Reviewerly/exHarmony huggingface dataset:
Efficient Face Manipulation Via Deep Feature Disentanglement And Reintegration Net

Deep neural networks (DNNs) have been widely used in facial manipulation. Existing methods focus on training deeper networks in indirect supervision ways (e.g., feature constraint), or in unsupervised ways (e.g., cycle-consistency loss) due to the lack of ground-truth face images for manipulated outputs. However, such methods can not synthesize realistic face images well and suffer from very high training overhead. To address this issue, we propose a novel Feature Disentanglement and Reintegraion network (FDRNet), which employs ground-truth images as informative supervision and dynamically adapts the fusion of informative features of the ground-truth images effectively and efficiently. FDRNet consists of a Feature Disentanglement (FD) Network and a Feature Reintegration (FR) Network, which encodes informative disentangled representations from the ground-truth images and fuses the disentangled representations to reconstruct the face images. By learning disentangled representations, our method can generate plausible faces conditioned on both landmarks and identities, which can be used for a variety of face manipulation tasks. Experiments on the CelebA-HQ and FFHQ datasets are conducted to demonstrate the superiority of our method over state-of-the-art methods in terms of effectiveness and efficiency.
OUP accepted manuscript

Abstract The use of digital technologies and big data in the humanities and social sciences provided many opportunities for cultural heritage management and research, enabling data sharing and interdisciplinary collaborations. These developments increased the need for standardized data formats. General and domain-specific standards for describing and classifying cultural data, based on linked data principles, are developed to support increasingly numerous digital collections. However, the existing standards do not fully address the particular challenges concerning the standardized descriptions of images. Here we focus on ancient coins, an official image-bearing medium. We present current approaches to coin iconography, including the application of statistical measures to infer patterns in the use of images for communication. We discuss the importance of consistent, standardized data for quantitative research, and propose a generalized approach, focused on basic concepts and limiting the level of detail for the sake of simplicity, interoperability, and compatibility with statistical methods, as a necessary first step towards creating reliable iconographic standards.
One-shot Representational Learning for Joint Biometric and Device Authentication

In this work, we propose a method to simultaneously perform (i) biometric recognition (i.e., identify the individual), and (ii) device recognition, (i.e., identify the device) from a single biometric image, say, a face image, using a one-shot schema. Such a joint recognition scheme can be useful in devices such as smartphones for enhancing security as well as privacy. We propose to automatically learn a joint representation that encapsulates both biometric-specific and sensor-specific features. We evaluate the proposed approach using iris, face and periocular images acquired using near-infrared iris sensors and smartphone cameras. Experiments conducted using 14,451 images from 13 sensors resulted in a rank-1 identification accuracy of upto 99.81% and a verification accuracy of upto 100% at a false match rate of 1%.
Attention Stereo Matching Network

Despite great progress, previous stereo matching algorithms still lack the ability to match textureless regions and slender structure areas. To tackle this problem, we propose ASM-Net, an attention stereo matching network. Attention module and disparity refinement module are constructed in the ASMNet. The attention module can improve correlation information between two images by channels and spatial attention. The feature-guided disparity refinement module learns more geometry information in different feature levels to refine the coarse prediction resolution constantly. The proposed approach was evaluated on several benchmark datasets. Experiments show that the proposed method achieves competitive results on KITTI and Scene-Flow datasets while running in real-time at 14ms.
Analysing Bias in Spoken Language Assessment Using Concept Activation Vectors

A significant concern with deep learning based approaches is that they are difficult to interpret, which means detecting bias in network predictions can be challenging. Concept Activation Vectors (CAVs) have been proposed to address this problem. These use representations - perturbations of activation function outputs - of interpretable concepts to analyse how the network is influenced by the concept. This work applies CAVs to assess bias in a spoken language assessment (SLA) system, a regression task. One of the challenges with SLA is the wide range of concepts that can introduce bias in training data, for example L1, age, acoustic conditions, and particular human graders, or the grading instructions. Simply generating large quantities of expert marked data to check for all forms of bias is impractical. This paper uses CAVs applied to the training data to identify concepts that might be of concern, allowing a more targeted dataset to be collected to assess bias. The ability of CAVs to detect bias is assessed on the BULATS speaking test using both a standard system and a system to which bias was artificially introduced. A strong bias identified by CAVs on the training data matches the bias observed in expert marked held-out test data.
Global and Local Structure Preservation for Nonlinear High-dimensional Spectral Clustering

