Reviewerly/exHarmony huggingface dataset:
A Paradox Lens to Systems Development Projects: The Case of the Agile Software Development

Research into organizations has concluded that organizational effectiveness is paradoxical (i.e., effective organizations must have simultaneously contradictory, even mutually exclusive, attributes). Although systems development projects constitute temporary organizations, researchers have largely omitted the paradox lens in their context. In this paper, I move toward rectifying the situation by focusing specifically on the agile software development (ASD) as a timely systems development approach in practice. I identify 11 interrelated and actable paradoxical tensions concerning the priority, structure, and execution of systems development projects. Each tension imposes competing demands on projects. To address them requires human ingenuity and judgement, though systems development methods and approaches can provide aid. I show that ASD comprises mechanisms for that purpose largely due to the reflective nature of the ASD process in which each retrospective assesses what went well in the previous sprint (iteration) and what could be improved in the next sprint. At the same time, ASD has built-in flexibility that makes it possible to adapt the method-in-use when deemed necessary or reasonable.
Adaptor Grammars for Unsupervised Paradigm Clustering

This work describes the Edinburgh submission to the SIGMORPHON 2021 Shared Task 2 on unsupervised morphological paradigm clustering.Given raw text input, the task was to assign each token to a cluster with other tokens from the same paradigm.We use Adaptor Grammar segmentations combined with frequency-based heuristics to predict paradigm clusters.Our system achieved the highest average F1 score across 9 test languages, placing first out of 15 submissions.
Reinforcement Learning as One Big Sequence Modeling Problem

Reinforcement learning (RL) is typically concerned with estimating single-step policies or single-step models, leveraging the Markov property to factorize the problem in time. However, we can also view RL as a sequence problem, with the goal being to predict a sequence of actions that leads to a sequence of high rewards. Viewed in this way, it is tempting to consider whether powerful, high-capacity sequence prediction models that work well in other domains, such as natural-language processing, can also provide simple and effective solutions to the RL problem. To this end, we explore how RL can be reframed as one big sequence modeling problem, using state-of-the-art Transformer architectures to model distributions over sequences of states, actions, and rewards. Addressing RL as a sequence problem significantly simplifies a range of design decisions: we no longer require separate behavior policy constraints, as is common in prior work on offline model-free RL, and we no longer require ensembles or other epistemic uncertainty estimators, as is common in prior work on model-based RL. All of these roles are filled by the same Transformer sequence model. In our experiments, we demonstrate the flexibility of this approach across long-horizon dynamics prediction, imitation learning, goal-conditioned RL, and offline RL.
The People’s Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage

The People's Speech is a free-to-download 30,000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset). The data is collected via searching the Internet for appropriately licensed audio data with existing transcriptions. We describe our data collection methodology and release our data collection system under the Apache 2.0 license. We show that a model trained on this dataset achieves a 9.98% word error rate on Librispeech's test-clean test set.Finally, we discuss the legal and ethical issues surrounding the creation of a sizable machine learning corpora and plans for continued maintenance of the project under MLCommons's sponsorship.
Data Visualization and Analysis of Playing Styles in Tennis

Sports data analysis and visualization are useful for gaining insights into the games. In this paper, we present a new visual analytics technique called Tennis Fingerprinting to analyze tennis players’ tactical patterns and styles of play. Tennis is a complicated game, with a variety of styles, tactics, and strategies. Tennis experts and fans are often interested in discussing and analyzing tennis players’ different styles. In tennis, style is a complicated and often abstract concept that cannot be easily described or analyzed. The proposed visualization method is an attempt to provide a concrete and visual representation of a tennis player’s style. We demonstrate the usefulness of our method by analyzing matches played by Roger Federer and Rafael Nadal at Wimbledon, Roland Garros, and Australian Open. Although we focus on tennis data analysis and visualization in this paper, this idea can be extended to the analysis of other competitive sports, including E-sports.
TLA Twitter Linguistic Analysis

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Learning Binary Hash Codes Based on Adaptable Label Representations

