Reviewerly/exHarmony huggingface dataset:
A Deep Neural Networks Based on Multi-task Learning and Its Application

With the development of machine learning, the deep neural network has achieved excellent performance. However, the learning of deep neural network often needs a large number of data and the independent learning of a single task tends to ignore the information from other tasks, resulting in redundant training and the cost of learning resources. In order to solve these problems, we introduce the deep neural network based on multi-task learning model. We introduce the structure of the multi-tasking learning model in detail, and use demographic information to prove the effectiveness of the model. Finally deep neural network based on multi-task learning framework is adapted to builds a normal behavior model for wind turbine generator in which each subspace of operating condition are taken as a task. The operating data comes from Liaoning wind farm in northeast China are used to verify the proposed method. The results indicate the subspace division of different operating condition benefits to improve the accuracy of normal behavior model of wind turbine generator, and multi-task learning is suitable to deal with the integrate subspace model.
The calculus of language: explicit representation of emergent linguistic structure through type-theoretical paradigms

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Prognosis and Prediction of Breast Cancer Using Machine Learning and Ensemble-Based Training Model

Abstract There has been an increase in occurrence of human diseases all over the world. Among those, Breast Cancer has increased with an alarming rate in the past decade and this trend of increase would continue to grow. Now, there is a need for efficient text analytics and feature extraction tools to assist classifying, sharing and retrieving the information on human diseases in general and Breast Cancer in particular. In light of above, the present study has been undertaken with the objective to provide a comparative analysis of different classifiers on Breast Cancer dataset, and to propose a new ensemble training method of Machine Learning Classification. Here, machine learning models (such as K-Nearest Neighbour, Logistic Regression, Decision Tree, Random Forest, Gradient Boost, Support Vector Machine) and deep learning classifiers (such as Multi-Layer Feed Forward Neural Network, Recurrent Neural Network and Long Short Term Memory) have been applied on Breast Cancer dataset. An Ensemble Learning model for Prediction is proposed to classify the results among different classifiers. Finally, the Voting Ensemble is implemented to find out the optimal classifier for prediction of Breast Cancer. The results have been computed using the evaluation parameters such as Accuracy, Precision, Recall and Specificity. The confusion matrix drawn on the basis of evaluation parameters provides more emphasis on predicted and actual instances. Performance Evaluation for various machine learning models is computed. Results of this investigation concludes that Voting Ensemble outperforms other machine learning models. The prediction using Voting Ensemble resulted in an accuracy rate of 97.9 per cent, precision of 96.77 per cent and recall of 100 per cent.
Digital Land Registry System Using Blockchain

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SOURCE: a Freesound Community Music Sampler

SOURCE is an open-source hardware music sampler powered by Freesound's collection of near 500k Creative Commons sounds contributed by a community of thousands of people around the world. SOURCE provides a hardware interface with Freesound and implements different methods to search and load sounds into the sampler. We see SOURCE as a proof of concept device that can be used as and extendable base system on top of which further research on the interaction between hardware devices and online large sound collections can be carried out. This paper describes the architecture of SOURCE and the different ways in which it interacts with Freesound. Even though we have not carried out a formal evaluation of SOURCE our informal tests show that SOURCE can be successfully integrated into a live music performance setup, and influence the creative process in interesting ways by being able to quickly generate new rich sound palettes that would otherwise be difficult to create with traditional hardware music samplers.
Two Reconfigurable NDP Servers: Understanding the Impact of Near-Data Processing on Data Center Applications

