Reviewerly/exHarmony huggingface dataset:
Social Representations of Communication Design: Symbolic Universes

The development of Design as a discipline in Portugal was slow. Therefore, only in the ‘80s, visual culture was implemented through Communication Design. Its rises were just at the beginning of the twentieth century, which constrain the identification of Communication Design as a specific field of Design, isolated from other areas of knowledge such as Advertising. Communication Design is strategic to conveying messages in a world that converges on visual culture. Its role in society is to facilitate informational and cognitive processes that enhance the human being’s physical, neurological, and psychological behavior and the connection established with objects, others, and the world. The messages’ interpretations are anchored on symbolic representations that compose collective memories, i.e., shared social construction derived from social representations validated by media and social systems. This chapter aims to explore how social representations influence perceptions of Design. We conducted an exploratory study based on the Free Word Association Test with Design students was implemented to put the analysis forward. The main conclusions refer to a central design association for Advertising, and peripheral associations for Marketing, forms of expression, and Design and media products.
Head-Body Correlation for Robust Crowd Human Detection

A simple yet effective proposal-based human head and body detection framework is developed in crowded scenes. Human heads are too small for detectors to locate and human bodies are frequently occluded in the crowds, which requires more robust location capability of detectors. To tackle the issues above, we propose a head-body correlation module to utilize the location prior knowledge of human body and human head. Compared with Faster R-CNN, our approach can improve the Average Precision (AP) gains for human body and head detection by 2.15% and 2.52% on the challenging CrowdHuman dataset.
Hybrid Vision Transformer for Domain Adaptable Person Re-identification

Person re-identification refers to finding person images taken from different cameras at different times. Supervised re-id methods rely on labeled dataset, which is usually not available in real word situations. Therefore, a procedure must be devised to adapt unseen domains in an unsupervised manner. In this work we have proposed a domain adaptation methodology by using hybrid Vision Transformers and incorporating Cluster loss along with the widely used Triplet loss. Our proposed methodology has shown to improve results of exiting unsupervised domain adaptation methods for person re-id.
A new approach to training more interpretable model with additional segmentation

It is not straightforward to understand how the complicated deep learning models work because they are almost black boxes. To address this problem, various approaches have been developed to provide interpretability and applied in black-box deep learning models. However, the traditional interpretable machine learning only helps us to understand the models which have already been trained. Therefore, if the models are not properly trained, it is obvious that the interpretable machine learning will not work well. We propose a simple but effective method which trains models to improve interpretability for image classification. We also evaluate how well the models focus on appropriate objects, not just relying on classification accuracy. We use Class Activation Mapping (CAM) to train and evaluate the model interpretability. As a result, with VOC PASCAL 2012 datasets, when the ResNet50 model is trained by the proposed approach the 0.5IOU is 29.61%, while the model which is trained only by images and labels is 13.00%. The classification accuracy of the proposed approach is 75.03%, the existing method is 68.38%, and FCN is 60.69%. These evaluations show that the proposed approach is effective.
Stochastic Volatility Analysis Model of regional financial market based on S-type membership function

In order to overcome the low precision of membership degree calculation and random fluctuation analysis in traditional regional financial market random fluctuation analysis model, this paper proposes a new regional financial market random fluctuation analysis model based on S-type membership function. This paper analyzes the characteristic types of regional financial market random volatility, constructs the expected return membership function and risk membership function of regional financial market through S-type function, and on this basis, analyzes the time series data of regional financial market, and obtains the first-order random volatility model and local random volatility model. The local stochastic volatility model is extended to obtain the regional financial market stochastic volatility analysis model. Finally, through the volatility persistence parameters and volatility level parameters, the analysis of regional financial market random volatility is completed. The experimental results show that, compared with the traditional analysis model, the membership calculation accuracy and random volatility analysis accuracy of the constructed model are greatly improved, and the model has stronger practical application performance, which helps regional financial markets better apply volatility risk.
Advances in Autonomous Systems: A Summary of the AutoDefence Summer School at IEEE ICAS’21

This paper presents a panel summary on the framework of Autonomous Systems (AS) and paradigms in development. AS are advanced intelligent systems and general AI technologies triggered by the transdisciplinary development in intelligence science, system science, brain science, cognitive science, robotics, computational intelligence, and intelligent mathematics. It is recognized that, in a rigorous perspective, the only matured AS is human brains and human collective intelligence. It explains why there was rarely man-made AS in the past half century, because of the theoretical, mathematical, computational, and programming language unreadiness. Therefore, the ultimate goal of AS is to implement a brain-inspired system that may think and behave as a human counterpart in hybrid intelligent systems and general AI implementations. There is no doubt that AS will be increasingly demanded by the intelligence-based industries and societies for cognitive computers, deep machine learning systems, robotics, brain-inspired systems, mission-critical systems, self-driving vehicles, and intelligent appliances.
Mechanisms for Encrypting Big Unstructured Data: Technical and Legal Aspects

