Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Market Data Structures Via Machine Learning Algorithms

We made use of a stacked autoencoder machine learning algorithm to detect injected anomalies in swaption volatility offset cubes, a financial market data entity. These consist of 240 datapoints each. Injected cubes contained a modification at one single point, five modi-fied cubes with modifications of different severity had been constructed as our test cases. We investigated the detection results of four slightly different autoencoder architectures with SELU activation, ADAM optimizer and five symmetric hidden layers, differing in number of nodes in the bottleneck layer and whether input noise was applied or not. Implementation was established using the tensorflow library.Unsupervised training was performed on 638 historical swaption volatility offset cubes, ten percent of which had been randomly selected and set aside to constitute the test dataset. The trained algorithms detected the three largest injections reliably and marked a total of 25 da-tasets out of 638 historical and 5 injected ones as ‘spurious’, including all five cubes containing an injection. Using this technology in quality assurance to pre-select for conspicuous data, we could – in our case – reduce the data requiring human attention by 96%, making it a useful tool for the market data quality manager which can easily be applied to other problems. Code and example data can be found on our GitHub repo (Roeder Dirk, Mueller Henning, 2019).
Face Mask Detection Using Hybrid Approach

Recently the world is suffering from a pandemic. Nations were extensively affected by the Covid-19. Individuals are fighting the battle to survive; people lost their lives or the ones that matter to them while some lost their livelihood. In an hour of need everyone is trying their best to win this battle, (WHO) World Health Organization advised, to maintain at least one-meter distance, to get a vaccination, and to wear face masks. Face masks can be efficacious as a barrier between humans and the virus. The Face Mask Detection performs a critical task in suppressing the transmission of covid-19. Here, the Face masks detector was constructed with a hybrid technique. The real-time face masks detector was distributed into two segments: Training Phase and Testing Phase. The paper represents the union of the transfer learning concept, MobileNetV2 model by google, and the Caffe model, with the help of some open-source libraries used for computer vision and machine learning. The datasets were provided by the Kaggle, and they were parted into two categories: training and testing sets. The model's accuracy was compared for the two datasets. The dataset with a greater number of images has better accuracy, and to avoid overfitting data augmentation was used.
Fine-grained Entity Typing without Knowledge Base.

Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision. However, the reliance on KB means this training setting can be hampered by the lack of or the incompleteness of the KB. To alleviate this limitation, we propose a novel setting for training FET models: FET without accessing any knowledge base. Under this setting, we propose a two-step framework to train FET models. In the first step, we automatically create pseudo data with fine-grained labels from a large unlabeled dataset. Then a neural network model is trained based on the pseudo data, either in an unsupervised way or using self-training under the weak guidance from a coarse-grained Named Entity Recognition (NER) model. Experimental results show that our method achieves competitive performance with respect to the models trained on the original KB-supervised datasets.
Text Detection in Images

In Chap. 4, the methods based on Delaunay triangulation and histogram-oriented moments for text detection in video are discussed. The methods explored temporal information for achieving better text detection results. This chapter focuses on cognitive-based text detection (Dey et al. in, Neurocomputing, 96–112, 2017 []) in still image of video and natural scene images.
Variable-curvature hybrid A-star search for AMR path planning in limited space

A new AMR path planning method is proposed: Firstly, the local map is constructed by the prior road map, obstacle information and Contours of other AMRs. Then the path can be successfully planned in the limited manufacturing scenario, using a variable-curvature hybrid A-star search.
Digital Literacy

Teaching and learning in the 21st century is greatly influenced by advancements in technology. Numerous technological tools are today being maximized as mediums of communication and instruction. Learning at various levels requires a level of technological literacy, especially to communicate, as well as partake in educative tasks. Similarly, to gain access to opportunities in the 21st century, a certain level of technological skill is needed. Therefore, it has become imperative that both young and adult learners acquire the skills needed to navigate the contemporary infrastructures of communication. This chapter is a discussion of the historical perspectives, definitions, significance, as well as the approaches to the development of digital literacy in adult learners, a key literacy required for social justice in a tech-dependent world.
Encrypted Traffic Identification Method Based on Multi-scale Spatiotemporal Feature Fusion Model with Attention Mechanism