Existing near-data processing (NDP)-powered architectures have demonstrated their strength for some data-intensive applications. Data center servers, however, have to serve not only data-intensive but also compute-intensive applications. An in-depth understanding of the impact of NDP on various data center applications is still needed. For example, can a compute-intensive application also benefit from NDP? In addition, current NDP techniques focus on maximizing the data processing rate by always utilizing all computing resources at all times. Is this “always running in full gear” strategy consistently beneficial for an application? To answer these questions, we first propose two reconfigurable NDP-powered servers called RANS ( R econfigurable A RM-based N DP S erver) and RFNS ( R econfigurable F PGA-based N DP S erver). Next, we implement a single-engine prototype for each of them based on a conventional data center and then evaluate their effectiveness. Experimental results measured from the two prototypes are then extrapolated to estimate the properties of the two full-size reconfigurable NDP servers. Finally, several new findings are presented. For example, we find that while RANS can only benefit data-intensive applications, RFNS can offer benefits for both data-intensive and compute-intensive applications. Moreover, we find that for certain applications the reconfigurability of RANS/RFNS can deliver noticeable energy efficiency without any performance degradation.
Elucidating the relationships between two automated handwriting feature quantification systems for multiple pairwise comparisons

Recent advances in complex automated handwriting identification systems have led to a lack of understandability of these systems' computational processes and features by the forensic handwriting examiners that they are designed to support. To mitigate this issue, this research studied the relationship between two systems: FLASH ID® , an automated handwriting/black box system that uses measurements extracted from a static image of handwriting, and MovAlyzeR® , a system that captures kinematic features from pen strokes. For this study, 33 writers each wrote 60 phrases from the London Letter using cursive writing and handprinting, which led to thousands of sample pairs for analysis. The dissimilarities between pairs of samples were calculated using two score functions (one for each system). The observed results indicate that dissimilarity scores based on kinematic spatial-geometric pen stroke features (e.g., amplitude and slant) have a statistically significant relationship with dissimilarity scores obtained using static, graph-based features used by the FLASH ID® system. Similar relationships were observed for temporal features (e.g., duration and velocity) but not pen pressure, and for both handprinting and cursive samples. These results strongly imply that both the current implementation of FLASH ID® and MovAlyzeR® rely on similar features sets when measuring differences in pairs of handwritten samples. These results suggest that studies of biometric discrimination using MovAlyzeR® , specifically those based on the spatial-geometric feature set, support the validity of biometric matching algorithms based on FLASH ID® output.
Transfer Learning in sEMG-based Gesture Recognition

The latest advancements in the field of deep learning and biomedical engineering have allowed for the development of myoelectric interfaces based on deep neural networks. A longstanding problem of these interfaces is that the models cannot easily be applied to new users due to the high variability and stochastic nature of the electromyography signals. Further training a new model for every new subject requires the collection of large volumes of data. Therefore, this work proposes a transfer learning (TL) scheme which allows reusing the knowledge of a pre-existing model for a new user. Firstly, a convolutional neural network (CNN) is trained on an initial dataset using the data of multiple subjects. Then, the weights of this model are fine-tuned for a new target subject. The approach is evaluated on the Ninapro datasets DB2 and DB7. The experimentation included three different CNN models and eight preprocessing alternatives. The results showed that the success of the TL method depends on how the data are preprocessed. Specifically, the biggest accuracy improvement (+5.14%) is achieved when only the first 20% of the signal duration is used.
Magic Bookmark: A Nonintrusive Electronic System for Functionalizing Physical Books

The use of physical paper is often preferred due to its unique physical properties that improve various aspects of reading. However, digital media and information are more engaging, diverse, and up to date, thereby challenging the existence of paper in our everyday life. By combining the two types of media in a seamless way, the interactivity of multimedia content can complement the reading experience, maintaining the unique feel of paper books. The current state of the art addressing this application negatively impacts the reading experience and often does not consider the manufacturability and sustainability of the proposed solutions. Herein, the Magic Bookmark, a technical solution for automatically recognizing the open page of a physical book, to provide seamless augmentation without changing the user's behavior and experience significantly is introduced. Three alternative solutions are implemented, with various degrees of ease of use, manufacturability at scale, and reliability of data reading. The optimal realization is found to be a reflective optical readout array, for which routes to implementation that may allow blending the graphical and functional aspects of the augmented book are proposed.
Chapter 7. Email communication in English and in German

