Reviewerly/exHarmony huggingface dataset:
Intelligent Inventory Management for Cryptocurrency Brokers

In equity trading, internalization is the predominant execution method for uninformed order flow, allowing retail brokers to realize cost savings and thereby offer price improvements to customers. In cryptocurrency trading, there are doubts as to whether informed and uninformed traders can be distinguished in the same way, leading brokers to seek cost savings through internal order matching instead. Using the historical order flow of the German cryptocurrency broker BISON, we present a prediction-based approach to internal order matching: Upon receiving a customer order, our model forecasts whether future order flow will be sufficient to neutralize the order before the settlement date. With a prediction accuracy of 85%, it enables brokers to match three-quarters of order volume internally, which is three times as much as a traditional static approach, and realize meaningful cost savings, even after accounting for common minimum price improvements.
Attention Mechanism for Distinctive Features of Star Fish Detection From under Water Videos

The coral reef and underwater light attenuation shows important problem of star fish detection. The convolutional neural network extracts pattern and textural features along with shape and shade features. The combinational effects of coral reef and star fish show important problem of distinguishing feature requirement. This problem is addressed with use of attention layer in the ResNet18 model which improves the star fish detection capability of the model with almost accuracy of 92%.
An Automatic Construction Technology of Chinese Knowledge Graph for Teaching Plan

With the wide application of knowledge in the field of education, the demand for automatic knowledge graph construction based on educational data is becoming more and more urgent. The existing construction technology of educational Chinese knowledge graph has some problems, such as easy omission of knowledge points, low accuracy of knowledge point relationship, manual participation and so on. This paper puts forward the automatic construction technology of Chinese knowledge graph based on educational teaching plan. The automatic generation of Chinese knowledge graph based on educational teaching plan is aimed to improve teaching efficiency. This paper studies the knowledge graph technology based on teaching plan in the computer field. It automatically extracts knowledge points and the relationship between knowledge points, allocates the relationship weight according to the relationship rules, and generates the knowledge graph based on teaching plan.
A Motivating Case Study on Code Variant Selection by Reinforcement Learning

In this paper, we investigate the applicability of reinforcement learning as a possible approach to select code variants. Our approach is based on the observation that code variants are usually convertible between one another by code transformations. Actor-critic proximal policy optimization is identified as a suitable reinforcement learning algorithm. To study its applicability, a software framework is implemented and used to perform experiments on three different hardware platforms using a class of explicit solution methods for systems of ordinary differential equations as an example application.
Spatial Group-Wise Enhance: Exploring Multiple Spatial Semantics and Local-Global Similarities in Attention for Convolutional Networks

The success of attention modules in CNN has attracted increasing and widespread attention over the past years. However, most existing attention modules fail to consider two important factors: (1) For images, different semantic entities are located in different areas, thus they should be associated with different spatial attention masks; (2) most existing framework exploits individual local or global information to guide the generation of attention masks, which ignores the joint information of local-global similarities that can be more effective. To explore these two ingredients, we propose the Spatial Group-wise Enhance (SGE) module. SGE explicitly distributes different but accurate spatial attention masks for various semantics, through the guidance of local-global similarities inside each individual semantic feature group. Furthermore, SGE is lightweight with almost no extra parameters and calculations . Despite being trained with only category supervisions, SGE is effective in highlighting multiple active areas with various high-level semantics (such as the dog’s eyes, nose, etc.). When integrated with popular CNN backbones, SGE can significantly boost their performance on image recognition tasks. Specifically, based on ResNet101 backbones, SGE improves the baseline by 0.7% Top-1 accuracy on ImageNet classification and 1.6∼1.8% AP on COCO detection tasks. The code and pretrained models are available at https://github.com/implus/PytorchInsight .
New Instructional Design Theory for Integrating Information Technology and Subjects Teaching

As mentioned earlier in the above, theory of the new instructional design was formed after Chinese scholars’ carrying out innovative research on traditional instructional design theory in two aspects.
The EDiT method guideline - enabling distributed teams through situation-adequate method application