With the increasing complexity of encryption protocols in recent years, the existing network traffic identification and classification methods are facing great challenges. Researchers have found that most of the improved methods for deep learning are achieved by increasing the width and depth of the network. However, a large number of parameters need to be calculated in the process of network training, which increase the computational complexity of the algorithm. To address the problems of different important features characterization, this paper proposes a new classification model, which firstly preprocesses original traffic, followed by extracting the spatial features by CNN layers, obtaining the temporal features from LSTM layers, and then we fuse multi-scale features before features are put into attention mechanism to improve the ability of feature representation. Finally, we validate the effectiveness of our method on handling the ISCXTor2016 dataset. The experimental results show that the classification algorithm proposed in this paper achieved the lowest loss and the highest accuracy in the identification of encrypted traffic.
MEML: A Deep Data Augmentation Method by Mean Extrapolation in Middle Layers

Data augmentation, generating new data that are similar but not same with the original data by making a series of transformations to the original data, is one of the mainstream methods to alleviate the problem of insufficient data. Instead of augmenting input data, this paper proposes a method for augmenting features in the middle layers of deep models, called MEML (Mean Extrapolation in Middle Layers). It gets the features outputted by any middle layer of deep models and then creates new features by extrapolating some randomly selected features with their corresponding class mean. After that, it replaces these selected features with the new ones, and then let the new composed output continue to propagate forward. Experiments on two classic deep neural network models and three image datasets show that our MEML method can significantly improve the model classification accuracy and outperform the state-of-the-art feature space augmentation methods such as dropout and K Nearest Neighbors extrapolation in most experiments. Interestingly, when coupled with some input space augmentation methods, e.g., rotation and horizontal flip, MEML could further improve the performance of deep models, implying that the input space augmentation methods and MEML could complement each other.
Establishment of the model on the expression and guidance of contemporary college students’ demands in the cyberspace environment

Abstract Faced with the most recent changes of the times, expectations of the nation and mission of universities, college students have been playing a major role in their respective university and become increasingly engaged in the development of universities and colleges. They are critical thinkers who are willing to undertake their responsibilities, and they have a strong sense of equality, legal awareness and consciousness for protecting their rights. In addition, in terms of the campus environment where they live, the needs of these college students vary from each other, and their demands for interest deserve much attention from both universities and the entire society. This paper intends to explore the status-quo of how contemporary college students’ demands are expressed, guided and handled in the cyberspace environment, and based on our analysis, we aim to put forward targeted suggestions of optimization. We have adopted the method of questionnaire survey to investigate the mechanism on the expression and guidance of contemporary college students’ demands in the cyberspace environment. By using such softwares as Excel, SPSS and Amos in our statistical analysis, we have established the model on the expression and guidance of contemporary college students’ demands in the cyberspace environment, which provides theoretical support for the endeavor of colleges and universities in this respect.
European Union Digital Passenger Locator Form System (EUdPLF) development

Abstract Introduction Passenger Locator Forms (PLFs) are used by public health authorities to facilitate international contact tracing. Digital PLFs (dPLF) allows for easier, more rapid data collection and exchange between stakeholders, making international contact tracing more effective and efficient. In response to COVID-19, EU HEALTHY GATEWAYS developed a common European Union digital Passenger Locator Form System (EUdPLF) for all transport sectors (aircrafts, cruise ships/ferries and ground transport). Methods A working group was established consisting of 9 EUMS, EASA, EMSA, ECDC, IATA, ERA, CLIA EUROPE. A minimum mandatory dataset was developed. Personal data protection procedures and security measures were implemented. Interconnection of EUdPLF with the EASA exchange platform allowing exchange of data between MS with national dPLF and MS using the EUdPLF was completed. Pilot testing was conducted with four MS for the air and ferry sector. Results The EUdPLF system developed consists of a multilingual website being the first point of information for users and the EUdPLF app which is the web app for completion of the dPLF. One dPLF is completed per family/group, a QR code is generated and dPLF is sent to the passenger's email. Pilot testing generated 1013 PLFs submissions and 1450 passenger registrations. Feedback received was positive and used to improve user experience. To date, Italy is officially using the EUdPLF for all arriving passengers for all modes of transport. France, Slovenia, Lithuania and Austria are finalising practicalities before official use and testing of the system is ongoing with other MS. Conclusions EUdPLF provides multilingual, user-friendly, secure and GDPR compliant single entry point and database for the collection of PLFs, flexible to be customised to the MS needs and to be connected to external systems e.g. check-in system of transport operators, vaccination/testing/certificate information systems of each MS.
A Study on the Process of ISO-Based Capstone Design Class in the Engineering Field