Abstract Spectral clustering is widely applied in real applications, as it utilizes a graph matrix to consider the similarity relationship of subjects. The quality of graph structure is usually important to the robustness of the clustering task. However, existing spectral clustering methods consider either the local structure or the global structure, which can not provide comprehensive information for clustering tasks. Moreover, previous clustering methods only consider the simple similarity relationship, which may not output the optimal clustering performance. To solve these problems, we propose a novel clustering method considering both the local structure and the global structure for conducting nonlinear clustering. Specifically, our proposed method simultaneously considers (i) preserving the local structure and the global structure of subjects to provide comprehensive information for clustering tasks, (ii) exploring the nonlinear similarity relationship to capture the complex and inherent correlation of subjects and (iii) embedding dimensionality reduction techniques and a low-rank constraint in the framework of adaptive graph learning to reduce clustering biases. These constraints are considered in a unified optimization framework to result in one-step clustering. Experimental results on real data sets demonstrate that our method achieved competitive clustering performance in comparison with state-of-the-art clustering methods.
The eXPose Approach to Crosslier Detection

Transit of wasteful materials within the European Union is highly regulated through a system of permits. Waste processing costs vary greatly depending on the waste category of a permit. Therefore, companies may have a financial incentive to allege transporting waste with erroneous categorisation. Our goal is to assist inspectors in selecting potentially manipulated permits for further investigation, making their task more effective and efficient. Due to data limitations, a supervised learning approach based on historical cases is not possible. Standard unsupervised approaches, such as outlier detection and data quality-assurance techniques, are not suited since we are interested in targeting non-random modifications in both category and category-correlated features. For this purpose we (1) introduce the concept of crosslier: an anomalous instance of a category which lies across other categories; (2) propose eXPose: a novel approach to crosslier detection based on supervised category modelling; and (3) present the crosslier diagram: a visualisation tool specifically designed for domain experts to easily assess crossliers. We compare eXPose against traditional outlier detection methods in various benchmark datasets with synthetic crossliers and show the superior performance of our method in targeting these instances.
A Hardware-Oriented Image Compression Algorithm Based on BTC and YEF Color Space

This paper proposes a new compression algorithm based on color sampling and Block Truncation Coding (BTC). It is composed of several steps, color sampling, BTC parameter training, threshold selection, sub-sampling, prediction and quantization, and Huffman coding. The color sampling was applied using YEF color space which reduced 34% storage. The subsequent sub-sampling, quantization table prediction, and Huffman coding further increase compression rates. In addition, the BTC parameter training step finds the best reconstruction value for each 4*4 block and bitmap threshold value to improve the image quality. Compared with previously studies, the proposed algorithm shows a better average compression rate.
Compare Between Histogram Similarity and Histogram Differencing For More Brief Key Frames Extraction from Video Stream

Abstract Speedy increase in the employ of the content of multimedia is very commonly perceived in existing generations. Video is one of the extreme exceedingly recovered data of multimedia which can provide quick fixes and immediate gratification for whole the kinds of user information willingness. Detection of Key frame from Videos and classification of videos are the necessary protocols in the complements tasks of video retrieval. The aim of this research is to compare between histogram similarity and histogram differencing for more brief key frames extraction from video stream. The suggested system for key frame extraction has three steps. Firstly, the video frames series will take and the characteristics for points of interest are elicited utilizing SUSAN detector. After eliciting interest characteristics from all video frames images, secondly, K-Means clustering technique was utilized for these features to construct the clusters with a number of interest points. Thirdly, a histogram builds for each video frame based on numbers of features in each cluster. X-axis of a histogram represents the cluster number and y-axis represents the number of features in each cluster. For key frames extraction, numbers for each cluster can be offered and a query histogram can be constructed based on entered clusters’ numbers. A query histogram was matched with every video frame histogram using Manhattan distance to discover the similar histogram to query histogram. After chosen similar histogram, it can extract all key frames from that video frames. The experimental results show that the number of key frames extracted from the video is very brief when using the histogram similarity compared to the histogram differencing. For example, the number of key frames extracted from a car video is 70 when using the histogram similarity, while it is 180 when using the histogram differencing.
Comparative Studies on Inclusive Digital Teaching in Higher Education in Light of COVID-19

The COVID-19 pandemic has disrupted the higher education sector, which is a critical determinant of a country's economic future. The aim is to compare the views of teachers and students on inclusive digital teaching in higher education during the COVID-19 pandemic. The meaning of “inclusive digital teaching” and “view” are studied. The empirical study was carried out in September-November 2020. Forty respondents from Germany, Latvia, India, Ukraine, and South Africa participated in the study. The data were collected via a semi-structural interview. The theoretical findings allow defining inclusive digital teaching and its criteria and indicators. The positive views on inclusive digital teaching in higher education during the COVID-19 pandemic were expressed by the university teachers. The students' views were more negative. The novel contribution of this chapter is the implications on inclusive digital teaching in higher education. Directions of further research are formulated.
WTA: Towards a Web-Based Testbed Architecture