To counteract individual losses in distributed product development, the EDiT method (Enabling Distributed Teams) enables product development teams to continuously improve their distributed collaboration based on the characteristics of distributed product development. The EDiT method is the result of research into supporting distributed product development teams and is continuously being developed. For the successful transfer of the method into practice, a guideline for the situation-adequate adaptation and user-centered application of the EDiT method is designed. Therefore, a comprehensive understanding of the requirements for the EDiT method is compiled. An online guideline provides access and situation-adequate selection, adaptation, and application of the suitable method variant. A reference process model serves as orientation support for the situation-adequate application.
Human-AI Interaction Paradigm for Evaluating Explainable Artificial Intelligence

This article seeks to propose a framework and corresponding paradigm for evaluating explanations provided by explainable artificial intelligence (XAI). The article argues for the need for evaluation paradigms – different people performing different tasks in different contexts will react differently to different explanations. It reviews previous research evaluating XAI explanations while also identifying the main contribution of this work – a flexible paradigm researchers can use to evaluate XAI models, rather than a list of factors. The article then outlines a framework which offers causal relationships between five key factors – mental models, probability estimates, trust, knowledge, and performance. It then outlines a paradigm consisting of a training, testing and evaluation phase. The work is discussed in relation to predictive models, guidelines for XAI developers, and adaptive explainable artificial intelligence - a recommender system capable of predicting what the preferred explanations would be for a specific domain-expert on a particular task.
Online Panoptic 3D Reconstruction as a Linear Assignment Problem

Real-time holistic scene understanding would allow machines to interpret their surrounding in a much more detailed manner than is currently possible. While panoptic image segmentation methods have brought image segmentation closer to this goal, this information has to be described relative to the 3D environment for the machine to be able to utilise it effectively. In this paper, we investigate methods for sequentially reconstructing static environments from panoptic image segmentations in 3D. We specifically target real-time operation: the algorithm must process data strictly online and be able to run at relatively fast frame rates. Additionally, the method should be scalable for environments large enough for practical applications. By applying a simple but powerful data-association algorithm, we outperform earlier similar works when operating purely online. Our method is also capable of reaching frame-rates high enough for real-time applications and is scalable to larger environments as well. Source code and further demonstrations are released to the public at: https://tutvision.github.io/Online-Panoptic-3D/
A Survey on Automated Text Summarization System for Indian Languages

Text summarization is the process of finding specific information after reading the document text and generating a short summary of the same. There are various applications of text summarization. It is important when we need a quick result of information instead of reading the whole text. It has become an essential tool for many applications, such as newspaper reviews, search engines, market demands, medical diagnosis, and quick reviews of the stock market. It provides required information in a short time. This paper is an attempt to summarize and present the view of text summarization for Indian regional languages. There are two major approaches of automatic text summarization, i.e., extractive and abstractive that are discussed in detail. The techniques for summarization ranges from structured to linguistic approach. The work has been done for various Indian languages, but they are not so efficient at generating powerful summaries. Summarization has not yet reached to its mature stage. The research carried out in this area has experienced strong progress in the English language. However, research in Indian language text summarization is very few and is still in its beginning. This paper provides the present research status or an abstract view for automated text summarization for Indian languages.
Generating Synthetic Hand-Object Contact Maps for Grasping Region Prediction

From a human point of view, adapting our grasping to different shaped and sized objects feels natural, since we are continuously learning from environment interaction. We have mastered object manipulation, grabbing a hammer by its handle is all but unconscious. However, performing the same task from a machine's perspective is particularly challenging, as it requires an in-depth understanding of objects and its manipulation. We could approximate such an understanding by capturing the hand-object contact resulting from human grasps, i.e., contact maps. This has already been done using cumbersome capture systems, and not on a large scale. In this work, we simplify and accelerate such a process by generating contact maps from our interaction with household objects in photorealistic virtual reality environments. To the best of our knowledge, we are the first to generate contact maps at scale from our interaction in a virtual scenario. We train an image-to-image translation method to predict grasp regions on objects, demonstrating the usefulness of our generated contact maps. Our purpose with this work is to foster applications where hand-object understanding is necessary, so all the developed tools, code and generated data are provided.
Colour Image Retrieval Based on Adaptive Jseg Image Segmentation and Statistical Distance Measure