This semi-longitudinal investigation examines greetings and closings in emails written by German university students in their native and foreign language. The results reveal students’ preference for formal greetings in both English and German. However, the data also suggest that not all students were aware of appropriate address term combinations in formal English emails. Regarding closings, the students used a limited set of standard formal closings in English but employed a greater variety of closing strategies in German. The results further revealed the importance of micro-analysing individual students’ contributions in a corpus, as this provides insights into an individual’s language use over time and also shows the impact individual students’ routine preferences can have on group scores.
Idea of Pandemic-Pedagogy: Reflexive Rumination on Teaching and Learning Practices

Lately, the debates on teaching-practices in the online mode have given grounds to ask a fundamental question, does pedagogy really matter? For educationists, it surely does. But, what idea of pedagogy prevails upon the pedagogues, the teachers, techno-managerial professors, particularly in the higher education? This article rummages through the contemporary milieu that solicits debate on the idea of pedagogy, by and large marginal in the teaching practices in higher education. The structural academic Brahmanism of the ‘old normal’ has been melodramatically palpable in the time of ‘new normal’ too. In the academic years during the COVID-19 pandemic, a phenomenology of teaching practices occasions a polemical realization about limits and possibilities of pedagogy.
A Technological and Methodological Ecosystem for Dynamic Virtual Acoustics in Telematic Performance Contexts

This paper presents the design and development of a technological ecosystem which facilitates research at the intersection of Virtual Acoustics, Networking, and Telematic Music. Building upon existing robust software packages, we integrate and extend this work in the context of exploring the effect of telematically-shared dynamic virtual acoustic spaces and expressive movement trajectories of player sound source positions upon live play contexts. Through analysis of impulse responses generated by a distributable measurement kit, remote players are able to share these spaces with their collaborators. Supplementary tools developed as part of this research also extend established telematic software solutions, allowing for easier use of network based musical communication systems for both performers and technicians. A network topology of interconnected telematic solutions is described, allowing flexibility between various internet based audio platforms. Applications which challenge and contain these developments are discussed, including a large-scale musical ensemble, web based musical performance, and an ongoing study into collaborative telematic improvisation.
Identification of elderly patients at risk for 30‐day readmission: Clinical insight beyond big data prediction

This study explores the potential benefit of combining clinicians' risk assessments and the automated 30-day readmission prediction model.Automated readmission prediction models based on electronic health records are increasingly applied as part of prevention efforts, but their accuracy is moderate.This prospective multisource study was based on self-reported surveys of clinicians and data from electronic health records. The survey was performed at 15 internal medicine wards of three general Clalit hospitals between May 2016 and June 2017. We examined the degree of concordance between the Preadmission Readmission Detection Model, clinicians' readmission risk classification and the likelihood of actual readmission. Decision trees were developed to classify patients by readmission risk.A total of 694 surveys were collected for 371 patients. The disagreement between clinicians' risk assessment and the model was 34.5% for nurses and 33.5% for physicians. The decision tree algorithms identified 22% and 9% (based on nurses and physicians, respectively) of the model's low-medium-risk patients as high risk (accuracy 0.8 and 0.76, respectively).Combining the Readmission Model with clinical insight improves the ability to identify high-risk elderly patients.This study provides algorithms for the decision-making process for selecting high-risk readmission patients based on nurses' evaluations.
Accelerating Pretrained Language Model Inference Using Weighted Ensemble Self-distillation

