Reviewerly/exHarmony huggingface dataset:
Challenges and Opportunities for Designing with Biodata as Material

This one-day workshop will bring together researchers working with biodata and design for discussing and reflecting upon how design and HCI operate with the body and its data. The main workshop objective will be to identify core challenges and opportunities in this domain. We intend to cohere the projects of the workshop participants into a set of artifacts, images, and theory to create an annotated portfolio that represents the state-of-the art in designing with biodata in HCI. The workshop will be structured around four themes relating to the use and contexts of working with biodata: 1. Making sense of biodata by taking into account a holistic perspective of body, mind and emotions, as exemplified through soma design methods in HCI, for example; 2. Feminist perspectives of biodata that take into account bodily transitions, pluralities of bodies and temporalities of bodies; 3. Non-anthropocentric perspectives of working with and making sense of biodata, to also account for microorganisms and other material agencies in our bodies; and 4. Emerging domains, contexts, and practices around the use of biodata, such as health, affective, and performance contexts. The significance of the workshop is twofold: One, we will produce a group of researchers and practitioners that are working with biodata, aiming to unpack, discuss and respond to critical issues around designing with and for it. Two, we will create an annotated portfolio that can travel on its own as documentation and vision for future biodata-based design practices.
Knowledge-driven graph similarity for text classification

Abstract Automatic text classification using machine learning is significantly affected by the text representation model. The structural information in text is necessary for natural language understanding, which is usually ignored in vector-based representations. In this paper, we present a graph kernel-based text classification framework which utilises the structural information in text effectively through the weighting and enrichment of a graph-based representation. We introduce weighted co-occurrence graphs to represent text documents, which weight the terms and their dependencies based on their relevance to text classification. We propose a novel method to automatically enrich the weighted graphs using semantic knowledge in the form of a word similarity matrix. The similarity between enriched graphs, knowledge-driven graph similarity , is calculated using a graph kernel. The semantic knowledge in the enriched graphs ensures that the graph kernel goes beyond exact matching of terms and patterns to compute the semantic similarity of documents. In the experiments on sentiment classification and topic classification tasks, our knowledge-driven similarity measure significantly outperforms the baseline text similarity measures on five benchmark text classification datasets.
Compatibility of a Security Policy for a Cloud-Based Healthcare System with the EU General Data Protection Regulation (GDPR)

Currently, there are several challenges that cloud-based healthcare systems around the world are facing. The most important issue is to ensure security and privacy, or in other words, to ensure the confidentiality, integrity, and availability of the data. Although the main provisions for data security and privacy were present in the former legal framework for the protection of personal data, the General Data Protection Regulation (GDPR) introduces new concepts and new requirements. In this paper, we present the main changes and the key challenges of the GDPR and, at the same time, we present how a cloud-based security policy could be modified in order to be compliant with the GDPR, as well as how cloud environments can assist developers to build secure and GDPR compliant cloud-based healthcare systems. The major concept of this paper is dual-purpose; primarily, to facilitate cloud providers in comprehending the framework of the new GDPR and secondly, to identify security measures and security policy rules, for the protection of sensitive data in a cloud-based healthcare system, following our risk-based security policy methodology that assesses the associated security risks and takes into account different requirements from patients, hospitals, and various other professional and organizational actors.
A Systematic Literature Review on Teaching and Learning English Using Mobile Technology

Mobile technology has become increasingly popular in the past decade and provides a chance to move forward towards 21st century education. The art of teaching and learning English with the adoption of mobile technology creates a different learning environment for both teachers and students. Hence, reviewing existing works systematically is crucial in finding the gaps in teaching and learning English using mobile technology. A Systematic Literature Review (SLR) is conducted to identify important factors influenced in the teaching and learning English using mobile technology and existing studies in overcoming the issues. The results show that providing suitable educational technology is one of the key success factors to enhance teaching and learning English. This paper reports the key gap and limitation in current studies that focus on teaching and learning English using mobile technology.
Manipulating the spectral collapse in two-photon Rabi model

