Reviewerly/exHarmony huggingface dataset:
A Probabilistic Formulation for Meta-Weight-Net

In the last decade, deep neural networks (DNNs) have become dominant tools for various of supervised learning tasks, especially classification. However, it is demonstrated that they can easily overfit to training set biases, such as label noise and class imbalance. Example reweighting algorithms are simple and effective solutions against this issue, but most of them require manually specifying the weighting functions as well as additional hyperparameters. Recently, a meta-learning-based method Meta-Weight-Net (MW-Net) has been proposed to automatically learn the weighting function parameterized by an MLP via additional unbiased metadata, which significantly improves the robustness of prior arts. The method, however, is proposed in a deterministic manner, and short of intrinsic statistical support. In this work, we propose a probabilistic formulation for MW-Net, probabilistic MW-Net (PMW-Net) in short, which treats the weighting function in a probabilistic way, and can include the original MW-Net as a special case. By this probabilistic formulation, additional randomness is introduced while the flexibility of the weighting function can be further controlled during learning. Our experimental results on both synthetic and real datasets show that the proposed method improves the performance of the original MW-Net. Besides, the proposed PMW-Net can also be further extended to fully Bayesian models, to improve their robustness.
Analysis of Text Identification Techniques Using Scene Text and Optical Character Recognition

In today's era, data in digitalized form is needed for faster processing and performing of all tasks. The best way to digitalize the documents is by extracting the text from them. This work of text extraction can be performed by various text identification tasks such as scene text recognition, optical character recognition, handwriting recognition, and much more. This paper presents, reviews, and analyses recent research expansion in the area of optical character recognition and scene text recognition based on various existing models such as convolutional neural network, long short-term memory, cognitive reading for image processing, maximally stable extreme regions, stroke width transformation, and achieved remarkable results up to 90.34% of F-score with benchmark datasets such as ICDAR 2013, ICDAR 2019, IIIT5k. The researchers have done outstanding work in the text recognition field. Yet, improvement in text detection in low-quality image performance is required, as text identification should not be limited to the input quality of the image.
Adaptive Directional Cubic Convolution for Integrated Circuit Chip Defect Image Interpolation

An adaptive directional cubic convolution interpolation method for integrated circuit (IC) chip defect images is proposed in this paper, to meet the challenge of preserving edge and texture information. In the proposed method, Otsu thresholding technique is employed to distinguish strong edge pixels from weak ones and texture regions, and estimate the direction of strong edges, adaptively. Boundary pixels are pre-interpolated using the original bicubic interpolation method to help improve the interpolation accuracy of the interior pixels. The experimental results of both classic test images and IC chip defect images demonstrate that the proposed method outperforms the competing methods with better edge and texture preservation, interpolation quality, more natural visual effect of the interpolated images and reasonable computational time. The proposed method can provide high quality IC chip images for defect detection and has been successfully applied on practical vision inspection for IC chips
Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming

Abstract Genetic programming (GP) automatically designs programs. Evolutionary programming (EP) is a real-valued global optimisation method. EP uses a probability distribution as a mutation operator, such as Gaussian, Cauchy, or Lévy distribution. This study proposes a hyper-heuristic approach that employs GP to automatically design different mutation operators for EP. At each generation, the EP algorithm can adaptively explore the search space according to historical information. The experimental results demonstrate that the EP with adaptive mutation operators, designed by the proposed hyper-heuristics, exhibits improved performance over other EP versions (both manually and automatically designed). Many researchers in evolutionary computation advocate adaptive search operators (which do adapt over time) over non-adaptive operators (which do not alter over time). The core motive of this study is that we can automatically design adaptive mutation operators that outperform automatically designed non-adaptive mutation operators.
Biodiversity Knowledge Graphs: Time to move up a gear!

