Reviewerly/exHarmony huggingface dataset:
Context-Aware Data Augmentation for Efficient Object Detection by UAV Surveillance

The problem of object detection by YOLOv4 deep neural network (DNN) is considered on Stanford drone dataset (SDD) with object classes (pedestrians, bicyclists, cars, skateboarders, golf carts, and buses) collected by Unmanned Aerial Vehicle (UAV) video surveillance. Some frames (images) with labels were extracted from videos of this dataset and structured in the open-access SDD frames (SDDF) version (https://www.kaggle.com/yoctoman/stanford-drone-dataset-frames). The context-aware data augmentation (CADA) was proposed to change bounding box (BB) sizes by some percentage of its width and height. To investigate the possible effect of the dataset labeling quality the "dirty" and "clean" dataset versions were prepared, which differ by the evaluation subset only. CADA procedures lead to significant improvement of performance by loss and mean average precision (mAP) that can be observed both for "dirty" and "clean" evaluation subsets in comparison to experiments without CADA. Moreover, CADA procedures allow to get the mAP values on the "dirty" (real) evaluation subset that can be similar (and for some classes higher even) to the mAP values on the "clean" (ground-truth - GT) evaluation subset without CADA procedures. This effect can be explained by increase of signal-to-noise ratios for object-to-background pairs after IN-like cropping CADA procedures and then by increase of variability of object-to-background pair after subsequent OUT-like enlarging CADA procedures. It should be noted the non-commutative nature of CADA-based retraining procedures because their reverse direction like first-OUT-then-IN CADA in contrast to first-IN-then-OUT CADA did not lead to such a big increase of mAP values. Several CADA-sequences were analyzed and the best strategy consists in first-IN-then-OUT CADA procedures, where the extent of decrease and increase of BBs width and height can be different for various applications and datasets.
User Interface Evaluation of Design Alternatives of the Emotion-Libras—An Emotional Self-Report Instrument for Sign Language Users

Abstract For researchers interested in analyzing participants’ self-reflection about their emotional experience during user studies, having an instrument designed for a specific target user can be seen to show empathy for providing socio-cultural elements that involved parties feel a connection with. In user studies, communication mediation between sign language users and non-signers may include a bias; therefore, direct communication between the involved parties is welcome to reduce this risk and expand sign language users’ autonomy. This paper presents the Emotion-Libras, a self-report instrument to collect emotional responses of sign language users after interacting with technology and report findings from a user study. We conducted a focus group with five sign language users to collect their perceptions regarding the three design alternatives of the instrument. As a result, participants selected the best-fitted design interface of the instrument and also found room for improvement in video quality, language choice, written labels and user control.
TRR: Target-relation regulated network for sequential recommendation

Item co-occurrence is an important pattern in recommendation. Due to the difference in correlation, the matching degrees between the target and historical items vary. The higher the matching degree, the greater probability they co-occur. Recently, the recommendation performance has been greatly improved by leveraging item relations. As an important bond imposed by relations, these connected items should have a strong correlation in the calculation of certain measures. This kind of correlation can be the biased knowledge that benefits parameter training. Specifically, we focus on tuples containing the target item and latest relational items that have relations such as complement or substitute with the target item in user's behavior sequence. Such close relations mean the matching degrees between relational items and historical items should be highly affected by that of the target item and historical items. For example, given a relational item having relation complement with the target item, if the target item has high matching degrees with some items in user's behavior sequence, this complementary item should behave similarly for the co-occurrence of complementary items. Under guidance of the above thought, in this work, we propose a target-relation regulated mechanism which converts the biased knowledge of high correlation of matching degrees into a regulation. It integrates the target item and relational items in history as a whole to characterize the matching score between the target item and historical items. Experiments conducted on three real-world datasets demonstrate that our model can significantly outperform a set of state-of-the-art models.
Cell phone uses among students: Two aspects of coin

Mobile phones and similar gadgets are increasingly being used owing to the massive growth in internet and advancement in digital device technology. Learning via mobile phones and similar gadgets has become one of the most popular and useful teaching tools used by different class of students across the world. But any new technology has some drawbacks also which should be addressed in timely and efficiently to reduce its ill effects. A multipronged approach may be utilised to curtail the ill effects of mobile phones and similar gadgets.
Nim variants