We have investigated the eigenenergy spectrum of the two-photon Rabi model with a full quadratic coupling, particularly the special feature "spectral collapse". The critical coupling strength is reduced by half from that of the two-photon Rabi model, implying that the spectral collapse can now occur at a more attainable value of the critical coupling. At the critical coupling some discrete eigenenergy levels still survive below the continuous energy spectrum, i.e. an incomplete spectral collapse, and the set of discrete eigenenergies has a one-to-one mapping with that of a particle of variable effective mass in a finite potential well. Since the energy difference between the two atomic levels specifies the depth of the potential well, the number of bound states available (or the extent of the "spectral collapse") can be straightforwardly monitored. Obviously, this bears a great resemblance to the spectral collapse of the two-photon Rabi model, at least qualitatively. Moreover, since the full quadratic coupling includes an extra term proportional to the photon number operator only, our analysis indicates that one may manipulate the critical coupling of the two-photon Rabi model by incorporating an adjustable proportionality constant to this extra term.
Indian Education: Ancient, Medieval and Modern

Education is a platform in which young generations are trained and make them future-ready. Education provides knowledge and skills which help the person to be employable. The Indian education system is very popular and diversified among other countries’ education systems due to its change in the evolution from ancient to the modern education system. During the ancient and medieval periods of education, students were trained by teachers in such a manner that they can survive and live in that era. After independence, there is a tremendous growth in the Indian education system providing teaching and training in all aspects, but it does not satisfy the global demands of the market. This chapter focuses on teaching methodology, curriculum, characteristics, methods of learning, aims of the Indian education system during the ancient and medieval period and how it differed in today’s modern education and what are the things that our today’s modern education need to learn and implement from ancient and medieval education. The mentioned points are used to differentiate ancient, medieval, and modern education with advantages and disadvantages. Through this chapter, students, teachers will get to know the difference in the education system and what else to be adapted in the future to overcome all the problems.
Speaker-Utterance Dual Attention for Speaker and Utterance Verification

In this paper, we study a novel technique that exploits the interaction between speaker traits and linguistic content to improve both speaker verification and utterance verification performance. We implement an idea of speaker-utterance dual attention (SUDA) in a unified neural network. The dual attention refers to an attention mechanism for the two tasks of speaker and utterance verification. The proposed SUDA features an attention mask mechanism to learn the interaction between the speaker and utterance information streams. This helps to focus only on the required information for respective task by masking the irrelevant counterparts. The studies conducted on RSR2015 corpus confirm that the proposed SUDA outperforms the framework without attention mask as well as several competitive systems for both speaker and utterance verification.
Single image super-resolution via Image Quality Assessment-Guided Deep Learning Network

In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. The performance comparison between our proposed method with recent SISR methods shows that the former achieves a better tradeoff between perceptual quality and distortion measure than the latter. Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model.
SmartOTPs

With the recent rise of cryptocurrencies' popularity, the security and management of crypto-tokens have become critical. We have witnessed many attacks on users and providers, which have resulted in significant financial losses. To remedy these issues, several wallet solutions have been proposed. However, these solutions often lack either essential security features, usability, or do not allow users to customize their spending rules.
Multi-Sensor Fusion Tracking Algorithm Based on Augmented Reality System

With the rapid development of modern science and technology, multi-sensor information fusion target tracking technology has gradually become an increasingly important topic in the field of computer vision. It is not only of great significance in traffic monitoring, unmanned vehicle systems, human-computer interaction, etc., but also has a wide range of applications in target tracking. Aiming at the problem of multi-sensor multi-target tracking, this article proposes a multi-sensor multi-target tracking algorithm based on random finite set. The basic theory of random sets, random finite set statistics and the probability hypothesis density of random sets are introduced. The distribution function of random sets and the derivation process of set derivatives and set integrals are discussed, and the theoretical framework of information fusion based on random sets is given. The single-sensor single-target specification Bayes modeling method is extended to the multi-sensor multi-target situation, and the significance of the probability hypothesis density is explained, and the multi-sensor multi-target tracking is realized by the probability hypothesis density filtering. The simulation experiment results show that the algorithm has a better tracking effect for the maneuvering target in the nonlinear motion model stage, and it is verified that the unscented Kalman filter has smaller filtering error and higher estimation accuracy than the extended Kalman filter.
Learning and Evaluating Representations for Deep One-class Classification

