Reviewerly/exHarmony huggingface dataset:
Measure of Similarity between GMMs Based on Geometry-Aware Dimensionality Reduction

Gaussian Mixture Models (GMMs) are used in many traditional expert systems and modern artificial intelligence tasks such as automatic speech recognition, image recognition and retrieval, pattern recognition, speaker recognition and verification, financial forecasting applications and others, as simple statistical representations of underlying data. Those representations typically require many high-dimensional GMM components that consume large computing resources and increase computation time. On the other hand, real-time applications require computationally efficient algorithms and for that reason, various GMM similarity measures and dimensionality reduction techniques have been examined to reduce the computational complexity. In this paper, a novel GMM similarity measure is proposed. The measure is based on a recently presented nonlinear geometry-aware dimensionality reduction algorithm for the manifold of Symmetric Positive Definite (SPD) matrices. The algorithm is applied over SPD representations of the original data. The local neighborhood information from the original high-dimensional parameter space is preserved by preserving distance to the local mean. Instead of dealing with high-dimensional parameter space, the method operates on much lower-dimensional space of transformed parameters. Resolving the distance between such representations is reduced to calculating the distance among lower-dimensional matrices. The method was tested within a texture recognition task where superior state-of-the-art performance in terms of the trade-off between recognition accuracy and computational complexity has been achieved in comparison with all baseline GMM similarity measures.
Hybrid Machine Learning Classification and Inference of Stalling Events in Mobile Videos

Mobile video streaming accounts for a considerable percentage of network traffic. However, the fluctuations in the available bandwidth cause video stalling events which negatively affect the user’s quality of experience. The recognition of stalling events is of great importance for solving this issue. Predicting stalling events helps in predicting the user’s experience as well as aids in finding solutions to mitigate the existing issues. Despite the existence of some research that attempts to predict video stalling, no one framework proposes a hybrid approach that predicts this issue at several levels. In this paper, we propose a novel framework to classify video stalling into three levels while considering several supervised learning methods. First, logistic regression is used to detect the existence of a stalling event using a binary approach. Then, a supervised Random Forest model is trained to classify using multi-class classification the level of stalling events existing in a specific video. Moreover, beyond the acquisition of discrete video stalling level, a novel continuous quantitative method is proposed based on artificial neural networks to predict the number of video stalling events. For our experiments, we rely on a recently published dataset that collects 1,081 hours of time-synchronous video measurements at the network, transport, and application layer. Finally, the classification and regression results demonstrate the feasibility and accuracy of up to 92% of the proposed hybrid learning methods.
A quantum algorithm for syndrome decoding of classical error-correcting linear block codes

Decoding of linear block codes using syndrome decoding and finding whether there exists a codeword of weight <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w$</tex> in a set of codewords are known to belong to a class of NP-complete problems. For syndrome decoding of linear block codes, we provide an algorithm that gives a probabilistic solution with least Hamming weight. The algorithm can speed up this process significantly with the help of quantum search algorithm. For this, we make use of a search algorithm inspired from Grover's search. We also suggest a general approach for building a quantum circuit for syndrome detection. We test the algorithm in IBM Quantum Experience using qasm simulator and perform the complexity analysis identifying the algorithm's domain of utility.
An improved approach to the development of software with increased requirements for flexibility and reliability in terms of creating small and medium-sized projects

The use of new and improved software development approaches is becoming a necessity due to high quality requirements and increasing development complexity. This paper analyzes the most common approaches to the development of flexible and reliable software as well as determines their weaknesses and strengths. Based on the analysis, possible improvements to the existing approaches were proposed and the idea of an improved approach to software development with increased requirements for flexibility and reliability was formed. This approach, unlike the existing ones, provides comprehensive recommendations to software developers regarding the use of the most common practices in the conditions of creating small and medium-sized projects.
Robust Data Augmentation for Neural Machine Translation through EVALNET