Tests, evaluations, and solution comparisons in complex use cases are often realized by creating a testbed for a domain of use cases and solutions. Web-based testbeds add key advantages like results sharing, remote test execution, and collaboration. Focusing on their research objectives, creators see their testbeds as a means to that end. The resulting web-based testbeds are similar in structure and functionality, but there is no common architecture supporting their creation, introducing redundant design efforts. Therefore, we determine structural similarities based on insights into the architecture of current web-based testbeds, from which we derive a generic web-based testbed architecture. This framework of reference will help to develop future testbeds focusing on the testbed domain instead of reinventing general testbed functionality.
Disocclusion-Reducing Geometry for Multiple RGB-D Video Streams

Depth-image-based rendering (DIBR) is a key method for synthesizing virtual views from multiple RGB-D video streams. A challenging issue inherent in this approach is the disocclusion that occurs as the virtual viewpoint moves away from a reference view. In this work, we present a technique for extracting 3D geometric data, called the disocclusion-reducing geometry, from the input video streams. This auxiliary information, represented as a 3D point cloud, can then be combined easily with a conventional DIBR pipeline to reduce the disoccluded region as much as possible during the view warping process, eventually decreasing the visual artifacts by the subsequent hole-filling process.
From letters to families

This study analyzes a database of initialized signs, which are formed with handshapes corresponding to English letters, collected from an ASL dictionary. ASL handshapes are classified as either primarily used for initialization, such as the R handshape, or rarely used for initialization, such as the X handshape. Additionally, initialized signs are shown to forge kind-of and whole-part relationships with existing ASL signs. An example is biology, which derives from ASL science, reflecting that ‘biology is a kind of science’. These facts about initialized signs are analyzed in terms of constructional schemas, capturing properties that are shared among ASL signs and explaining productive instances of initialization (and de-initialization) in ASL.
Enriching the food experience: A design journey through innovative technologies for creating, experimenting, consuming, socializing, and playing with food

This chapter is a design journey on the food experience enriched by emerging digital technologies. The topic is explored though the lens of five themes focused on creating, experimenting, consuming, socializing, and playing with food. Different cases are described such as the use of 3D printing to create novel recipes and forms of food; cross-modal stimulations to transform the human perception of food; design of environmentally friendly solutions to protect, monitor, and consume food and dispose its packaging; multisensory augmentation of eating; and the use of social media and big data to share information and exalt the hedonic qualities of food. The exploration of these themes sets the scene for possible future developments in the field and advocates for a fundamental role of design in providing a human-centered approach in the envisioning of future scenarios exploiting the multisensory, social, aesthetic, and playful facets of food.
Kidney Lesion Segmentation in MRI Using Clustering with Salp Swarm Algorithm

In this paper, kidney lesion segmentation in MRI using clustering with salp swarm algorithm (SSA) is proposed. The segmentation results of kidney MRI are degraded by the noise and intensity inhomogeneities (IIHs) in MR images. Therefore, at the outset, the MR images are denoised using median filter. Then IIHs are corrected using the max filter-based method. A hard-clustering technique using SSA is developed to segment the MR images. Finally, the lesions are extracted from the segmented MR images. The proposed method is compared with the K-means algorithm using well-known clustering validity measure DB-index. The experimental results demonstrate that the proposed method performs better than the K-means algorithm in the segmentation of kidney lesions in MRI.
Automatic Text Summarization for Hadith with Indonesian Text using Bellman-Ford Algorithm

Automatic text summarization is one of Natural Language Processing technology to create a summary automatically by not changing the core or main idea of a summarized document. According to the agreement of the majority of Muslim scholars, Hadith is a second source of Muslim's life guideline. This study aims to extract the main meaning of Hadith document using automatic text summarization. The method used in this study is Bellman-Ford Algorithm from graph theory to extract and score the sentence based on the closeness and interconnection between sentences. The Hadith summary which resulted from Bellman-Ford algorithm is evaluated using Recall-Oriented Understudy for Gisting Evaluation - Longest Common Subsequence (ROUGE-L) metrics. Based on experiment using 20 Hadith documents with Indonesian text and ROUGE-L metrics evaluation, the summary results from sentence extraction are influenced by the type of similarity formula between sentences. The result shows that an average value of precision is 46.5%, recall value is 56% recall and f-score 49.5%. The evaluation result is not good enough because the summary result is extraction summary, while the summary evaluation database is abstraction summary. However, from human evaluation, this research contributes to understand the contents of the Hadith with a shorter text but does not eliminate the essence of the Hadith itself.
The Upper Confidence Bound Algorithm

