Reviewerly/exHarmony huggingface dataset:
Hierarchical Belief Propagation on Image Segmentation Pyramid

The Markov random field (MRF) for stereo matching can be solved using belief propagation (BP). However, the solution space grows significantly with the introduction of high-resolution stereo images and 3D plane labels, making the traditional BP algorithms impractical in inference time and convergence. We present an accurate and efficient hierarchical BP framework using the representation of the image segmentation pyramid (ISP). The pixel-level MRF can be solved by a top-down inference on the ISP. We design a hierarchy of MRF networks using the graph of superpixels at each ISP level. From the highest/image to the lowest/pixel level, the MRF models can be efficiently inferred with constant global guidance using the optimal labels of the previous level. The large texture-less regions can be handled effectively by the MRF model on a high level. The advanced 3D continuous labels and a novel support-points regularization are integrated into our framework for stereo matching. We provide a data-level parallelism implementation which is orders of magnitude faster than the best graph cuts (GC) algorithm. The proposed framework, HBP-ISP, outperforms the best GC algorithm on the Middlebury stereo matching benchmark.
Solving Modal Logic Problems by Translation to Higher-Order Logic

This paper describes an evaluation of Automated Theorem Proving (ATP) systems on problems taken from the QMLTP library of first-order modal logic problems. Principally, the problems are translated to higher-order logic in the TPTP language using an embedding approach, and solved using higher-order logic ATP systems. Additionally, the results from native modal logic ATP systems are considered, and compared with those from the embedding approach. The findings are that the embedding process is reliable and successful, the choice of backend ATP system can significantly impact the performance of the embedding approach, native modal logic ATP systems outperform the embedding approach, and the embedding approach can cope with a wider range modal logics than the native modal systems considered.
VTAE: Variational Transformer Autoencoder With Manifolds Learning

Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables and these models use a non-linear function (generator) to map latent samples into the data space. On the other hand, the non-linearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning. This weak projection, however, can be addressed by a Riemannian metric, and we show that geodesics computation and accurate interpolations between data samples on the Riemannian manifold can substantially improve the performance of deep generative models. In this paper, a Variational spatial-Transformer AutoEncoder (VTAE) is proposed to minimize geodesics on a Riemannian manifold and improve representation learning. In particular, we carefully design the variational autoencoder with an encoded spatial-Transformer to explicitly expand the latent variable model to data on a Riemannian manifold, and obtain global context modelling. Moreover, to have smooth and plausible interpolations while traversing between two different objects' latent representations, we propose a geodesic interpolation network different from the existing models that use linear interpolation with inferior performance. Experiments on benchmarks show that our proposed model can improve predictive accuracy and versatility over a range of computer vision tasks, including image interpolations, and reconstructions.
Attention fusion and target-uncertain detection for discriminative tracking

Most existing prevalent trackers draw support from deep learning networks to acquire more effective target features, while we found that the dominant discriminative trackers employ a plain convolution block to turn backbone features into target classification features suitable for tracking. In this paper, we propose a lightweight feature separation and fusion module to obtain more effective and efficient semantic features in an end-to-end manner. Moreover, discriminative trackers entail collecting a certain number of new samples to online optimize the target classifier for retaining its discriminative ability, but it is not easy to pick out a more reliable sample for storage. Therefore, a feasible target-uncertain detection technique is devised to alleviate the tracking model corruption problem. In order to demonstrate the strong effectiveness and compatibility of our proposed approaches, we choose the excellent SuperDiMP and ToMP as baseline methods and conduct comprehensive experimental evaluations on seven public benchmarks. The results reveal that our methods perform superiorly against several state-of-the-art trackers on challenging benchmarks. The code and trained models will be available at https://github.com/hexdjx/VisTrack.
Modular models for systems based on multi-tenant services: A multi-level petri-net-based approach