The goal of supervised hashing is to construct hash mappings from collections of images and semantic annotations such that semantically relevant images are embedded nearby in the learned binary hash representations. Existing deep supervised hashing approaches that employ classification frameworks with a classification training objective for learning hash codes often encode class labels as one-hot or multi-hot vectors. We argue that such label encodings do not well reflect semantic relations among classes and instead, effective class label representations ought to be learned from data, which could provide more discriminative signals for hashing. In this article, we introduce Adaptive Labeling Deep Hashing (AdaLabelHash) that learns binary hash codes based on learnable class label representations. We treat the class labels as the vertices of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -dimensional hypercube, which are trainable variables and adapted together with network weights during the backward network training procedure. The label representations, referred to as codewords, are the target outputs of hash mapping learning. In the label space, semantically relevant images are then expressed by the codewords that are nearby regarding Hamming distances, yielding compact and discriminative binary hash representations. Furthermore, we find that the learned label representations well reflect semantic relations. Our approach is easy to realize and can simultaneously construct both the label representations and the compact binary embeddings. Quantitative and qualitative evaluations on several popular benchmarks validate the superiority of AdaLabelHash in learning effective binary codes for image search.
Applying Occam’s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn’t, and What is Really Necessary

The introduction of pre-trained transformerbased contextualized word embeddings has led to considerable improvements in the accuracy of graph-based parsers for frameworks such as Universal Dependencies (UD).However, previous works differ in various dimensions, including their choice of pre-trained language models and whether they use LSTM layers.With the aims of disentangling the effects of these choices and identifying a simple yet widely applicable architecture, we introduce STEPS, a new modular graph-based dependency parser.Using STEPS, we perform a series of analyses on the UD corpora of a diverse set of languages.We find that the choice of pre-trained embeddings has by far the greatest impact on parser performance and identify XLM-R as a robust choice across the languages in our study.Adding LSTM layers provides no benefits when using transformerbased embeddings.A multi-task training setup outputting additional UD features may contort results.Taking these insights together, we propose a simple but widely applicable parser architecture and configuration, achieving new state-of-the-art results (in terms of LAS) for 10 out of 12 diverse languages. 1
Spatial position constraint for unsupervised learning of speech representations

The success of supervised learning techniques for automatic speech processing does not always extend to problems with limited annotated speech. Unsupervised representation learning aims at utilizing unlabelled data to learn a transformation that makes speech easily distinguishable for classification tasks, whereby deep auto-encoder variants have been most successful in finding such representations. This paper proposes a novel mechanism to incorporate geometric position of speech samples within the global structure of an unlabelled feature set. Regression to the geometric position is also added as an additional constraint for the representation learning auto-encoder. The representation learnt by the proposed model has been evaluated over a supervised classification task for limited vocabulary keyword spotting, with the proposed representation outperforming the commonly used cepstral features by about 9% in terms of classification accuracy, despite using a limited amount of labels during supervision. Furthermore, a small keyword dataset has been collected for Kadazan, an indigenous, low-resourced Southeast Asian language. Analysis for the Kadazan dataset also confirms the superiority of the proposed representation for limited annotation. The results are significant as they confirm that the proposed method can learn unsupervised speech representations effectively for classification tasks with scarce labelled data.
MONROE: Measuring Mobile Broadband Networks in Europe

Mobile broadband (MBB) networks (e.g., 3G/4G) underpin numerous vital operations of the society and are arguably becoming the most important piece of the communications infrastructure.Given the importance of MBB networks, there is a strong need for objective information about their performance, particularly, the quality experienced by the end user.Such information is valuable to operators, regulators and policy makers, consumers and society at large, businesses whose services depend on MBB networks, researchers and innovators.In this chapter, we introduce the MONROE 1 measurement platform:An open access, European-scale, and flexible hardware-based platform for measurements and custom experimentation on operational MBB networks with WiFi connectivity.The platform consists of mobile and stationary nodes that are flexible and powerful enough to run most measurement and experiments tasks, including demanding applications like adaptive video streaming.Access to such a platform enables accurate, realistic and meaningful assessment of the performance of MBB networks by continuously monitoring these networks via active testing (e.g., delay test, web performance test, download speed test) and context metadata collection (e.g., connection mode, signal strength parameters).The multihoming feature of MONROE allows for the comparison of different networks under similar conditions as well as the exploration of new ways of aggregating providers to increase performance and robustness.In this chapter, we showcase the monitoring capabilities of the platform by analyzing preliminary performance measurement results.Considering that MONROE is open to external users, we further discuss a representative set of measurements and experiments to highlight the potential use cases of the platform.We argue that mobile measurements over operational networks, hence platforms such as MONROE, are crucial not only for characterizing and improving the user experience for services that are running on the current 3G/4G infrastructure, but also for providing feedback on the design of upcoming 5G technologies.
Gaze-aware displays and interaction