Harnessing worldwide biodiversity data requires integrating myriad pieces of information, often sparse and incomplete, into a global, coherent data space. To do so, projects like the Global Biodiversity Information Facility, Catalog of Life and Encyclopedia of Life have set up platforms that gather, consolidate, and centralize billions of records from multiple data sources. This approach lowers the entry barrier for scientists willing to consume aggregated biodiversity data but tends to build silos that hamper cross-platform interoperability. The Web of Data embodies a different approach underpinned by the Linked Open Data (LOD) principles (Heath and Bizer 2011). These principles bring about the building of a large, distributed, cross-domain knowledge graph (KG), wherein data description relies on vocabularies with shared, formal, machine-processable semantics. So far however, little biodiversity data have been published this way. Early efforts focused primarily on taxonomic registers, such as NCBI, VTO and AGROVOC. More recent efforts have started paving the way for the publication of more diverse biodiversity KGs (Page 2019, Penev et al. 2019, Michel et al. 2017). Today, we believe that it is time for more biodiversity data producers to join in and start publishing connected KGs spanning a much broader set of domains, far beyond just taxonomic registers. In this talk, we wish to present an on-going endeavor in line with this vision. In a previous work, we published TAXREF-LD (Michel et al. 2017), a LOD representation of the French taxonomic register developed and maintained by the French National Museum of Natural History. We modeled nomenclatural information as a thesaurus of scientific names, taxonomic information as an ontology of classes denoting taxa, and additional information such as ranks and vernacular names. Recently, we have extended the scope of TAXREF-LD to represent and interlink data as various as geographic locations, species interactions, development stages, trophic levels, as well as conservation, biogeographic, and legal status (regulations, protections, etc.). We put a specific effort into working out a model that accurately accounts for the semantics of the data while respecting knowledge engineering practices. For instance, a common design shortcoming is to attach all information as properties of a taxon. This is a rightful choice for some properties like a scientific name or conservation status, but properties that actually pertain to biological individuals themselves, e.g. habitat and trophic level, should better be attched to class members. With the presentation of this work, we wish to advance the discussion about integration scenarios based on knowledge graphs with the different biodiversity data stakeholders.
Information Sieve: Content Leakage Reduction in End-to-End Prosody Transfer for Expressive Speech Synthesis

Expressive neural text-to-speech (TTS) systems incorporate a style encoder to learn a latent embedding as the style information. However, this embedding process may encode redundant textual information. This phenomenon is called content leakage. Researchers have attempted to resolve this problem by adding an ASR or other auxiliary supervision loss functions. In this study, we propose an unsupervised method called the sieve to reduce the effect of content leakage in prosody transfer. The rationale of this approach is that the style encoder can be forced to focus on style information rather than on textual information contained in the reference speech by a well-designed downsample-upsample filter, i.e., the extracted style embeddings can be downsampled at a certain interval and then upsampled by duplication. Furthermore, we used instance normalization in convolution layers to help the system learn a better latent style space. Objective metrics such as the significantly lower word error rate (WER) demonstrate the effectiveness of this model in mitigating content leakage. Listening tests indicate that the model retains its prosody transferability compared with the baseline models such as the original GST-Tacotron and ASR-guided Tacotron.
Signaling for Covert Quantum Sensing

Motivated by application to quantum radar and the known benefits of quantum illumination in the high-noise low-reflectance regime, we study the design of signaling schemes for covertly probing a distant target over a lossy and noisy bosonic channel. Specifically, we analyze the performance of diffuse and sparse signaling schemes, which achieve covertness by spreading a constant number of photons in many modes or in a few modes, respectively. We benchmark the performance against a converse bound that holds for arbitrary covert quantum illumination schemes. Numerical results suggest the superior performance of the diffuse signaling scheme, which we conjecture outperforms any other covert quantum illumination scheme.
Hierarchical Context-Aware Transformers for Non-Autoregressive Text to Speech

In this paper, we propose methods for improving the modeling performance of a Transformer-based non-autoregressive text-to-speech (TNA-TTS) model. Although the text encoder and audio decoder handle different types and lengths of data (i.e., text and audio), the TNA-TTS models are not designed considering these variations. Therefore, to improve the modeling performance of the TNA-TTS model we propose a hierarchical Transformer structure-based text encoder and audio decoder that are designed to accommodate the characteristics of each module. For the text encoder, we constrain each self-attention layer so the encoder focuses on a text sequence from the local to the global scope. Conversely, the audio decoder constrains its self-attention layers to focus in the reverse direction, i.e., from global to local scope. Additionally, we further improve the pitch modeling accuracy of the audio decoder by providing sentence and word-level pitch as conditions. Various objective and subjective evaluations verified that the proposed method outperformed the baseline TNA-TTS.
Applications of high‐resolution borehole image rapid synthesis method for the refined detection of in‐suit rock mass structural features during deep‐buried geotechnical engineering