The article describes the issues which appear during the storage and search in big unstructured data which is kept in cloud storage, as well as encryption mechanisms to optimize the search for such data and solutions used to process big unstructured data. The article is concerned with the technical and legal aspects of processing such data.
How to send entangled photons across hundreds of km? A multimode platform for near-term quantum repeaters

Cleverly utilized, quantum theory can tremendously enhance a broad range of optical technologies from secure communication and distributed computing to super-resolved imaging. In particular, pairs of photons can be easily produced in quantum entangled states of their polarizations – Bell states. Such states yield a non-local property. Measuring polarization of one photon gives a random result, but then the other is always found in the orthogonal polarization. While of many applications, entangled photonic states are also very fragile. After transmission through 100 km of telecom (1500 nm) fiber only ca. 1% of the photons survive and an entangled state cannot be directly amplified. As a solution quantum repeaters (QR) have been proposed and demonstrated [1] .
Inversion of managerial component of architectural education process

The purpose of the study is to identify ways of inversion of professional knowledge and competencies of an architect on the example of managerial ones. The research problem is caused by the need to include professional education in the implementation of programs for the socio-economic development of the country and regions, to increase the competitiveness of graduates and to develop technologies for introducing innovations in education. The research hypothesizes that it is necessary to provide conditions for communication of the academic society of higher architectural schools with representatives of the external environment – administrative structures, industrial, design and other organizations, as well as representatives of civil society. A comparative analysis of the organizational and administrative components of the process of professional architectural education revealed four types of strategies for its development and four different approaches to the management of architectural schools: achieving leadership in all areas, promoting the dissemination of knowledge in society, raising the level of culture in society, pragmatic idea of providing access to the market of design services by obtaining a professional license. The comparison criteria were the goals, motivation, methods, principles, content, evaluation and learning outcomes laid down in the development strategies. The scientific novelty of the research lies in the approach to the study of the processes of inversion of professional knowledge and competencies acquired during architectural education, a look at the organizations of architectural education that are in demand by modern practice, the use of intra-professional, interdisciplinary and public communications for this purpose. As a result of the study, four approaches to the inversion of professional knowledge and competencies in architecture were identified, differing in administrative and managerial components of the educational process. Common to all approaches is the involvement of representatives of the external society, who are carriers of new knowledge and experience for expert evaluation of the components of professional education, selection of research directions, evaluation of the results of scientific and educational activities of students, development and implementation of strategies for the development of the university and architectural faculties.
The Impact of Haze Non-Homogeneity on the Recent Image Dehazing Methods

Haze is a common atmospheric phenomenon that affects the visual quality of the recorded images. Since in general the existing dehazing techniques assume a homogeneous haze distribution, this work analysis the impact of haze non-homogeneity on several recent image dehazing techniques. We perform a comprehensive evaluation using our recent image dataset introduced this year at CVPR NTIRE 2021 workshop. HH-HAZE21 dataset contains 35 non-homogeneous hazy images and their corresponding haze-free images captured from the same scene. The hazy images have been generated using a special setup that includes a professional haze generator. The qualitative and quantitative comparative results demonstrate that images with non-homogeneous haze introduce new challenges for the existing image dehazing techniques.
A New Hybrid Algorithm for the Continuous Site Inspection Points Location-Assignment-Path Planning Problem

This paper considers a continuous inspection points location-allocation-path planning problem with unknown number of the points. A new hybrid algorithm combining modified Genetic Algorithm and Ant Colony Optimization is proposed. The modified Genetic Algorithm introduces the Potential Field heuristic information to get better location while the Ant Colony Optimization is used to solve the path planning problem. The simulation results show the better performance of the proposed algorithm compared with the traditional Genetic Algorithm.
A New Approach for Video Concept Detection Based on User Comments