Being able to detect and to employ gaze enhances digital displays. Research on gaze-contingent or gaze-aware display devices dates back two decades. This is the time, though, that it could truly be employed for fast, low-latency gaze-based interaction and for optimization of computer graphics rendering such as in foveated rendering. Moreover, Virtual Reality (VR) is becoming ubiquitous. The widespread availability of consumer grade VR Head Mounted Displays (HMDs) transformed VR to a commodity available for everyday use. VR applications are now abundantly designed for recreation, work and communication. However, interacting with VR setups requires new paradigms of User Interfaces (UIs), since traditional 2D UIs are designed to be viewed from a static vantage point only, e.g. the computer screen. Adding to this, traditional input methods such as the keyboard and mouse are hard to manipulate when the user wears a HMD. Recently, companies such as HTC announced embedded eye-tracking in their headsets and therefore, novel, immersive 3D UI paradigms embedded in a VR setup can now be controlled via eye gaze. Gaze-based interaction is intuitive and natural the users. Tasks can be performed directly into the 3D spatial context without having to search for an out-of-view keyboard/mouse. Furthermore, people with physical disabilities, already depending on technology for recreation and basic communication, can now benefit even more from VR. This course presents timely, relevant information on how gaze-contingent displays, in general, including the recent advances of Virtual Reality (VR) eye tracking capabilities can leverage eye-tracking data to optimize the user experience and to alleviate usability issues surrounding intuitive interaction challenges. Research topics to be covered include saliency models, gaze prediction, gaze tracking, gaze direction, foveated rendering, stereo grading and 3D User Interfaces (UIs) based on gaze on any gaze-aware display technology.
Distribution-free uncertainty quantification for classification under label shift

Trustworthy deployment of ML models requires a proper measure of uncertainty, especially in safety-critical applications. We focus on uncertainty quantification (UQ) for classification problems via two avenues --- prediction sets using conformal prediction and calibration of probabilistic predictors by post-hoc binning --- since these possess distribution-free guarantees for i.i.d. data. Two common ways of generalizing beyond the i.i.d. setting include handling covariate and label shift. Within the context of distribution-free UQ, the former has already received attention, but not the latter. It is known that label shift hurts prediction, and we first argue that it also hurts UQ, by showing degradation in coverage and calibration. Piggybacking on recent progress in addressing label shift (for better prediction), we examine the right way to achieve UQ by reweighting the aforementioned conformal and calibration procedures whenever some unlabeled data from the target distribution is available. We examine these techniques theoretically in a distribution-free framework and demonstrate their excellent practical performance.
Blockchain based EHR system architecture and the need of blockchain inhealthcare

Purchases are concurrently safe and secure as well as credible because of making use of cryptographic guidelines. Recently, blockchain technology has become extremely cool and trendy and infiltrated various domain names, usually as a result of the appeal of cryptocurrencies. One field where blockchain technology possesses significant ability is medical care, because of the requirement for an extra patient-centric method to medical care systems and to link inconsonant devices and also boost the reliability of digital medical care files (EHRs). In this organized review, an analysis of cutting edge blockchain research study in the field of health care is carried out. It's own' prospective in the multi-stakeholder operated market like health and wellness has been responsible for several expenditures, researches, and executions. Electronic Health Records (EHR) devices typically made use of for the swap of wellness details amongst healthcare stakeholders have been criticised for centralising energy, failures and attack-points along with exchange records protectors. EHRs have battled when faced with multi-stakeholder as well as unit criteria while adhering to safety and security, privacy, reliable as well as other regulative constraints. Blockchain is promising among others to attend to the many EHR challenges, largely trustless and also protected swap of health-relevant information among stakeholders. This paper provides the demand for blockchain in medical care and also blockchain-based EHR device architecture.
Migrant Inventors as Agents of Technological Change