Since building Neural Machine Translation (NMT) systems requires a large parallel corpus, various data augmentation techniques have been adopted, especially for low-resource languages. In order to achieve the best performance through data augmentation, the NMT systems should be able to evaluate the quality of augmented data. Several studies have addressed data weighting techniques to assess data quality. The basic idea of data weighting adopted in previous studies is the loss value that a system calculates when learning from training data. The weight derived from the loss value of the data, through simple heuristic rules or neural models, can adjust the loss used in the next step of the learning process. In this study, we propose EvalNet, a data evaluation network, to assess parallel data of NMT. EvalNet exploits a loss value, a cross-attention map, and a semantic similarity between parallel data as its features. The cross-attention map is an encoded representation of cross-attention layers of Transformer, which is a base architecture of an NMT system. The semantic similarity is a cosine distance between two semantic embeddings of a source sentence and a target sentence. Owing to the parallelism of data, the combination of the cross-attention map and the semantic similarity proved to be effective features for data quality evaluation, besides the loss value. EvalNet is the first NMT data evaluator network that introduces the cross-attention map and the semantic similarity as its features. Through various experiments, we conclude that EvalNet is simple yet beneficial for robust training of an NMT system and outperforms the previous studies as a data evaluator.
A Cross-Platform Personalized Recommender System for Connecting E-Commerce and Social Network

In this paper, we build a recommender system for a new study area: social commerce, which combines rich information about social network users and products on an e-commerce platform. The idea behind this recommender system is that a social network contains abundant information about its users which could be exploited to create profiles of the users. For social commerce, the quality of the profiles of potential consumers determines whether the recommender system is a success or a failure. In our work, not only the user’s textual information but also the tags and the relationships between users have been considered in the process of building user profiling model. A topic model has been adopted in our system, and a feedback mechanism also been design in this paper. Then, we apply a collative filtering method and a clustering algorithm in order to obtain a high recommendation accuracy. We do an empirical analysis based on real data collected on a social network and an e-commerce platform. We find that the social network has an impact on e-commerce, so social commerce could be realized. Simulations show that our topic model has a better performance in topic finding, meaning that our profile-building model is suitable for a social commerce recommender system.
Image Deblurring Based on an Improved CNN-Transformer Combination Network

Recently, using a CNN has been a common practice to restore blurry images due to its strong ability to learn feature information from large-scale datasets. However, CNNs essentially belong to local operations and have the defect of a limited receptive field, which reduces the naturalness of deblurring results. Moreover, CNN-based deblurring methods usually adopt many downsample operations, which hinder detail recovery. Fortunately, transformers focus on modeling the global features, so they can cooperate with CNNs to enlarge the receptive field and compensate for the details lost as well. In this paper, we propose an improved CNN-transformer combination network for deblurring, which adopts a coarse-to-fine architecture as the backbone. To extract the local features and global features simultaneously, the common methods are two blocks connected in parallel or cascaded. Different from these, we design a local-global feature combination block (LGFCB) with a new connecting structure to better use the extracted features. The LGFCB comprises multi-scale residual blocks (MRB) and a transformer block. In addition, we adopt a channel attention fusion block (CAFB) in the encoder path to integrate features. To improve the ability of feature representation, in the decoder path, we introduce a supervised attention block (SAB) operated on restoration images to refine features. Numerous experiments on GoPro and RealBlur datasets indicated that our model achieves remarkable accuracy and processing speed.
The role of digital consciousness in change management

The article deals with the problems of change management in the new conditions of superfragility of the BANI-world. Interpretations of the concepts of “transcendental thinking”, “digital consciousness”, “immersiveness” are given. The prerequisites substantiating the idea that transcendental thinking needs to be cultivated in the organisation in order to achieve effective management flexibility are presented. The latter is formed in the broader context of the digital consciousness organisational culture, without which it is impossible to set up the organisation’s staff for a positive perception of immersive management practices and the digital technologies productive use. Barriers to the transformation of organisational processes have been identified. It has been shown that the digital transformation of the organisation increases the resistance on the part of the staff by digital resistance plane, when aversion to any change correlates with a lack of ­understanding of digital opportunities. The relationship between the conceptual methodologies of digital consciousness and metacognition and the effectiveness of the formation of a change management strategy has been established.
Second language learning designs in online adult education