Recently, the emergence of multi-tenant services-based systems (SBS) in cloud has gained significant attention. These systems, commonly adopted by Cloud software vendors, offer the advantage of building software as a service (SaaS) in a multi-tenant environment with distinct variability plans. However, the task of managing the SBS is challenging due to the interdependence among its diverse components. The difficulty primarily stems from the specific performance requirements of different variability plans and the interdependence between these plans. The main concerns involve effectively representing the components and levels of this system, and establishing synchronization rules to ensure its seamless operation. Refining these rules is crucial for improving coordination among the various components and levels of the SBS. However, it becomes particularly challenging when there are correlation issue among services that fall under different plan characteristics. In this context, it is vital to guarantee performance differentiation for each tenant by adhering to predefined quality of service (QoS) specified in Service Level Agreements (SLAs).To address these challenges, we propose a modular modeling approach based on multi-level Petri-Nets (n-NLS). By adopting these approach, the ambiguity of SBS system can be significantly reduced. An example of a SBS application illustrates our modular modeling approach using n-NLS.
Comprehensive review on vehicle Detection, classification and counting on highways

Vehicle detection, counting and finally classification has been an important aspect of traffic analysis specially on highways in many developed and developing nations. This has vitalized the monitoring of freeways and reduced the reliance on human traffic monitors specially in developed nations. However, much research carried out in this regard since 1995 have been slow to progress until around 2000. Since then, much more encouraging outcomes have been achieved. This has been mainly due to the advances on vision-based computing and the miniaturizing of hardware since the early 2000. Initial vision-based systems used basic computer vision approaches such as background subtraction and edge detection to detect and count vehicles. Early progress of classification had little success until around 2010. Since 2010, many computer vision approaches using Neural Networks have been gradually increasing the efficiency and the realtime operation of such systems. Since then, many deep learning based neural network approaches have produced remarkable outcomes specially in classification. We have also reported highly accurate and efficient deep learning based YOLOv5 system that has recorded an astounding success. We have used models YOLOv5l, YOLOv5m, YOLOv5n and YOLOv5s in our research to ascertain the best model for the task. However, low light conditions associated with overcast, and dusk have significantly reduced the accuracy in many deep learning-based systems. There are other techniques based on approaches such as headlight detection have appeared to increase the accuracy of counting however, classification at night without adequate lighting still pauses a formidable challenge. The work presented here analyses the vehicle counting problem using computer vision providing many different viewpoints in a view to provide the reader with accurate problem definition.
Performance Assessment and Mitigation of Timing Covert Channels over the IEEE 802.15.4

The fast development and adoption of IoT technologies has been enabling their application into increasingly sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices increases annually, they still present severe cyber-security vulnerabilities, becoming potential targets and entry points for further attacks. As these nodes become compromised, attackers aim to set up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes in a cloaked fashion, and network timing covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim to analyse the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol, particularly upon the DSME behaviour, one of the most promising for large-scale time critical communications. This enables us to better understand the involved risk of such threats and help support the development of active cyber-security mechanisms to mitigate these threats, which, for now, we provide in the form of practical network setup recommendations.
Explainable Image Classification: The Journey So Far and the Road Ahead

Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address the interpretability challenges posed by complex machine learning models. In this survey paper, we provide a comprehensive analysis of existing approaches in the field of XAI, focusing on the tradeoff between model accuracy and interpretability. Motivated by the need to address this tradeoff, we conduct an extensive review of the literature, presenting a multi-view taxonomy that offers a new perspective on XAI methodologies. We analyze various sub-categories of XAI methods, considering their strengths, weaknesses, and practical challenges. Moreover, we explore causal relationships in model explanations and discuss approaches dedicated to explaining cross-domain classifiers. The latter is particularly important in scenarios where training and test data are sampled from different distributions. Drawing insights from our analysis, we propose future research directions, including exploring explainable allied learning paradigms, developing evaluation metrics for both traditionally trained and allied learning-based classifiers, and applying neural architectural search techniques to minimize the accuracy–interpretability tradeoff. This survey paper provides a comprehensive overview of the state-of-the-art in XAI, serving as a valuable resource for researchers and practitioners interested in understanding and advancing the field.
Open problems in causal structure learning: A case study of COVID-19 in the UK

Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation provided by these algorithms enables transparency and explainability, which is necessary for decision making in critical real-world problems. Yet, causal ML has had limited impact in practice compared to associational ML. This paper investigates the challenges of causal ML with application to COVID-19 UK pandemic data. We collate data from various public sources and investigate what the various structure learning algorithms learn from these data. We explore the impact of different data formats on algorithms spanning different classes of learning, and assess the results produced by each algorithm, and groups of algorithms, in terms of graphical structure, model dimensionality, sensitivity analysis, confounding variables, predictive and interventional inference. We use these results to highlight open problems in causal structure learning and directions for future research. To facilitate future work, we make all graphs, models, data sets, and source code publicly available online.
Research Culture of Russian University Students

The article is devoted to the summarizing results of the sociological study of the re- search culture of students (February-April 2023, n=3,032 people). The aim of the article was to iden- tify its features and contradictions in the conditions of modern Russian universities. The productivity of the theoretical and methodological framework for studying this problem, developed on the basis of L.N. Kogan’s approach, is argued. The article proposes to consider the research culture as a way of developing the essential forces of students and to identify its structural elements such as cognitive, axiological and praxeological. In accordance with the theoretical and methodological approach, the characteristic of the research culture of students is given. The results of the survey allowed us to conclude that the majority of students have an interest in science and scientific knowledge as values, and at the same time – about the formation of a critical assessment of the risks that research produces among students who are active in scientific terms. The problem areas of students’ research competencies that prevent the “seamless” integration of student and “adult” science, including meth- odological and organizational ones, have been identified. Such problematic features of the praxe- ological block of students’ research culture as their low involvement in publication practices and tol- erance to unethical research practices are shown. The significant influence of teachers and scientific mentors performing activating and socializing functions on the formation of the research culture of students is substantiated. The practical significance of the research lies in a comprehensive under- standing of the state of the research culture of students of Russian universities, identifying factors that influence (positively or negatively) its formation and development. On this basis, it is possible to develop scientifically based programs for the academic development of students, especially in terms of its scientific track.
A VR Client Simulation to Prepare MSW Social Work Students for Practicum: A Feasibility Study

In this feasibility study of virtual reality (VR)-based learning, we surveyed Master of Social Work students (n = 54) to examine student perceptions of a VR client simulation (VRCS) as a preparatory tool to work with real clients. The pre-post survey examined changes in students' perceived anxiety, stress, excitement, competency, confidence and frustration to communicate and interact with a client. We also measured changes in students' perceived interest toward working with older adults (70+) years. Wilcoxon signed-rank tests showed statistically significant decreases in participants perceived anxiety, stress, and frustration, and statistically significant increases in participants perceived competency, confidence, and excitement to interact with a future live client as part of their practicum placement requirements. There was a statistically significant increase in participants' perceptions that practicing with a VRCS will transfer effectively to interacting with a real live client in the future. Results also showed statistically significant increases in participant's perceived interest to work with older adults. Outcomes suggest a VRCS may complement other learning activities in a curricular program especially at the start as an experiential strategy to prepare students for practicum.
Novelty in Handwriting Recognition

In the domain of Handwriting Recognition (HWR), many novelties may be encountered and can negatively affect the performance of automatic transcription. This is often the case with historical documents. In the context of this unified framework of novelty, HWR is a task consisting of multiple subtasks. The primary task in HWR is transcription, where novel glyphs, characters, words, and phrases may be encountered during the transcription process. Another key task of HWR is style recognition, including writer identification and overall document image appearance. The transcription task involves an agent taking a digital image of a handwritten document as input and processing it to recognize the individual characters to produce a plaintext output. The style recognition task involves the agent identifying known and unknown aspects of visual appearance for both the text, e.g., how are individual characters stylized?, and page i.e., what does the page look like holistically? Two subtasks for style recognition are considered in this paper: (1a) writer identification and (1b) Overall Document Appearance Identification (ODAI). The former involves multi-class classification to distinguish between individual known writers and new writers unseen at training time, while the latter involves multi-class classification to distinguish between known global appearances of handwritten documents and appearances unseen at training time.
General Multiscenario Ultrasound Image Tumor Diagnosis Method Based on Unsupervised Domain Adaptation