The purpose of this study was to analyze for process of capstone design class based on ISO in engineering. Based on results from literature review, this study clarifies a theoretical ground for ‘process of capstone design class based on ISO’. Initial items to measure this concept were verified by content analysis and then finalized. After a pilot test done with 887 college students, gathered data were analyzed by item selection and exploratory factor analysis to verify their validity. Next, the main test implemented with 1,228 college students was analyzed with exploratory factor analysis using the method for rotation based on maximum likelihood analysis and direct oblimin for validating the final items to measure ‘process of capstone design class based on ISO’. As a result, the scale for ‘process of capstone design class based on ISO’ consists of 40 items to measure the following four factors: reflective observation, reflective abstract, active experiment and experiential awareness. Criterion-related validity were performed at last to check this scale’s theoretical construct. In conclusion, this study concluded that the constructs for process of capstone design class based on ISO could be generalized and applicable to other samples.
Digital Radiography with a Consumer Camera: Image Denoising and Deblurring

Projection radiography is the most popular medical imaging modality due to its simple design and ease of use. To maintain quality, direct flat-panel detectors must be used to collect patient images, thereby providing a digital radiography (DR) imaging system. In this paper, we investigate an imaging system utilizing a DSLR camera to capture images from a scintillator illuminated by X-rays passing through the object to be imaged. However, maintaining the quality of the image from the proposed system is difficult since it contains impulsive-style noise, and blur varies based on distance. To get rid of unwanted radiographic impulsive noise, we use a statistical-based impulsive noise removal method. To handle the blurring issue in our proposed imaging system, we first use a systematic method to estimate the X-ray source and detector point spread functions (PSFs) from the radiographic image. The PSFs are then used in a non-blind deconvolution algorithm to deblur the image. Experiments on a variety of DR images show that with the proposed denoising and deblurring approaches, our imaging system can provide DR images with fine detail, and it can potentially be used for basic radiography.
GuidedMix: An on‐the‐fly data augmentation approach for robust speaker recognition system

Data augmentation is an essential technique for building a high-robustness speaker recognition system. this letter proposes a novel on-the-fly data augmentation strategy called GuidedMix. It significantly increases augmented data fidelity by mixing the spectrum of different speakers in a guided way, which can not only ensure that the central discriminative regions of the spectrum are always retained after the mixing operation, but also the pasting patches from the different spectrums are effective enough. This replacement strategy allows us to generate more reasonable training samples. At last, we refine the model trained with GuidedMix with un-augmented data to balance robustness and accuracy. Experiments on VoxCeleb indicate that GuidedMix improves performance under clean, noisy, and short-duration test scenarios and can be well combined with offline data augmentation.
Multi-Facet Clustering Variational Autoencoders

Work in deep clustering focuses on finding a single partition of data. However, high-dimensional data, such as images, typically feature multiple interesting characteristics one could cluster over. For example, images of objects against a background could be clustered over the shape of the object and separately by the colour of the background. In this paper, we introduce Multi-Facet Clustering Variational Autoencoders (MFCVAE), a novel class of variational autoencoders with a hierarchy of latent variables, each with a Mixture-of-Gaussians prior, that learns multiple clusterings simultaneously, and is trained fully unsupervised and end-to-end. MFCVAE uses a progressively-trained ladder architecture which leads to highly stable performance. We provide novel theoretical results for optimising the ELBO analytically with respect to the categorical variational posterior distribution, and corrects earlier influential theoretical work. On image benchmarks, we demonstrate that our approach separates out and clusters over different aspects of the data in a disentangled manner. We also show other advantages of our model: the compositionality of its latent space and that it provides controlled generation of samples.
Sequence-to-Sequence Lexical Normalization with Multilingual Transformers

Current benchmark tasks for natural language processing contain text that is qualitatively different from the text used in informal day to day digital communication. This discrepancy has led to severe performance degradation of state-of-the-art NLP models when fine-tuned on real-world data. One way to resolve this issue is through lexical normalization, which is the process of transforming non-standard text, usually from social media, into a more standardized form. In this work, we propose a sentence-level sequence-to-sequence model based on mBART, which frames the problem as a machine translation problem. As the noisy text is a pervasive problem across languages, not just English, we leverage the multi-lingual pre-training of mBART to fine-tune it to our data. While current approaches mainly operate at the word or subword level, we argue that this approach is straightforward from a technical standpoint and builds upon existing pre-trained transformer networks. Our results show that while word-level, intrinsic, performance evaluation is behind other methods, our model improves performance on extrinsic, downstream tasks through normalization compared to models operating on raw, unprocessed, social media text.
Habitat 2.0: Training Home Assistants to Rearrange their Habitat