We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. The framework not only allows to learn better representations, but also permits building one-class classifiers that are faithful to the target task. We argue that classifiers inspired by the statistical perspective in generative or discriminative models are more effective than existing approaches, such as a normality score from a surrogate classifier. We thoroughly evaluate different self-supervised representation learning algorithms under the proposed framework for one-class classification. Moreover, we present a novel distribution-augmented contrastive learning that extends training distributions via data augmentation to obstruct the uniformity of contrastive representations. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks, including novelty and anomaly detection. Finally, we present visual explanations, confirming that the decision-making process of deep one-class classifiers is intuitive to humans. The code is available at https://github.com/google-research/deep_representation_one_class.
The Agile Coach Role: Coaching for Agile Performance Impact

It is increasingly common to introduce agile coaches to help gain speed and advantage in agile companies. Following the success of Spotify, the role of the agile coach has branched out in terms of tasks and responsibilities, but little research has been conducted to examine how this role is practiced. This paper examines the role of the agile coach through 19 semistructured interviews with agile coaches from ten different companies. We describe the role in terms of the tasks the coach has in agile projects, valuable traits, skills, tools, and the enablers of agile coaching. Our findings indicate that agile coaches perform at the team and organizational levels. They affect effort, strategies, knowledge, and skills of the agile teams. The most essential traits of an agile coach are being emphatic, people-oriented, able to listen, diplomatic, and persistent. We suggest empirically based advice for agile coaching, for example companies giving their agile coaches the authority to implement the required organizational changes within and outside the teams.
Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity

This paper revisits the receptive theory in the context of computational creativity. It presents a case study of a Paranoid Transformer—a fully autonomous text generation engine with raw output that could be read as the narrative of a mad digital persona without any additional human post-filtering. We describe technical details of the generative system, provide examples of output, and discuss the impact of receptive theory, chance discovery, and simulation of fringe mental state on the understanding of computational creativity.
A Comprehensive Study of Blockchain Services: Future of Cryptography

Cryptography is the process of protecting information from intruders and letting only the intended users' access and understand it.It is a technique originated in 2000 BC where simple methods were used in earlier times to keep the information in a way that is not understandable by everyone.Only the intended receiver knows how to decode the information.Later, as technology advances, many sophisticated techniques were used to protect the message so that no intrusion can invade the information.Many mathematically complex algorithms like AES, RSA are used to encrypt and decrypt the data.Due to the advancements in the computer science field, recently, cryptography is used in the development of cryptographic currencies of cryptocurrencies.Blockchain technology, a distributed ledger technology identified to be the foundation of Bitcoin cryptocurrency, implements a high-level cryptographic technique like public-key cryptography, Hash Functions, Merkle Trees, Digital signatures like Elliptic curve digital signatures, etc.These advanced cryptographic techniques are used to provide security to blockchain data and for the secure transmission of information, thereby making Blockchain more popular and demandable.Blockchain applies cryptography in various phases, and some of the techniques used in Blockchain are advanced in cryptographic sciences.This paper intends to provide a brief introduction to cryptography and Blockchain Technology and discusses how both technologies can be integrated to provide the best of the security to the data.This paper reviews the various cryptographic attacks in Blockchain and the various security services offered in Blockchain.The challenges of blockchain security are also analyzed and presented briefly in this paper.
Visual Analytics of Air Pollution Propagation Through Dynamic Network Analysis

The propagation of pollutants between regions has become a noticeable factor affecting air pollution. Given the complicated propagation relationship, most of the existing works lack an effective perception mechanism of geographic correlations and time-varying features, which is crucial in exploring and understanding the propagation mechanism by integrating empirical knowledge and data inherent characteristics. In this paper, we abstract the complicated propagation relationship between regions as a dynamic network, and introduce visual analytics techniques to explore the spatiotemporal multivariate patterns of air pollution propagation. A particle tracking based model is first proposed to construct pollution propagation networks under multi-source factors. It combines numerical simulation and data characteristics simultaneously, and detects active pollution source areas based on long-term transport relationships and temporal correlations. Based on it, we extract propagation patterns and analyze the temporal evolution of diachronic propagation networks. Moreover, we design an interactive system to achieve an in-depth analysis of air pollution issues. Through elaborate multi-level glyphs and linkage views, the system facilitates users to perceive and explore propagation mechanism in spatiotemporal multivariate information, and compare propagation patterns from global and local perspectives. We present several case studies to demonstrate the usefulness of our work in air pollution propagation analysis.
Foveated Image Processing for Faster Object Detection and Recognition in Embedded Systems Using Deep Convolutional Neural Networks