The utilization of computer-aided diagnosis (CAD) in breast ultrasound image classification has been limited by small sample sizes and domain shift. Current ultrasound classification methods perform inadequately when exposed to cross-domain scenarios, as they struggle with data sets from unobserved domains. In the medical field, there are situations in which all images must share the same networks as they capture the same symptom of the same participant, implying that they share identical structural content. Nevertheless, most domain adaptation methods are not suitable for medical images as they overlook the common features among the images.To overcome these challenges, we propose a novel diverse-domain 2-D feature selection network (FSN), which uses the similarities among medical images and extracts features with a reconstruction network with shared weights. Additionally, it penalizes the feature domain distance through two adversarial learning modules that align the feature space and select common features. Our experiments illustrate that the proposed method is robust and can be applied to ultrasound images of various diseases.Compared with the latest domain adaptive methods, 2-D FSN markedly enhances the accuracy of classification of breast, thyroid and endoscopic ultrasound images, achieving accuracies of 82.4%, 96.4% and 89.7%, respectively. Furthermore, the model was evaluated on an unsupervised domain adaptation task using ultrasound images from multiple sources and achieved an average accuracy of 77.3% across widely varying domains.In general, 2-D FSN improves the classification ability of the model on multidomain ultrasound data sets through the learning of common features and the combination of multimodule intelligence. The algorithm has good clinical guidance value.
Does China’s Flow of FDI and Institutional Quality Matter for Poverty? Evidence from Sub-Sahara Africa

The study adds to the discussion on the necessity for Sub-Sahara African (SSA) countries to eradicate poverty, as outlined in SDG 1 and Africa’s Agenda 2063. This contribution was successful in achieving some key objectives. First, we examine the impact of Chinese FDI and institutional quality on the eradication of poverty in SSA. Second, we test empirically whether institutional quality can help Chinese FDI to reduce poverty in SSA. Using an annual dataset for 36 SSA countries for a 20-year period ending in 2020, our pooled OLS results showed that Chinese FDI does not reduce poverty unless there are strong institutions and good governance. Also, the results showed that strong institutions and good governance reduce poverty in SSA. As a result, we recommend that governments establish policies to develop systems and structures that encourage industrialization and attract foreign investors for SSA to reap the full benefits of Chinese FDI.
A large-scale television advertising dataset for detailed impression analysis

Abstract Creating impressive video content such as movies and advertisements is a very important yet challenging task in business that requires both a sense of creativity and a lot of experience. Even professionals cannot necessarily invoke the impressions and emotions that they have aimed at. Many video advertisements are created and then disappear without giving a large impact on viewers. This paper presents a large-scale dataset of television (TV) advertisements that consists of 14,490 videos. The impressions of each video such as the recognition rate and interestingness rate are from the results of questionnaires answered by 620 participants. We also present a baseline for predicting the impression effects of TV advertisements by using visual and audio information, metadata such as broadcasting pattern, business category, the popularity of the casts, and text information including texts appearing on videos and narrations in audios. We predict four impressions of the viewers: 1) how much participants remember the video afterward, 2) how much they feel like buying the product/service, 3) how much they become interested in the product/service, and 4) how much they like the content of the advertisement itself. By combining images, audio, metadata, cast data, and text data, our baseline method is able to predict such impressions with a correlation of 0.69-0.82, much better than using a single-modal feature such as visual data or audio data only. This paper also gives some possible applications such as estimating the importance scores of each key frame, which gives us informative insights about how to make the advertisement content more impressive.