Abstract The refined detection of in‐suit rock mass structural features always plays an important part in geomechanics and geotechnical engineering, but actual image resolution of the obtained borehole image by present borehole camera system cannot meet the requirements of real‐time and precise analysis of rock structures. Faced with hundreds of boreholes in the deep‐buried geotechnical engineering, a high‐resolution borehole image rapid synthesis method is provided for solving the actual application problems during the geological exploration. Results show the rapid synthesis method of high‐resolution borehole image by using image mosaic and data fusion techniques can improve the resolution and sharpness of the obtained borehole image with better work efficiency. The lateral resolution and longitudinal resolution of the synthetic borehole images can be up to 0.1 mm for the refined detection of in‐suit rock mass structural features in deep‐buried boreholes. The applications of this method can improve the refined detection process of in‐suit rock mass structural features with automation and intelligent, and promotes the innovation and development of borehole camera technique during the applications in the geological exploration of deep‐buried geotechnical engineering.
Prediction and Analysis of the Quality of Multimedia Professional Talents Combining Multiobjective Data Fuzzy Evolution

In response to the relatively huge error, low efficiency, and low accuracy in the prediction and analysis data on the quality of multimedia professional talents at present, an analytical method for the prediction of the quality of multimedia professional talents based on the multiobjective data fuzzy evolutionary algorithm is proposed in this paper. The weights of the talent quality data are analyzed in detail mainly based on the multiobjective data fuzzy evolution algorithm to establish a prediction and analysis model for the quality of multimedia professional talents. This model can be used to implement the prediction and analysis of the comprehensive abilities of multimedia professional talents effectively. Finally, through a practical case study, it is verified that the method put forward in this paper has high efficiency and high accuracy in predicting and analyzing the quality of multimedia professional talents.
A quantitative model of ensemble perception as summed patterns of activation in feature space

Ensemble perception allows the limited capacity visual system to efficiently consolidate noisy input from the environment to construct reliable representations, guide attention and classify features or objects (Whitney & Yamanashi, 2018). Despite the importance of ensemble perception, there are few computational models that provide a formal, process-level account of ensemble perception across a range of experimental conditions and stimuli. This is the goal of the current work; specifically, we propose a novel ensemble model, which is motivated by a recent signal detection model of memory (Schurgin, Wixted, & Brady, 2020). According to the proposed model, items evoke distributed patterns of familiarity over feature space (e.g., a color evokes a pattern of familiarity over all color channels), and ensemble representations reflect the global sum of these signals across all items -- a location-independent distribution over what features are present. We then assume that individuals report on their memory of the ensemble by selecting the feature value that generates the maximum familiarity signal after the signals are corrupted by noise. We leverage this set of minimal assumptions to capture the entire distribution of individuals’ errors on an ensemble continuous report task using solely those individuals’ estimates of performance on a separate VWM task. That is, we account for the full distribution of errors on the ensemble task with zero free parameters. The ensemble model was assessed in three experiments in which individuals were probed on their memory for ensemble color, where we varied the number of items, color range, and presence of an outlier. To assess the generalizability of our modeling results across stimuli spaces, we evaluated the model for ensemble processing of shapes. We discuss our model and results in the context of current theories of ensemble perception and population coding models of ensemble representation and memory.
DIGITAL LITERACY OF SELECTED SENIOR HIGH SCHOOL STUDENTS: AN ANALYSIS FOR ONLINE EDUCATION READINESS

The demand for Education 4.0 is believed as response to the onset of the COVID-19 pandemic. Founded on this concept, learners are expected to be digitally literate since Education 4.0 promotes novel ways of learning that focuses on collaboration of men and machines. For this reason, this quantitative-correlational study aimed to analyze the readiness of 204 senior high school students for online education by evaluating the extent of their digital literacy based on sex, strand and grade level, and the interrelationship across their digital literacy domains. It adapted a survey questionnaire from relevant studies that focuses on six digital literacy domains. Results showed that there are significant differences in the extent of digital literacy domains namely Media Analysis, Effective Application of Technology and Interaction through Technologies among the students in terms of sex. Meanwhile, there are no significant differences in the extent of digital literacy of the students in terms of strand and grade level across the six domains as shown by the p-values. When it comes to the relationship between and within the domains of digital literacy of students, results emphasized that the moderate positive correlations among all domains are significant at the 0.01 level (2-tailed). The study recommends to schools to have a focused and improved program that may help male students to cope with the demands of digital learning. Furthermore, there should be quality teaching-learning innovations in online education to promote the digital literacy of senior high school students especially the males through the development of enhancement curricula. Digital literacy across all subjects in senior high school should be integrated and enhanced, especially in the conduct of online education in order to realize the learning competencies of each subject area with provided activities and assignments inclined towards the utilization of internet and online applications.
Toward Streaming ASR with Non-Autoregressive Insertion-Based Model