Combinatorial game theory provides results for the class of two-player, deterministic games with perfect information. With the aim of generalizing this theory to the class of non-perfect information games in mind, we introduce and analyze three variants of the game of Nim. In these variants, the opponent only receives partial information on the move executed by the opponent. We model the variants as games in extensive form and compute Nash equilibria for different starting configurations. For one variant, this provides a full characterization of the game. For the other variants, we prove some partial and structural results, but a full characterization remains elusive.
Optimisation Techniques for Flexible SPARQL Queries

Resource Description Framework datasets can be queried using the SPARQL language but are often irregularly structured and incomplete, which may make precise query formulation hard for users. The SPARQL AR language extends SPARQL 1.1 with two operators—APPROX and RELAX—to allow flexible querying over property paths. These operators encapsulate different dimensions of query flexibility, namely, approximation and generalisation, and they allow users to query complex, heterogeneous knowledge graphs without needing to know precisely how the data is structured. Earlier work has described the syntax, semantics, and complexity of SPARQL AR , has demonstrated its practical feasibility, but has also highlighted the need for improving the speed of query evaluation. In the present article, we focus on the design of two optimisation techniques targeted at speeding up the execution of SPARQL AR queries and on their empirical evaluation on three knowledge graphs: LUBM, DBpedia, and YAGO. We show that applying these optimisations can result in substantial improvements in the execution times of longer-running queries (sometimes by one or more orders of magnitude) without incurring significant performance penalties for fast queries.
Session-Recommendation Based on Gated Neural Network to Extract Item Structure Feature Graph

The issue of inaccurate user portrait recommendations for short-term conversations is solved. The classical recommendation is based on an assumption: the user's historical behavior can represent the user's stable long-term preferences, and the result is generated based on the user's historical behavior. However, there is sparseness in the user's historical behavior information record in the actual data set, and the obtained data cannot accurately represent the user's preferences. Research on a session recommendation focuses on user behavior data in the short term, and aims at predict anonymous user behavior based on in-session behavior information. It plays a vital role in application scenarios where user behavior information is scarce. Feature extraction is performed on the user's anonymous behavior information graph generated in a fixed period of time as the embedding representation of user's interest preference; the graph structure of the same item in the global click interaction graph is combined with the input a gated graph neural network as the item attribute embedding representation. The prediction part adopts simulated interactive calculation to produce results. With the help of gated graph neural network, features extraction and analysis of data in short-term sessions: session-based user behavior data and in-session item attributes are obtained from the graph data structure, which reduces errors caused by inconsistent data processing methods. The user's current interest is captured based on the behavior information in the session, the global inter-item conversion graph is generated based on the sequence of click moments, and the feature vector is obtained through graph neural network processing. The generation of recommendation results is more time-sensitive, complete the anonymity problem due to the scarcity of user's information, and improves the accuracy of recommendations.
A more cost-efficient Chinese Named Entity Recognition based on trigger and matching network

The lack of training data in new domain is a typical problem for named entity recognition (NER). Currently, researchers have introduced “entity trigger” to improve the cost-effectiveness of the model. However, it still required the annotator to attach additional trigger label, which increases the workload of the annotator. Moreover, this trigger applies only to English text and lacks research into other languages. Based on this problem, we have proposed a more cost-effective trigger tagging method and matching network. The approach not only automatic tagging entity triggers based on the characteristics of Chinese text, but also adds mogrifier LSTM to the matching network to reduce context-free representation of input tokens. Experiments on two public datasets show that our automatic trigger is effective. And it achieves better performances with automatic trigger than other state-of-the-art methods (The F1-scores increased by 1∼4).
Product Agnostic Generic Software Design and Development

The Automotive Software Architecture consists of several components, like the Third-Party Software/Stack, OEM Standard Software, and Supplier Software. As an automotive Tier-1 supplier, we face enormous challenges in reusing the software components across various product lines because of many parameters like the compilers, development, folder structure, integration procedures, and lack of product knowledge. Due to the decentralized way of working, the engineers focus only on the associated project and are oblivious to the others, resulting in redundant development efforts and a weaker knowledge management database. This paper presents a novel approach to configuring generic software components for various products using an in-house developed Python User Interface. This approach’s proof of concept for the generic Flash Bootloader component is available in this paper.
Engineered wisdom for learning machines