Video concept detection means describing a video with semantic concepts that correspond to the content of the video. The concepts help to retrieve video quickly. These semantic concepts describe high-level elements that depict the key information present in the content. In recent years, many efforts have been done to automate this task because the manual solution is time-consuming. Nowadays, videos come with comments. Therefore, in addition to the content of the videos, the comments should be analyzed because they contain valuable data that help to retrieve videos. This paper focused especially on videos shared on social media. The specificity of these videos was the presence of massive comments. This paper attempted to exploit comments by extracting concepts from them. This would support the research effort that works only on the visual content. Natural language processing techniques were used to analyze comments and to filter words to retain only the ones that could be considered as concepts. The proposed approach was tested on YouTube videos. The results demonstrated that the proposed approach was able to extract accurate data and concepts from the comments that could be used to ease the retrieval of videos. The findings supported the research effort of working on the visual and audio contents of the videos.
Human Work Interaction Design for Socio-Technical Theory and Action

This chapter introduces and compares systematically HWID to current research knowledge about socio-technical HCI. It introduces to the existing body of HWID research, and to traditional and more recent approaches to work analysis and interaction design. Then it compares systematically HWID and Experience Design (Hassenzahl et al in Synth Lect Human-Cent Inform 3(1):1–95, ), HWID and Practice-Based Design (Volker Wulf et al in Socio-informatics—a practice-based perspective on the design and use of IT artifacts. Oxford University Press, Oxford, ), and HWID and ‘Design Tensions’ for group work (Gross in Comput Supp Cooperat Work (CSCW) 22(4):425–474, ). This is followed up with a summary overview and outline of what is unique and what HWID as a platform for socio-technical HCI theorizing shares with other socio-technical HCI design approaches. The chapter ends with an introduction to four types of HWID relation artefacts.
Clustering Algorithms: An Exploratory Review

A process of similar data items into groups is called data clustering. Partitioning a Data Set into some groups based on the resemblance within a group by using various algorithms. Partition Based algorithms key idea is to split the data points into partitions and each one replicates one cluster. The performance of partition depends on certain objective functions. Evolutionary algorithms are used for the evolution of social aspects and to provide optimum solutions for huge optimization problems. In this paper, a survey of various partitioning and evolutionary algorithms can be implemented on a benchmark dataset and proposed to apply some validation criteria methods such as Root-Mean-Square Standard Deviation, R-square and SSD, etc., on some algorithms like Leader, ISODATA, SGO and PSO, and so on.
COVID-19 Lockdown &amp;amp; Technology Engagement: New Evidence from a Large-Scale m-Health Intervention in India

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Application of Precise Mining Optimization Algorithm in Enterprise Economic Management

In order to adapt to the development of today's society, how to better improve the economic management level of enterprises has become our current topic. Through research and innovation, we found that the cost optimization algorithm can be well applied to the application of enterprise economic management. It opens up a new application field for enterprise economic management. At the same time, according to the actual situation of the optimization and innovation, by the user's praise.
A Gaussian Process Model for Unsupervised Analysis of High Dimensional Shape Data

Applications of medical image analysis are often faced with the challenge of modelling high-dimensional data with relatively few samples. In many settings, normal or healthy samples are prevalent while pathological samples are rarer, highly diverse, and/or difficult to model. In such cases, a robust model of the normal population in the high-dimensional space can be useful for characterizing pathologies. In this context, there is utility in hybrid models, such as probabilistic PCA, which learns a low-dimensional model, commensurates with the available data, and combines it with a generic, isotropic noise model for the remaining dimensions. However, the isotropic noise model ignores the inherent correlations that are evident in so many high-dimensional data sets associated with images and shapes in medicine. This paper describes a method for estimating a Gaussian model for collections of images or shapes that exhibit underlying correlations, e.g., in the form of smoothness. The proposed method incorporates a Gaussian-process noise model within a generative formulation. For optimization, we derive a novel expectation maximization (EM) algorithm. We demonstrate the efficacy of the method on synthetic examples and on anatomical shape data.
Use Case: Autonomous Supply Chain Management System

Autonomous control is seen as an option to handle the increasing complexity and dynamics of supply chains. In autonomous control is defined as a “processes of decentralized decision-making in hierarchical structures”. It presumes interacting elements in non-deterministic systems, which possess the capability and possibility of rendering decisions independently. The objective of autonomous control is the achievement of increased robustness and positive emergence of the total system due to distributed and flexible management of dynamics and complexity. Here, based on the SaaS principle, mediation among autonomous agents, representing nodes in a supply network, is introduced as a more natural approach to supply chain management.
A remark on skew factorization and new 𝔽4-linear codes