Current approaches to assessing digital competence in education may be too broad to support teachers in developing their online learning designs in specific subjects. During the pandemic, studies have identified that the development of teaching practices (and subsequently their learning designs) has taken a leap. However, because digital cultures differ between schools, local approaches need to survey how their practices have developed and what emerging practices can become good examples for others. Using a descriptive multiple case (n = 20) study methodology with observations, interviews (n = 33), and a survey, 12 elements in second language online learning designs (LDs) that seemed to engage learners and support online learning were identified. Data were analysed using pattern matching and descriptive statistics. Post observation, teachers were asked to rate the importance of the ability to include the element and how difficult they perceived its implementation would be. A case overview was used to contrast the survey. Results reveal that the number of digital technologies has little to do with the complexity of designs, and teachers with relatively few digital resources can offer more complex LDs. While most teachers rated the elements suggested as easy, the observations did not support this. However, around 30% of the teachers rated at least seven important elements as difficult or very difficult and designing learning activities that stimulate engagement in nuanced ways was considered challenging. This paper proposes that, identifying local elements may be a suitable way forward to support professional development, as well as to reframe teachers’ digital practices post Covid-19.
Scene table structure recognition with segmentation collaboration and alignment

This study addresses the challenge of scene table structure recognition (S-TSR). In contrast to the well-aligned tables in PDF documents or screenshots, natural scene tables tend to be inclined, rotated, and curved, thereby damaging the structure of the table. Consequently, many state-of-the-art methods cannot operate effectively owing to a lack of well-aligned priors. We propose a novel segmentation collaboration and alignment network (SCAN) to address the problem of table structure recognition in natural scenarios. Our SCAN combines the location and logical information of table cells through the segmentation collaboration module to segment the cell region accurately. The proposed cell alignment module aligns the segmentation result to restore the distorted table structure. The experimental results demonstrate that our method is robust to different challenging S-TSR sub-scenarios and achieves new state-of-the-art performance, with TEDS scores of 90.7 and 98.5 on the S-TSR benchmarks WTW and TAL_OCR_TABLE, respectively.
Robustness of visual perception system in progressive challenging weather scenarios

Traditional field test and laboratory test can only evaluate hardware performance, and cannot test the robustness of artificial intelligence (AI) device for object detection, instance segmentation, etc. under task scenarios. For AI-based visual perception system, this need for evaluation and testing is very urgent. In this paper, we consider the adverse effects of challenging weather on the visual perception system, investigate the robustness of visual perception system affected by adverse weather through imaging, and propose a method to evaluate the robustness of object detection model under different weather variations. To begin, we analyze the components that influence the visual perception system’s robustness and develop a weather generation model under rainy and foggy scenes. After that, quantifiable weather meta-datasets with visibility in fog and rain intensity as variable parameters are built. Finally, regression analysis and statistical test are used to verify the nonlinear relationship between model performance and weather variations, the robustness of state-of-the-art object detection algorithms under progressive challenging weather images is evaluated.
Self-supervised image co-saliency detection

Self-supervised learning has been shown to be highly effective to avoid the expensive cost of collecting and annotating large-scale datasets. In this paper, we explore its use in image co-saliency detection task which detects and segments the co-concurrent and conspicuous patterns in one image group, and a self-supervised framework is presented that contains three novel module designs. Specifically, we first design an unsupervised graph clustering algorithm to generate an initial detection result from extracted semantical features of convolutional networks. Then, the post-processed output is taken as the pseudo labels of the second module that learns a neural network for more accurate sample affinity estimation, followed by a manifold ranking based label smoothing. Finally, with the smoothed labels as supervision, we design a saliency network that aggregates a hierarchy of multi-scale features to predict final optimized co-saliency maps. In addition, the proposed framework can be readily extended to image saliency object detection task when giving a single image as input. Extensive evaluations on three co-saliency and five saliency object detection benchmark datasets demonstrate that the proposed framework achieves favorable performance against a variety of supervised and unsupervised state-of-the-arts.
Space Compression Algorithms Acceleration on Embedded Multi-core and GPU Platforms

Future space missions will require increased on-board computing power to process and compress massive amounts of data. Consequently, embedded multi-core and GPU platforms are considered, which have been shown beneficial for data processing. However, the acceleration of data compression - an inherently sequential task - has not been explored. In this on-going research paper, we parallelize two space compression standards on both CPUs and GPUs using two candidate embedded GPU platforms for space showing that despite the challenging nature of CCSDS algorithms, their parallelization is possible and can provide significant performance benefits.
Deep Incremental Hashing for Semantic Image Retrieval With Concept Drift