We argue that the concept of practical wisdom is particularly useful for organising, understanding, and improving human-machine interactions. We consider the relationship between philosophical analysis of wisdom and psychological research into the development of wisdom. We adopt a practical orientation that suggests a conceptual engineering approach is needed, where philosophical work involves refinement of the concept in response to contributions by engineers and behavioural scientists. The former are tasked with encoding as much wise design as possible into machines themselves, as well as providing sandboxes or workspaces to help various stakeholders build practical wisdom in systems that are sufficiently realistic to aid transferring skills learned to real-world use. The latter are needed for the design of exercises and methods of evaluation within these workspaces, as well as ways of empirically assessing the transfer of wisdom from workspace to world. Systematic interaction between these three disciplines (and others) is the best approach to engineering wisdom for the machine age.
Procedural generation of virtual pavilions via a deep convolutional generative adversarial network

Abstract Virtual pavilions can help spread culture and bring fun. A virtual pavilion needs a designed map, and terrain editors then manually layout each part of it. Procedural content generation via machine learning can quickly generate virtual pavilion maps to assist in virtual pavilion design. This article proposes adding a self‐attention module to some commonly used deep convolutional generative adversarial networks to generate virtual pavilion maps. A three‐dimensional(3D) virtual pavilion is built based on these maps, and interactive features are added to make it more experiential. Then the improved and original networks are mainly evaluated in generating maps that are solvable and similar to the training data for finding the best generator. The evaluation results show that our improved methods always perform better on each metric, and the WGAN with a self‐attention module is what we need.
Influential Aspects of Knowledge Sharing in the Context of a Marketplace Platform During the COVID-19 Pandemic: A Brazilian Case Study

The COVID-19 pandemic and social distancing entangled physical trading of products and services worldwide. As a result, the Commercial and Industrial Association of Marília (ACIM) developed and provided businesses with a marketplace platform called “Marília.Dakki.” In this context, knowledge emerged as a resource, and knowledge sharing became a fundamental activity for the marketplace implementation. Thus, the objective of the research was to identify and analyze the influential aspects of knowledge sharing (IAKS) in “Marília.Dakki.” The research is characterized as qualitative and exploratory and used the case study method. Questionnaires were used to acquire data from the 19 subjects involved in the “Marília.Dakki” operations, as well as an interview with the Director of IT and Innovation. As a result of the research, people, processes, and culture were identified as IAKS, and these aspects, when related, generated nine other IAKS. The researchers concluded that these IAKS found in the research impacted the marketplace adoption, in addition to its maintenance as a collaborative environment, and are of great importance for the survival and development of companies, both during the pandemic and after it.
A novel model-based reinforcement learning algorithm for solving the problem of unbalanced reward

Reward signal reinforcement learning algorithms can be used to solve sequential learning problems. However, in practice, they still suffer from the problem of reward imbalance, which limits their use in many contexts. To solve this unbalanced reward problem, in this paper, we propose a novel model-based reinforcement learning algorithm called the expected n-step value iteration (EnVI). Unlike traditional model-based reinforcement learning algorithms, the proposed method uses a new return function that changes the discount of future rewards while reducing the influence of the current reward. We evaluated the performance of the proposed algorithm on a Treasure-Hunting game and a Hill-Walking game. The results demonstrate that the proposed algorithm can reduce the negative impact of unbalanced rewards and greatly improve the performance of traditional reinforcement learning algorithms.
URL Degeneration: A Warning to Scholarly Community

The present study examines the degeneration of web citations in scholarly journals of Library and Information Science, and Communication and Media Studies. The journals were selected based on their high impact factor published between 2008 and 2017. A PHP script was used to crawl the Uniform Resource Locators collected from the references. A total of 8,767 articles were downloaded and 3,97,140 references were extracted. A total of 71,289 URLs were checked for their availability. Further, the characteristic features of URL like file extension, path depth, character length and the toplevel domain were determined. The findings indicated that more number of URLs in LIS journal articles decayed than in Communication and Media Studies journal articles. The majority of errors in both the disciplines were HTTP 404 error code (Not found error). The findings of the study imply that authors, publishers and editorial staff need to ensure the availability of web resources before citing them.
Iterated team semantics for a hierarchy of informational types