Nine new [Formula: see text] linear codes with lengths [Formula: see text] and [Formula: see text] that improve previously best known parameters are constructed. By modifying these codes, another 17 new codes are obtained. It is conjectured that the complete set of factors of [Formula: see text] can be derived from the factors of [Formula: see text] for even values of [Formula: see text] in the skew polynomial ring [Formula: see text]. It is further shown that the [Formula: see text] code obtained is linearly complementary dual.
Semi-unsupervised Learning: An In-depth Parameter Analysis

Creating datasets for supervised learning is a very challenging and expensive task, in which each input example has to be annotated with its expected output (e.g. object class). By combining unsupervised and semi-supervised learning, semi-unsupervised learning proposes a new paradigm for partially labeled datasets with additional unknown classes. In this paper we focus on a better understanding of this new learning paradigm and analyze the impact of the amount of labeled data, the number of augmented classes and the selection of hidden classes on the quality of prediction. Especially the number of augmented classes highly influences classification accuracy, which needs tuning for each dataset, since too few and too many augmented classes are detrimental to classifier performance. We also show that we can improve results on a large variety of datasets when using convolutional networks as feature extractors while applying output driven entropy regularization instead of a simple weight based L2 norm.
Speech error recognition based on broad learning

Abstract In recent years, with the increasing popularity of Chinese in the world, more foreigners have begun to learn Chinese. In this regard, this paper studies the phonetic errors in a single sentence, and designs a phonetic error classification model based on the broad learning network. First, the Laplace wavelet convolutional layer is designed as the first layer of the traditional CNN, and the Morlet-CNN model is used to extract the speech signal. Then, the extracted speech signal is input into broad learning network for training. Finally, the speech with different error types is tested. By comparing with the experimental results of a single speech amplitude feature and MFCC feature, the classification accuracy of the proposed model has been significantly improved, which verifies the effectiveness of the model.
Media File Descriptor Using Deep Learning

Media files like audios and videos are very useful sources of information, entertainment etc. Humans watch/listen to media files almost everyday for some purpose or another. There are lots of videos,audio and images on every content on the internet and other platforms. Sometimes, just the title can be misleading and humans end up utilizing time on irrelevant media files and often when they realize this isn't the relevant media file they have already wasted some time. Our Media File Descriptor is a generalized platform used to provide a summary of any media file that is audio, video or image quickly so that the user has optimum information of the media file beforehand by reading the summary and can decide if the file is relevant to what they are looking for. If not, the user doesn't end up wasting time on the wrong media file.
Performance Evaluation of a Progression of Recommender System Models

A recommender system is a data filtering system used primarily to endorse items for better sales. There are mainly two approaches: content-based and Collaborative Filtering(CF). Though these are efficient to a certain level, there is a limited scope to expand further. Hence, Deep Neural Networks (DNN) come into play. DNN helps to integrate user information and item features effortlessly, thereby capturing user-specific interests much effectively. In this paper, a Content-based, Item-based, and User-based Collaborative filtering model, a Restrictive Boltzmann Machine model, a CF Keras model, and a Multi-layer Perceptron model are implemented and analyzed under definite parameters using the MovieLens dataset. The root mean square error and time efficiency are analyzed and we effectively prove that deep learning techniques satisfy consumers to a greater extent.
Improving UNIWARD distortion function via isotropic construction and hierarchical merging

Distortion function is designed for evaluating the cost of modifications in adaptive steganography. UNIWARD is a successful and popular distortion scheme which achieves high performance both for spatial and JPEG images. In this paper, we analyze the UNIWARD scheme with some empirical rules of distortion function designation. Based on that we propose our scheme to improve UNIWARD distortion. In our scheme, we focus on the symmetric characteristic of UNIWARD, and suggest that not only use original wavelet filters but also their flippings to calculate sub-models of UNIWARD distortion to maintain its isotropic properties. Moreover, we design several schemes to merge sub-models, which could maintain its invariance regard to flipping or rotation and improve its security against steganalysis detection. Experimental results show our revised UNIWARD achieves better performance for spatial and JPEG image in comparison with original UNIWARD.
Efficient Fault Rules Mining for Multimedia Broadband Services in Power Sensor Network

In order to reduce the fault rate of the broadband multimedia service in the power sensor network and improve the accuracy of network fault diagnosis, an efficient fault association rule mining algorithm is proposed. Aiming at the characteristics of sparse correlation of fault data and large transaction volume, a set intersection strategy based on pruning and cross-counting is designed to realize efficient rule mining and ensure the normal communication of broadband multimedia services on the existing network. In order to verify the effectiveness of the method, a comparative experiment with the original Eclat algorithm is designed. The experimental results show that the improved method has better performance in the face of a variety of data sets with different characteristics.