Neural end-to-end (E2E) models have become a promising technique to realize practical automatic speech recognition (ASR) systems. When realizing such a system, one important issue is the segmentation of audio to deal with streaming input or long recording. After audio segmentation, the ASR model with a small real-time factor (RTF) is preferable because the latency of the system can be faster. Recently, E2E ASR based on non-autoregressive models becomes a promising approach since it can decode an $N$-length token sequence with less than $N$ iterations. We propose a system to concatenate audio segmentation and non-autoregressive ASR to realize high accuracy and low RTF ASR. As a non-autoregressive ASR, the insertion-based model is used. In addition, instead of concatenating separated models for segmentation and ASR, we introduce a new architecture that realizes audio segmentation and non-autoregressive ASR by a single neural network. Experimental results on Japanese and English dataset show that the method achieved a reasonable trade-off between accuracy and RTF compared with baseline autoregressive Transformer and connectionist temporal classification.
Contextual Meaning-Making in Reading: The Role of Affect

Emotions play a crucial role in how readers process, comprehend, and experience texts. This chapter focuses on what we have learned about emotional aspects of reading by using eye tracking, a methodology that provides detailed information about the time-course of reading processes as they occur online. The chapter first introduces basic emotion concepts relevant for reading research. It then describes key measures of eye movements used in previous research, focusing on sentence and text comprehension. We then briefly review previous eye tracking research on the role of emotions in reading conducted at three levels: word, sentence, and text. It appears that while word-level emotion effects have received quite a lot of attention, much less empirical work has been conducted on sentence and text-level phenomena. This lack of empirical evidence is reflected in a dearth of theoretical accounts, which currently are still under development. Thus, there is a clear need for vigorous empirical research to help advance theoretical work on emotional aspects of reading. This chapter highlights the importance of using naturalistic texts and the need for further development in advanced exploratory, predictive, and explanatory computational methods and models in order to foster understanding of the emotional aspects of reading.
A Quantum-Inspired Neural Network Model for Predictive BPaaS Management

Nowadays, companies are more and more adopting cloud technologies in the management of their business processes rising, then, the Business Process as a Service (BPaaS) model. In order to guarantee the consistency of the provisioned BPaaS, cloud providers should ensure a strategical management (e.g., allocation, migration, etc.) of their available resources (e.g., computation, storage, etc.) according to services requirements. Existing researches do not prevent resource provision problems before they occur. Rather, they conduct a real-time allocation of cloud resources. This paper makes use of historical resource usage information for providing enterprises and BPaaS providers with predictions of cloud availability zones’ states. For that, we first propose a Neural Network-based prediction model that exploits the superposition power of Quantum Computing and the evolutionary nature of the Genetic Algorithm, in order to optimize the accuracy of the predicted resource utilization. Second, we define a placement algorithm that, based on the prediction results, chooses the optimal cloud availability zones for each BPaaS fragment, i.e. under-loaded servers. We evaluated our approach using real cloud workload data-sets. The obtained results confirmed the effectiveness and the performance of our NNQGA approach, compared to traditional techniques.
Graph Ensemble Networks for Semi-supervised Embedding Learning

Recently, semi-supervised graph learning has attracted growing research interests. Since the Graph Convolutional Network (GCN) was formulated, some studies argue that shallow architectures fall through over-fitting and have limited ability to aggregate information from high-order neighbors. However, although deep GCNs have powerful nonlinear fitting ability, problems such as over-smoothing still exist with the expansion of layer depth. In this paper, we explore such an ignored question that whether a shallow GCN can achieve significant improvement over deep GCNs. Motivated by this curiosity, we propose an effective graph learning framework—Graph Ensemble Network (GENet) for semi-supervised learning tasks. We combine ensemble learning with data augmentation, which samples multi-subgraphs by randomly removing some edges to generate different topology spaces, and knowledge distillation is introduced to make the multi-subnetworks learn collaboratively. The central idea is that employ attention mechanism and consistency constraint to learn adaptive importance weights of the embeddings from the ensemble model, and then transfer learned knowledge to a shallow GCN. Extensive experiments on graph node classification verify the superiority of the proposed GENet compared with the state-of-the-art methods.
Stereo digital image correlation: formulations and perspectives

A literature review regarding functionals used in stereo digital image correlation (SDIC) is presented. A suitable functional for performing data assimilation, in the sense that all available information at all times is wisely taken into account, is also introduced. It is based on the comparison between a substitute image and actual ones, together with an associated weight. An interpretation of this weight is proposed. Eventually, a link between the functional and global SDIC ones is established. It clearly shows that the former encompasses the latter and provides the consistent weighting scheme to use in usual SDIC instead of ad hoc schemes.