Object detection and recognition algorithms using deep convolutional neural networks (CNNs) tend to be computationally intensive to implement. This presents a particular challenge for embedded systems, such as mobile robots, where the computational resources tend to be far less than for workstations. As an alternative to standard, uniformly sampled images, we propose the use of foveated image sampling here to reduce the size of images, which are faster to process in a CNN due to the reduced number of convolution operations. We evaluate object detection and recognition on the Microsoft COCO database, using foveated image sampling at different image sizes, ranging from 416x416 to 96x96 pixels, on an embedded GPU -- an NVIDIA Jetson TX2 with 256 CUDA cores. The results show that it is possible to achieve a 4x speed-up in frame rates, from 3.59 FPS to 15.24 FPS, using 416x416 and 128x128 pixel images respectively. For foveated sampling, this image size reduction led to just a small decrease in recall performance in the foveal region, to 92.0% of the baseline performance with full-sized images, compared to a significant decrease to 50.1% of baseline recall performance in uniformly sampled images, demonstrating the advantage of foveated sampling.
Taking Linear Logic Apart

Process calculi based on logic, such as πDILL and CP, provide a foundation for deadlock-free concurrent programming.However, in previous work, there is a mismatch between the rules for constructing proofs and the term constructors of the π-calculus: the fundamental operator for parallel composition does not correspond to any rule of linear logic.Kokke et al. [12] introduced Hypersequent Classical Processes (HCP), which addresses this mismatch using hypersequents (collections of sequents) to register parallelism in the typing judgements.However, the step from CP to HCP is a big one.As of yet, HCP does not have reduction semantics, and the addition of delayed actions means that CP processes interpreted as HCP processes do not behave as they would in CP.We introduce HCP -, a variant of HCP with reduction semantics and without delayed actions.We prove progress, preservation, and termination, and show that HCP -supports the same communication protocols as CP.
Squares of matrix-product codes

The component-wise or Schur product C⁎C′ of two linear error-correcting codes C and C′ over certain finite field is the linear code spanned by all component-wise products of a codeword in C with a codeword in C′. When C=C′, we call the product the square of C and denote it C⁎2. Motivated by several applications of squares of linear codes in the area of cryptography, in this paper we study squares of so-called matrix-product codes, a general construction that allows to obtain new longer codes from several “constituent” codes. We show that in many cases we can relate the square of a matrix-product code to the squares and products of their constituent codes, which allow us to give bounds or even determine its minimum distance. We consider the well-known (u,u+v)-construction, or Plotkin sum (which is a special case of a matrix-product code) and determine which parameters we can obtain when the constituent codes are certain cyclic codes. In addition, we use the same techniques to study the squares of other matrix-product codes, for example when the defining matrix is Vandermonde (where the minimum distance is in a certain sense maximal with respect to matrix-product codes).
Retouchdown: Releasing Touchdown on StreetLearn as a Public Resource for Language Grounding Tasks in Street View

The Touchdown dataset (Chen et al., 2019) provides instructions by human annotators for navigation through New York City streets and for resolving spatial descriptions at a given location. To enable the wider research community to work effectively with the Touchdown tasks, we are publicly releasing the 29k raw Street View panoramas needed for Touchdown. We follow the process used for the StreetLearn data release (Mirowski et al., 2019) to check panoramas for personally identifiable information and blur them as necessary. These have been added to the StreetLearn dataset and can be obtained via the same process as used previously for StreetLearn. We also provide a reference implementation for both Touchdown tasks: vision and language navigation (VLN) and spatial description resolution (SDR). We compare our model results to those given in (Chen et al., 2019) and show that the panoramas we have added to StreetLearn support both Touchdown tasks and can be used effectively for further research and comparison.