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More: A Metric Learning Based Framework for Open-Domain Relation Extraction

Open relation extraction (OpenRE) is the task of extracting relation schemes from open-domain corpora. Most existing OpenRE methods either do not fully benefit from high-quality labeled corpora or can not learn semantic representation directly, affecting downstream clustering efficiency. To address these problems, in this work, we propose a novel learning framework named MORE (Metric learning-based Open Relation Extraction). The framework utilizes deep metric learning to obtain rich supervision signals from labeled data and drive the neural model to learn semantic relational representation directly. Experiments result in two real-world datasets show that our method outperforms other state-of-the-art baselines. Our source code is available on Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Reflections About Non-knowledge in Planning Processes

During a planning process for strategic anthropic decisions that will invest a territory, or an environmental system, or a city organization, we deal with many data, many results of very different analyses. In an abstract way we could represent this huge amount of results and data as the set of what we know, but beside this set at least an equivalent set exists about what we don’t know. What we don’t know exists in the precise time we are deciding. It is something that could be extremely useful to catch instead, so to avoid wrong decisions when designing a hypothetical plan or strategic document. Therefore, we deal with what we know, with our knowledge: yet, invisibly, we also deal with our non-knowledge. Here we intend non-knowledge as a further step beyond uncertainty, as already studied in literature. In fact such non-knowledge set is not something ambiguous occurring when coping with in ill structured hypotheses to include what is uncertain. Indeed, it is something surely we do not know, as occurring e.g. when dealing with something we do not imagine that our decisions may trigger. How to define the set of what is not known? This is a position paper, and the objective of this reflection is to envision possible modeling perspectives able to simulate the activation of invisible relations that exist among the two sets. They are things we don’t know but that are unpredictably linked to things we know. A very first draft of a neural-ontological approach is proposed as possible method to cope this issue.
Powering TV Experiences with Anytime Environmental Exploration

Our work proposes a novel interactive model for TV such that users can freely decide when and how much to interact with the story. At any time, users can enter the immersive 3D environment of each scene in the story by controlling an avatar. In this scene, users embrace a rich set of interactive possibilities, including in-depth exploration, conversations with characters, and gamified quests to guide the story. Users also have the option to watch their avatar explore the 3D environment automatically for a lean-back experience. User evaluation of our prototype confirmed the acceptance of this new model and enthusiasm for the interactive freedom and depth it provides. To the best of our knowledge, this is the world's first lean-back-compatible TV model that offers a complete immersive experience driven by users, shedding light on a new direction for designing interactive videos appealing to a wide audience.
From Fiat to Crypto: The Present and Future of Money

Abstract This chapter aims to offer readers an entry point to the deep discussion of this volume and the rationale for the “Finance 4.0” system described in later chapters. What is money, why is it designed this way, and what could it become in the crypto age? The chapter contains three parts. The first part describes in rough strokes the basic functions of money and how today’s fiat money system implements them. The second part offers a modest critique of the fiat money system, arguing that many problems take root in the intimate power relationship between “money and state.” The final part presents two cases that address some of the shortcomings. The first is Bitcoin that infamously pursues a state-independent, decentralized conception of money. The second is Finance 4.0, a system that proposes a participatory multi-dimensional money system with built-in incentives for sustainable behavior. If more readers feel empowered to enter the public debate for a better money system in the twenty-first century, this short introduction achieved its aim.
Exploration on the First-class Courses Construction of Microcomputer Principles and Single Chip Microcomputer Interface Technology

Aiming at construction of First-class curriculum, the courses “Microcomputer Principle and Interface Technology” and “Single-chip Microcomputer” are combined into one course according to there are similar instruction architecture. Two main section include into this combined course, microcomputer principle and interface technology. In the part of microcomputer principle focuses on the intel 80X86 processor architecture and instruction set. In the part of interface technology focuses on intel 8051 interface circuit and C language programming. The related experimental topics are also increased to provide more practical opportunities for students. The start time of this combined course is moved up to spring semester in second year. With these reforms above made this course to provide beneficial support for innovative practice of students. After more than three years of practice, the ability for students to use microcomputers and practical innovation have been greatly improved, which is reflected in the fact that students have achieved very good results in various innovation competitions.