How do regions enter new and distant technological fields? Who is triggering this process? This work addresses these compelling research questions by investigating the role of migrant inventors in the process of technological diversification. Immigrant inventors can indeed act as carriers of knowledge across borders and influence the direction of technological change. We test these latter propositions by using an original dataset of immigrant inventors in the context of European regions during the period 2003–201. Our findings show that: immigrant inventors generate positive local knowledge spillovers; they help their host regions to develop new technological specialisations; they trigger a process of unrelated diversification. Their contribution comes via two main mechanisms: immigrant inventors use their own personal knowledge (knowledge creation); they import knowledge from their home country to the host region (knowledge transfer). Their impact is maximised when their knowledge is not recombined with the local one (in mixed teams of inventors), but it is reused (in teams made by only migrant inventors). Our work contributes to the existing literature of regional diversification by providing fresh evidence of unrelated diversification for European regions and by identifying important agents of structural change. It also contributes to the literature of migration and innovation by adding fresh evidence on European regions and by unveiling some of the mechanisms of immigrants’ knowledge transmission.
An Augmented-Reality Based Human-Robot Interface for Robotics Programming in the Complex Environment

Abstract To solve the problems of complex robot programming tasks, we propose an Augmented Reality (AR) based human-robot interface for planning a collision-free path in a complex environment. Current robot programming methods usually require a high level of experience in robot programming (online programming), the time-consuming 3D modeling of the working environment for collision detection (offline programming), and a tedious and inefficient re-planing to adapt environment or task changes (both online and offline programming). In order to address these problems, an end-to-end AR human-robot interface is proposed, which provides a new affordance to users by enabling them to plan the path in the AR environment. A set of user-interactive tools allow users to define and edit waypoints as the high-level guidance and the direct inputs for the toolpath planning package, Kinematics and Dynamics Library (KDL). With the fast sensing of the workspace and accurate rendering, an in-situ simulation module is utilized for collision check and verification by the users’ perception. Users will repeat the process of 1) waypoints definition and editing, and 2) the collision checking and path feasibility verification, until a satisfactory path is obtained. A preliminary testing is conducted in a use case with complex obstacles to verified the effectiveness and the efficiency of the proposed interface.
Cangjie's Poetry

This paper describes the conceptual background, artificial intelligent system design, and visualization strategies of an interactive art experience: Cangjie's Poetry. This artwork provides a conceptual response to the human-machine reality in the context of language, symbols, and semantic meanings. In the Cangjie's Poetry art installation, the intelligent system (Cangjie) constantly observes surroundings through the lens of a camera, writes poetry using its symbolic system based on its interpretation, and explains the evolving poem in natural language to audiences in real time. Due to the global pandemic of COVID-19, multiple presentation formats of this work were developed, which include in-person installation, virtual installation, and a special edition with pre-rendered video. This work prioritizes ambiguity and tension between machinic vision and human perception, the actual and the virtual, past and present.
Bühler’s organon model of communication: a semiotic analysis of advertising slogans

Abstract This article investigates the member units of sign systems used in contemporary Iranian advertising slogans according to the Organon Model introduced by Karl Bühler. In dealing with this subject, the writer introduces Bühler’s Organon Model and provides a short definition of the term advertising slogan . The next part of this article shows the three types of conative functions with regard to such slogans. The corpus of this study is the slogan of a hundred contemporary Iranian advertising messages used in Iranian markets which have not been studied from a semiotic perspective. The result of this research shows the tendencies of Iranian advertising messages to lean towards the descriptive type of conative function.
Programmable, high-speed all-optical nonlinear activation functions for neuromorphic photonics

We experimentally demonstrate programmable, all-optical nonlinear activation functions, including: rectified linear unit (ReLU), inverse-ReLU and quadratic functions, on a silicon-nitride platform at a remarkable speed of 10 Gbps, enabling real-time processing on all-purpose photonic hardware.
Design and Fabrication of 3D Interconnects for Photonic Neuronal Networks Using Two-Photon Polimerization

Photonic neuromorphic networks composed of spiking nanolight sources require novel approaches for compact and efficient optical interconnection. We design, simulate and fabricate 3D polymer waveguides and obtain promising elevated structures suited for complex waveguide crossings.
Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices

The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, especially on mobile platforms. However, to achieve high accuracy, state-of-the-art methods tend to have a large model size and complex post-processing algorithm, which costs intense computation and long end-to-end latency. To solve this problem, we propose an architecture optimization and weight pruning framework to accelerate inference of multi-person pose estimation on mobile devices. With our optimization framework, we achieve up to 2.51X faster model inference speed with higher accuracy compared to representative lightweight multi-person pose estimator.