This paper investigates the problems with automatic image retrieval, and addresses a solution based on Chernoff distance measure and addition to that to improve the ability of the proposed system by using JSEG image segmentation. In the case of automatic image retrieval, it is gruelling to identify the nature of an image and which distribution it follows. The proposed method overcomes this problem by adopting the Chernoff distance cum JSEG image segmentation, since it adapts itself according to the nature of the image and the adopted images are segmented for getting precision over an existing method, i.e. though the input and target images have been distributed to a Gaussian or a non-Gaussian or a mixture of two distributions or even if it is a distribution free. This is the main benefits of the proposed method. In order to validate the proposed method, an precision image database is constructed, which contains multifarious images such as textured, structured, diminish, noised, artefact images; and their feature-vector database is constructed.
Structure-Aware Photorealistic Style Transfer Using Ghost Bottlenecks

Photorealistic style transfer synthesizes a new image from a pair of content and style images. The transfer should express the visual patterns in the former while preserving the content details following the latter. However, most existing methods may generate an image that suffers disrupted content details and unexpected visual cues from the style image; hence, they do not satisfy the photorealism. We tackle this issue with a new style transfer architecture that effectively unifies a content encoder, an Xception style encoder, a Ghost Bottlenecks subnet, and a decoder. In our framework, the style features extracted from the Xception module balance well with the content features obtained from an encoder; the Ghost Bottlenecks subnet then integrates these features and feeds them into a decoder to produce the resulting style transferred image. Experimental results demonstrate that our model surmounts the structure distortion problem to satisfy photorealistic style transfer and hence obtains impressive visual effects.
DyNNamic: Dynamically Reshaping, High Data-Reuse Accelerator for Compact DNNs

Convolutional layers dominate the computation and energy costs of Deep Neural Network (DNN) inference. Recent algorithmic works attempt to reduce these bottlenecks via compact DNN structures and model compression. Likewise, state-of-the-art accelerator designs leverage spatiotemporal characteristics of convolutional layers to reduce data movement overhead and improve throughput. Although both are independently effective at reducing latency and energy costs, combining these approaches does not guarantee cumulative improvements due to inefficient mapping. This inefficiency can be attributed to (1) inflexibility of underlying hardware and (2) inherent reduction of data-reuse opportunities of compact DNN structures. To address these issues, we propose a dynamically reshaping, high data-reuse PE array accelerator, namely <i>DyNNamic</i> . DyNNamic leverages kernel-wise filter decomposition to partition the convolution operation into two compact stages: Shared Kernels Convolution (SKC) and Weighted Accumulation (WA). Because both stages have vastly different dimensions, DyNNamic reshapes its PE array to effectively map the algorithm to the architecture. The architecture then exploits data-reuse opportunities created by the SKC stage, further reducing data movement with negligible overhead. We evaluate our approach on various representative networks and compare against state-of-the-art accelerators. On average, DyNNamic outperforms DianNao by <inline-formula><tex-math notation="LaTeX">$8.4\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$12.3\times$</tex-math></inline-formula> in terms of inference energy and latency, respectively.
Text Classification Using Deep Neural Networks

Text classification is the classification process of assigning a well-defined labelled category, known as a class, to text. In most cases, we use the real-time implementations in which information plays a major role in educational institutions, business, IT industry and public non-profit organizations, and maintaining unorganized information is really a huge task. Classifying the text using DNN models helps us to assemble the documents in a built-in set of classes.
Challenging Issues in Applying GIS to Environmental Geochemistry and Health Studies