We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios. We make comprehensive contributions to all levels of the embodied AI stack - data, simulation, and benchmark tasks. Specifically, we present: (i) ReplicaCAD: an artist-authored, annotated, reconfigurable 3D dataset of apartments (matching real spaces) with articulated objects (e.g. cabinets and drawers that can open/close); (ii) H2.0: a high-performance physics-enabled 3D simulator with speeds exceeding 25,000 simulation steps per second (850x real-time) on an 8-GPU node, representing 100x speed-ups over prior work; and, (iii) Home Assistant Benchmark (HAB): a suite of common tasks for assistive robots (tidy the house, prepare groceries, set the table) that test a range of mobile manipulation capabilities. These large-scale engineering contributions allow us to systematically compare deep reinforcement learning (RL) at scale and classical sense-plan-act (SPA) pipelines in long-horizon structured tasks, with an emphasis on generalization to new objects, receptacles, and layouts. We find that (1) flat RL policies struggle on HAB compared to hierarchical ones; (2) a hierarchy with independent skills suffers from 'hand-off problems', and (3) SPA pipelines are more brittle than RL policies.
Fast and Accurate 3D Reconstruction of Plants Using MVSNet and Multi-View Images

Accurate 3D reconstruction of morphological structures, gro- wth processes is a prerequisite for image-based measurement of biological organisms. It is a particular challenge for digital plant and crop research. In this paper, multi-view images of structurally very different plants including Arabidopsis, Wheat and Physalis, were taken with a consumer-grade digital camera with a zoom lens and a turntable. Camera parameters were estimated using the SfM method (COLMAP), and then 3D point clouds were reconstructed using MVSNet and the camera parameters. The results show that the proposed method is able to quickly produce denser and complete 3D point cloud of plants. Compared with the existing methods, our method is an end-to-end framework and is more automatic and more promising for dense 3D reconstruction of plants, especially for plant phenotyping.
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning

Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge transfer, a common (latent) space is adopted for associating the visual and semantic domains in ZSL. However, existing common space learning methods align the semantic and visual domains by merely mitigating distribution disagreement through one-step adaptation. This strategy is usually ineffective due to the heterogeneous nature of the feature representations in the two domains, which intrinsically contain both distribution and structure variations. To address this and advance ZSL, we propose a novel hierarchical semantic-visual adaptation (HSVA) framework. Specifically, HSVA aligns the semantic and visual domains by adopting a hierarchical two-step adaptation, i.e., structure adaptation and distribution adaptation. In the structure adaptation step, we take two task-specific encoders to encode the source data (visual domain) and the target data (semantic domain) into a structure-aligned common space. To this end, a supervised adversarial discrepancy (SAD) module is proposed to adversarially minimize the discrepancy between the predictions of two task-specific classifiers, thus making the visual and semantic feature manifolds more closely aligned. In the distribution adaptation step, we directly minimize the Wasserstein distance between the latent multivariate Gaussian distributions to align the visual and semantic distributions using a common encoder. Finally, the structure and distribution adaptation are derived in a unified framework under two partially-aligned variational autoencoders. Extensive experiments on four benchmark datasets demonstrate that HSVA achieves superior performance on both conventional and generalized ZSL. The code is available at \url{this https URL} .
How to Write a Coequation ((Co)algebraic pearls)

There is a large amount of literature on the topic of covarieties, coequations and coequational specifications, dating back to the early seventies. Nevertheless, coequations have not (yet) emerged as an everyday practical specification formalism for computer scientists. In this review paper, we argue that this is partly due to the multitude of syntaxes for writing down coequations, which seems to have led to some confusion about what coequations are and what they are for. By surveying the literature, we identify four types of syntaxes: coequations-as-corelations, coequations-as-predicates, coequations-as-equations, and coequations-as-modal-formulas. We present each of these in a tutorial fashion, relate them to each other, and discuss their respective uses.