks on the safety of cyber-physical systems. We show how to represent systems, their components (either software and/or hardware), communication links, security measures, and attacks from a malicious intruder. The latter are typically taken from the MITRE ATT&amp;CK knowledge base. The discrete event simulation method makes it possible to represent any event affecting the system. We illustrate our approach by means of an illustrative example involving cyberattacks against the navigation system of an autonomous ship. We show how the formal modeling language AltaRica, primarily dedicated to safety analyses, can assess this illustrative example by representing the system and automatically extracting sequences of attacks, leading to a safety-critical situation, namely the deviation of the ship by the attacker. This article aims to discuss this approach and to outline the lessons learned from our experience.
Forecasting the prospects for innovative changes in the development of future linguistic education for the XXI century: the choice of optimal strategies

The paper analyzes orientations and research tools in the field of linguistics, namely the development of linguistic education, general themes and strategies, specific scientific orientations, as well as some aspects of the institutional structure where it develops. The perspective of the topics analyzes the general strategies that have already been defined by scientific thought or are still in their developmental stage and proposes their own strategies for foreign language learning. The transition to an information society has caused a paradigm shift in the gravity and role of language knowledge in society. With the regard to educational knowledge, the paradigm change can be summarized as follows: there has been a transition from the educational model characteristic of modern pedagogy to the educational model of postmodern pedagogy, which involves mainly quality learning, through a comprehensive approach, integrating the learning content at the project level of a certain learning type to achieve the ideal of learning and universal development of a holistic person. This paradigm shift is associated with the affirmation of the triad of the learning educational model: lifelong learning, self-education, and full use of educational opportunities. The paper emphasizes the importance of foreign language learning strategies in the aspect of foreign language learning and teaching. One summarizes that the context of strategies and concepts of foreign language learning describe the taxonomy of innovative strategies, consider the role of the teacher in learning and development of future linguistic education.
Blockchain for Patient Safety: Use Cases, Opportunities and Open Challenges

Medical errors are recognized as major threats to patient safety worldwide. Lack of streamlined communication and an inability to share and exchange data are among the contributory factors affecting patient safety. To address these challenges, blockchain can be utilized to ensure a secure, transparent and decentralized data exchange among stakeholders. In this study, we discuss six use cases that can benefit from blockchain to gain operational effectiveness and efficiency in the patient safety context. The role of stakeholders, system requirements, opportunities and challenges are discussed in each use case in detail. Connecting stakeholders and data in complex healthcare systems, blockchain has the potential to provide an accountable and collaborative milieu for the delivery of safe care. By reviewing the potential of blockchain in six use cases, we suggest that blockchain provides several benefits, such as an immutable and transparent structure and decentralized architecture, which may help transform health care and enhance patient safety. While blockchain offers remarkable opportunities, it also presents open challenges in the form of trust, privacy, scalability and governance. Future research may benefit from including additional use cases and developing smart contracts to present a more comprehensive view on potential contributions and challenges to explore the feasibility of blockchain-based solutions in the patient safety context.
Psychological predictors of consumer-level virtual reality technology adoption and usage

In recent years, virtual reality (VR) technology has been mainstreamed for at-home use, with various consumer-oriented devices released by media firms such as Meta, Google, Samsung, and HTC. The present research investigates the role of psychological traits—including immersive tendencies, absorption, sensation seeking, need for cognition, neophobia, and belief in science—as well as trait levels of individual innovativeness, self-perception of social well-being, and owner demographics, in predicting VR adoption rates and sustained use over time. Separate analyses were conducted for different classes of VR device (fixed, mobile, and standalone devices). In general, psychological factors generally emerged as more determinative of adoption than did demographics. Users’ immersive tendencies predicted earlier adoption of VR technology while absorption was associated with later adoption, with both predictive of higher overall initial usage of different types of devices. Additionally, perceiving oneself as socially successful was associated with higher initial VR usage, while a tendency to see one’s emotions as influenced by in-person rather than online contacts was negatively associated with usage. Finally, belief in science predicted greater consistency in usage over time while higher levels of absorption were associated with unstable usage patterns. These findings expand upon the limited work previously investigating the role of individual differences in adoption of VR and mark the promise of psychometrics for understanding the diffusion and continued usage of consumer-facing VR devices.
Fractional-order Diffusion based Image Denoising Model

Edge indicating operators such as gradient, mean curvature, and Gauss curvature-based image noise removal algorithms are incapable of classifying edges, ramps, and flat areas adequately. These operators are often affected by the loss of fine textures. In this paper, these problems are addressed and proposed a new coefficient of diffusion for noise removal. This new coefficient consists of two edge indicating operators, namely fractional-order difference curvature and fractional-order gradient. The fractional-order difference curvature is capable of analyzing flat surfaces, edges, ramps, and tiny textures. The fractional-order gradient can able to distinguish texture regions. The selection of the order is more flexible for the fractional order gradient and fractional-order difference curvature. This will result in effective image denoising. Since the discrete Fourier transform is simple to numerically implement, it is taken into consideration for the implementation of fractional-order gradient. The proposed method can give results that are visually appealing and improved quantitative outputs in terms of the Figure of Merit (FoM), Mean Structural Similarity (MSSIM), and Peak Signal to Noise Ratio (PSNR), according to comparative analysis.
Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview

With the emergence of deep learning, the performance of automatic speech recognition (ASR) systems has remarkably improved. Especially for resource-rich languages such as English and Chinese, commercial usage has been made feasible in a wide range of applications. However, most languages are low-resource languages, presenting three main difficulties for the development of ASR systems: (1) the scarcity of the data; (2) the uncertainty in the writing and pronunciation; (3) the individuality of each language. Uyghur, Kazakh, and Kyrgyz as examples are all low-resource languages, involving clear geographical variation in their pronunciation, and each language possesses its own unique acoustic properties and phonological rules. On the other hand, they all belong to the Altaic language family of the Altaic branch, so they share many commonalities. This paper presents an overview of speech recognition techniques developed for Uyghur, Kazakh, and Kyrgyz, with the purposes of (1) highlighting the techniques that are specifically effective for each language and generally effective for all of them and (2) discovering the important factors in promoting the speech recognition research of low-resource languages, by a comparative study of the development path of these three neighboring languages.
Teachers’ Professional Development and Intelligent Ways of Coping with It: A Systematic Review in Elementary and Middle School Education

This research addresses teacher training at different generational stages, with the aim of analysing the training actions developed by school teachers and the intentionality of linking them to their professional development, in order to offer a broad perspective of the paradigm of smart schools, allowing for the adjustment of the quality of training to real demands. To this end, a systematic review of articles published between 2012 and 2022 in the main databases (WoS, Scopus, Eric, Dialnet, and Google Scholar) was carried out. After applying the inclusion criteria, 56 articles were selected and analysed following the PRISMA 2020 statement. The findings showed the interest and importance of initial, continuous, and lifelong learning among teachers as a driver of professional development. The results also showed that research is mostly focused on novice teachers and qualitative methodologies predominate, although this is limited to certain countries and specialised publications. However, generational differences were observed. While younger teachers are more highly trained in ICT, older teachers have a higher level of competence at a processual and relational level in the classroom. In conclusion, it should be noted that teacher training linked to professional development has an impact on school improvement, especially if it is carried out from an intergenerational collaborative perspective, and the acquisition of new skills.
The Journal “Higher Education in Russia”: 30 Years of Research and Reflection

The round-table discussion dedicated to the 30th anniversary of the journal Vysshee obrazovanie v Rossii (Higher Education in Russia) took place on October 26, 2022 within the framework of XIII International conference of higher education (ICHE). Since its founding, the journal acts as a platform to discuss the urgent issues of higher education. In different periods, the researchers discussed competence-based approach, Bologna process, online education, quality of higher education, educational policy, educational management, assessment of learning outcomes, training of highly skilled researchers and teaching personnel, digitalization of education and many others. Today higher education in Russia is at a turning point of its history. We should impartially evaluate the path traveled, what has been achieved and what are the consequences. A look at the past became the subject of the round-table discussion.
Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection

Copy-move forgery is a manipulation of copying and pasting specific patches from and to an image, with potentially illegal or unethical uses. Recent advances in the forensic methods for copy-move forgery have shown increasing success in detection accuracy and robustness. However, for images with high self-similarity or strong signal corruption, the existing algorithms often exhibit inefficient processes and unreliable results. This is mainly due to the inherent semantic gap between low-level visual representation and high-level semantic concept. In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation. Our detection method expands the traditional works on two aspects: 1) we introduce the bag-of-visual-words model into this field for the first time, may meaning a new perspective of forensic study; 2) we propose a word-to-phrase feature description and matching pipeline, covering the spatial structure and visual saliency information of digital images. Extensive experimental results show the superior performance of our framework over state-of-the-art algorithms in overcoming the related problems caused by the semantic gap.
An improved autoencoder for recommendation to alleviate the vanishing gradient problem

In the recommendation domain, user rating data has high sparsity and the number of interaction information from each user is very uneven, which brings great technical challenge to designing high-quality personalized recommendation schemes. Although autoencoder-based recommendation models have achieved some success in handling sparse data, they do not deal well with the uneven distribution of the amount of interaction information among different users, which further prevents the improvement of recommendation performance. In this paper, we find that the vanishing gradient problem occurs in the process of training model with the data from active users and theoretically analyze its root cause. Then, an improved autoencoder is proposed to alleviate this problem in recommendation domain. Furthermore, based on the improved autoencoder, two recommendation schemes are presented for rating prediction task and top-N ranking task, respectively. Finally, the numerical experimental results demonstrate that the proposed recommendation schemes can achieve about 5% and 3% improvements in the rating prediction and top-N ranking, respectively. Therefore, the improved model can well cope with the challenges brought by data sparsity and uneven distribution of rating data and achieve good recommendation performance.
Cloud-Cluster: An uncertainty clustering algorithm based on cloud model

As a cornerstone of the world, uncertainty embodies the nature of data and knowledge. Existing uncertainty theory-based clustering algorithms learn fuzziness, i.e., the uncertainty of clustering objects belonging to different clusters. However, these algorithms do not refer to the fuzziness of objects themselves, i.e., the randomness of data. Here, we propose a clustering algorithm named Cloud-Cluster, which simultaneously characterizes the fuzziness and randomness of objects to reserve uncertain information, and to describe clusters into concepts. It embeds random uncertainty of concepts to extend the data distribution range for better data partitions and gradually constructs accurate concepts by an improved backward cloud transformation algorithm (MBCT-SR-Ex). Moreover, to ensure that the concept clustering process gradually converges, Cloud-Cluster introduces the Cluster Concept Drift Degree to evaluate the uncertainty of concepts during the clustering process. Experiments on UCI and OpenML clustering datasets show that Cloud-Cluster improves the average clustering accuracy by over 14% compared to K-Means and uncertainty theory-based clustering algorithms. Extensive experimental results on the evaluation of uncertainty show that Cloud-Cluster can handle the uncertainty of datasets in the clustering process well, in addition to exhibiting robustness with unclear clusters.