GIS has been widely used in geochemistry-related environmental health studies and practices to map and analyze sampling locations and spatial distribution of geochemical features and health information. In the big data era, the focus is shifting towards revealing the hidden patterns and features. This chapter explores the challenging issues of spatial analysis, machine learning, and uncertainty in such studies and practices. Spatial analysis needs to focus more on hot spot analysis and identification of spatial outliers, as well as exploration of spatially varying relationships. Machine learning can be adopted to conduct deep learning with a focus on non-linear features and their links with causal effects. The field and laboratory uncertainty of environmental geochemistry should be incorporated in GIS analysis. The analyses of the association between environment and health need to be more intelligent and accurate. GIS continues to provide useful tools to make novel findings in environmental geochemistry and health from the spatial aspect.
Designing new educational outcomes based on the tools of a multi-environment lesson

The article deals with the problem of designing new educational results using the tools of a multi-environment lesson, among which the author highlights and defines cross-cutting competencies. The author&amp;apos;s classification of cross-cutting competencies is also presented. With regard to the school stage of human education, seven generalized groups of competencies are proposed: social; educational and cognitive; psychological - pedagogical; general civil; technological; general cultural; managerial. The problems of a multi-environment lesson, classes, activities in the article are considered within a broader context associated with the cross-multidimensional organization of the educational process, didactics and organizational and pedagogical approaches to building a multi-environment lesson. The cross-multidimensional organization of the educational process is considered as a result of repetitive and situational, controlled and stochastic interactions and intersections of the educational environment with a variety of spaces and environments, including digital, cultural-historical, ethno-cultural, innovative, technological, valeological, subject-spatial environments; biblio - media, axiological, semantic, semiotic, noospheric, event and other spaces. Examples of intersection and interaction within the lesson, classes on extracurricular activities of individual spaces and environments, as well as the pedagogical results and effects achieved as a result of this are given. A significant place in the article is given to the description of the construction and application of the multi-environment lesson constructor, which allows you to simultaneously use the potentials and capabilities of several spaces and environments, solve educational problems in accordance with the Federal State Educational Standards and the needs of students, ensure the formation of their meta-subject results, cross-cutting competencies, skills of the future. A separate issue, which is covered in the article, is the problem-activity direction of a multi-environment lesson, the role of the teacher in its design, including determining educational results.
Grey Wolf Shuffled Shepherd Optimization Algorithm-Based Hybrid Deep Learning Classifier for Big Data Classification

In recent days, big data is a vital role in information knowledge analysis, predicting, and manipulating process. Moreover, big data is well-known for organized extraction and analysis of large or difficult databases. Furthermore, it is widely useful in data management as compared with the conventional data processing approach. The development in big data is highly increasing gradually, such that traditional software tools faced various issues during big data handling. However, data imbalance in huge databases is a main limitation in the research area. In this paper, the Grey wolf Shuffled Shepherd Optimization Algorithm (GWSSOA)-based Deep Recurrent Neural Network (DRNN) algorithm is devised to classify the big data. In this technique, for classifying the big data a hybrid classifier, termed as Holoentropy driven Correlative Naive Bayes classifier (HCNB) and DRNN classifier is introduced. In addition, the developed hybrid classification model utilizes the MapReduce structure to solve big data issues. Here, the training process of the DRNN classifier is employed using GWSSOA. However, the developed GWSSOA is devised by integrating Shuffled Shepherd Optimization Algorithm (SSOA) and Grey Wolf Optimizer (GWO) algorithms. The developed GWSSOA-based DRNN model outperforms other big data classification techniques with regards to accuracy, specificity, and sensitivity of 0.966, 0.964, 0.870, and 209837ms.
Generating Intelligent Remedial Materials with Genetic Algorithms and Concept Maps

This study proposes an intelligent remedial learning framework to improve students' learning effectiveness.Basically, this framework combines a genetic algorithm with a concept map in order to select a set of remedial learning units according to students' weaknesses of learning concepts.In the proposed algorithm, a concept map serves to represent the knowledge structure of learning concepts, and a genetic algorithm performs an iteratively evolutionary procedure in order to establish remedial learning materials based on students' understanding of these learning concepts.This study also conducted simulations in order to validate the proposed framework using artificially generated data sets, and problematic issues regarding generalizing the special case of the proposed framework are further discussed.The proposed algorithm can be generally-employed in elearning, providing a framework for generating remedial learning materials for all kinds of learning fields.