In this paper, we introduce and study a framework that is inspired by the team semantics for propositional dependence logic but deviates from it in several respects. Most importantly, instead of the two semantic layers used in dependence logic – possible worlds and teams – a whole hierarchy of contexts is introduced and different types of formulas are evaluated at different levels of this hierarchy. This leads to a rich stratification of informational types. In this framework, the dependence operator of dependence logic can be defined by the standard propositional connectives (negation, conjunction, disjunction and implication). We explore the formal aspects of this approach and apply it to a number of puzzling phenomena related to modalities and conditionals.
Real-Time Monitoring of College Sports Dance Competition Scenes Using Deep Learning Algorithms

In order to improve the real-time detection effect, therefore, a research on real-time scene detection of sports dance competition based on deep learning is proposed. The collected scene image is grayed by using the weighted average method, and the best image interpolation is calculated by using the deep learning method, so as to realize the smooth processing of sawtooth and mosaic information generated by panoramic mapping. After selecting the cube model, the processed scene information is projected to the visual plane to construct the panorama of the competition scene. Finally, combined with the three-frame difference, the changes between adjacent image frames are calculated to obtain the moving target. The test results show that the motion detection accuracy of professional dancers can reach more than 75.0% and that of amateur dancer can reach more than 64.2%.
Classification of Tissue Types in Histology Images Using Graph Convolutional Networks

This article uses Graphic Neural Network (GNN) models on histology images to classify tissue to find phenotypes. The majority of tissue phenotyping approaches are confined to tumor and stroma classification and necessitate a significant number of histology images. In this study, Graphics Convolutional Network (GCN) is used on the CRC Tissue Phenotyping dataset, which consists of seven tissue phenotypes, namely Benign, Complex Stroma, Debris, Inflammatory, Muscle, Stroma, and Tumor. First, the input images are converted into superpixels using the SLIC algorithm and the region neighborhood graphs (RAGs), where each superpixel is a node, and the edges connect neighboring superpixels to each other are converted. Finally, graphic classification is performed on the graphic data set using GCN.
The Online Courses in the System of Training and Further Training of the Teaching Staff in the Context of the Digitalization of the General Education

The article deals with the issues of the modernization of the further training system of the teaching staff in the context of the digitalization of the general education. There is presented the theoretical and pedagogical analysis of the innovative technology’s potential of the organization of the distance education in the form of the online courses as the educational resource, aimed at the teachers’ further training. The authors demonstrate the characteristics of the online courses and specify the definition of the concept “online course”. There is described the classification of the educational online courses. The authors consider the methodological aspects of training the experts of Basic State Examination of Physics on the basis of the implementation of the online course.
A comparative study on the effect of grid printing methods and camera hardware in automatic strain measurement through CGA

Strain analysis is an important step in sheet metal forming operations to identify the potential locations of failure. Circle grid analysis is a widely used method to evaluate the strains in sheet metal forming, where deformed circles on a formed component are measured to evaluate the strains at different locations. In this paper, two different USB cameras are used to capture the images of the deformed grids. The images are processed using the in-house software developed in MATLAB for strain measurement. The effect of image acquisition hardware and grid printing methods on strain measurement accuracy has been analyzed. For this, the sheets are printed with circular grids by screen printing, laser engraving, and electrochemical etching. The sheets are then deformed in incremental forming to produce different levels of strain in the formed components. The deformed grids on the formed components are captured using proposed USB cameras with appropriate attachments and processed by in-house strain measurement software. The strain measured using proposed hardware and software is also compared with the commercial strain measurement system. The mean absolute percentage error in strain measurement with the proposed USB cameras was found to be 0.671 and 0.761. The statistical results show that there is no evidence to support the hypothesis that the two hardware perform differently on average. Hence, the proposed hardware and software can be used in industry and research laboratories for strain analysis in sheet metal forming operations.
Multi-head Mutual Self-attention Generative Adversarial Network for Texture Synthesis

Example-based texture synthesis requires synthesizing textures that are as similar as possible to the exemplar. However, for complex texture patterns, the existing methods lead to wrong synthesis results due to insufficient feature extraction capabilities. To address this problem, this paper proposed an optimized generative adversarial network model to address the quality issues such as low resolution and insufficient detail in texture synthesis. To this end, we propose a new multi-head mutual self-attention (MHMSA) mechanism. Different from the self-attention, MHMSA is to model the mutual relationship of each position in the feature space, and clues from all feature positions can be used to generate details. Therefore, embedding the MHMSA into the generator can help to improve its ability to extract detailed features and global features. Experimental results show that the proposed model significantly improves the visual quality of texture synthesis images, and demonstrates that MHMSA outperforms self-attention in the image generation task.