Hashing methods are widely used for content-based image retrieval due to their attractive time and space efficiencies. Several dynamic hashing methods have been proposed for image retrieval tasks in non-stationary environments. However, concept drift problems in non-stationary environment are seldomly considered which lead to significant deterioration of performance. Therefore, we propose Deep Incremental Hashing (DIH). For the learning part, similarity-preserving object codes of each newly arriving data chunk are computed using the product of its label matrix and a random Gaussian matrix generated offline. A point-wise loss function is then devised to guide the learning of a deep hash neural network. To retain the learned knowledge of former chunks, a weighting-based method is utilized to combine different hash tables trained at different time steps to form a multi-table hashing system. Experimental results on 13 simulated concept drift environments show that DIH adapts to non-stationary data environments well and yields better retrieval performance than existing dynamic hashing methods.
Using Extended Reality to Study the Experience of Presence

Extended reality (XR), encompassing various forms of virtual reality (VR) and augmented reality (AR), has become a powerful experimental tool in consciousness research due to its capability to create holistic and immersive experiences of oneself and surrounding environments through simulation. One hallmark of a successful XR experience is when it elicits a strong sense of presence, which can be thought of as a subjective sense of reality of the self and the world. Although XR research has shed light on many factors that may influence presence (or its absence) in XR environments, there remains much to be discovered about the detailed and diverse phenomenology of presence, and the neurocognitive mechanisms that underlie it. In this chapter, we analyse the concept of presence and relate it to the way in which humans may generate and maintain a stable sense of reality during both natural perception and virtual experiences. We start by reviewing the concept of presence as developed in XR research, covering both factors that may influence presence and potential ways of measuring presence. We then discuss the phenomenological characteristics of presence in human consciousness, drawing on clinical examples where presence is disturbed. Next, we describe two experiments using XR that investigated the effects of sensorimotor contingency and affordances on a specific form of presence related to the sense of objects as really existing in the world, referred to as 'objecthood'. We then go beyond perceptual presence to discuss the concept of 'conviction about reality', which corresponds to people's beliefs about the reality status of their perceptual experiences. We finish by exploring how the novel XR method of 'Substitutional Reality' can allow experimental investigation of these topics, opening new experimental directions for studying presence beyond the 'as-if' experience of fully simulated environments.
Biases in human perception of facial age are present and more exaggerated in current AI technology

Abstract Our estimates of a person’s age from their facial appearance suffer from several well-known biases and inaccuracies. Typically, for example, we tend to overestimate the age of smiling faces compared to those with a neutral expression, and the accuracy of our estimates decreases for older faces. The growing interest in age estimation using artificial intelligence (AI) technology raises the question of how AI compares to human performance and whether it suffers from the same biases. Here, we compared human performance with the performance of a large sample of the most prominent AI technology available today. The results showed that AI is even less accurate and more biased than human observers when judging a person’s age—even though the overall pattern of errors and biases is similar. Thus, AI overestimated the age of smiling faces even more than human observers did. In addition, AI showed a sharper decrease in accuracy for faces of older adults compared to faces of younger age groups, for smiling compared to neutral faces, and for female compared to male faces. These results suggest that our estimates of age from faces are largely driven by particular visual cues, rather than high-level preconceptions. Moreover, the pattern of errors and biases we observed could provide some insights for the design of more effective AI technology for age estimation from faces.
Dynamic Unary Convolution in Transformers

It is uncertain whether the power of transformer architectures can complement existing convolutional neural networks. A few recent attempts have combined convolution with transformer design through a range of structures in series, where the main contribution of this paper is to explore a parallel design approach. While previous transformed-based approaches need to segment the image into patch-wise tokens, we observe that the multi-head self-attention conducted on convolutional features is mainly sensitive to global correlations and that the performance degrades when these correlations are not exhibited. We propose two parallel modules along with multi-head self-attention to enhance the transformer. For local information, a dynamic local enhancement module leverages convolution to dynamically and explicitly enhance positive local patches and suppress the response to less informative ones. For mid-level structure, a novel unary co-occurrence excitation module utilizes convolution to actively search the local co-occurrence between patches. The parallel-designed Dynamic Unary Convolution in Transformer (DUCT) blocks are aggregated into a deep architecture, which is comprehensively evaluated across essential computer vision tasks in image-based classification, segmentation, retrieval and density estimation. Both qualitative and quantitative results show our parallel convolutional-transformer approach with dynamic and unary convolution outperforms existing series-designed structures.