Pretrained language models (PLMs) have achieved remarkable results in various natural language processing tasks. As the performance of the model increases, it is also accompanied by more computational consumption and longer inference time, which makes deploying PLMs in edge devices for low-latency applications challenging. To address this issue, recent studies have recommended applying either model compression or early-exiting techniques to accelerate the inference. However, model compression permanently discards the modules of the model, leading to a decline in model performance. Train the PLMs backbone and the early-exiting classifier separately with early-exiting strategies. It not only brings extra training cost but also loses semantic information from higher layers, resulting in unreliable decisions of early-exiting classifiers. In this study, a weighted ensemble self-distillation method was proposed to improve the early-exiting strategy, which well balanced the performance and the inference time. It enables early-exiting classifiers to obtain rich semantic information from different layers with an attention mechanism according to the contribution of each layer to the final prediction. Furthermore, it simultaneously performs weighted ensemble self-distillation and fine-tuning of the PLMs backbone so that the PLMs can be fine-tuned in the training process of the early-exiting classifier to preserve the performance as much as possible. The experimental results show that the inference of the proposed model was accelerated at the minimum cost of performance loss, thus outperforming the previous early-exiting models. The code is available at: https://github.com/JunKong5/WestBERT.
SPEED21: Speed Climbing Motion Dataset

With the recent advances in computer vision and deep learning, the research interest in video-based and skeleton-based sports analysis is growing. Also, speed climbing as a sport is on the rise, being included as an Olympic sport in Tokyo 2020. This work aims to connect both of these worlds. First, a dataset of 362 speed climbing performances is provided for the community of domain experts and practitioners in human motion understanding and sports analysis. The dataset annotates pre-segmented performances of 55 world elite athletes in the form of 2D skeleton sequences extracted from world competition events videos. Secondly, a high descriptiveness and usability of 2D skeleton data is demonstrated in the search scenario that matches climbers by the similarities in their climbing style with high accuracy. The high k-NN search precision above 90 % is achieved by a synergic combination of suitable representation with a semi-dependent variant of Dynamic Time Warping (DTW). The proposed DTW variant computes distances separately across individual semantic body parts (e.g., hands and feet) whose atoms (joints or angles) are wired together for the temporal alignment.
End-to-end Boundary Exploration for Weakly-supervised Semantic Segmentation

It is full of challenges for weakly supervised semantic segmentation (WSSS) acquiring the pixel-level object location with only image-level annotations. Especially, the single-stage methods learn image- and pixel-level labels simultaneously to avoid complicated multi-stage computations and sophisticated training procedures. In this paper, we argue that using a single model to accomplish image- and pixel-level classification will fall into the balance of multi-target and consequently weakens the recognition capability. Because the image-level task tends to learn position-independent features, but the pixel-level task tends to be position-sensitive. Hence, we propose an effective encoder-decoder framework to explore object boundaries and solve the above dilemma. The encoder and decoder learn position-independent and position-sensitive features independently during the end-to-end training. In addition, a global soft pooling is suggested to suppress background pixels' activation for the encoder training and further improve the class activation map (CAM) performance. The edge annotations for the decoder training are synthesized by the high confidence CAMs, which do not requires extra supervision. The extensive experiments on the Pascal VOC12 dataset demonstrate that our method achieves state-of-the-art compared to the end-to-end approaches. It gets 63.6% and 65.7% mIoU scores on val and test sets respectively.
A Multi-Criteria Approach for Fast and Robust Representative Selection from Manifolds

The problem of representative selection amounts to sampling few informative exemplars from large datasets. Existing approaches to data selection often fall short of simultaneously handling non-linear data structures, sampling concise and non-redundant subsets, rejecting outliers, and yielding interpretable outcomes. This paper presents a novel representative selection approach, dubbed MOSAIC, for drawing descriptive sketches of arbitrary manifold structures. Resting upon a novel quadratic formulation, MOSAIC advances a multi-criteria selection approach that maximizes the global representation power of the sampled subset, ensures novelty of the samples by minimizing redundancy, and rejects disruptive information by effectively detecting outliers. Theoretical analyses shed light on geometrical characterization of the obtained sketch and reveal that the sampled representatives maximize a well-defined notion of data coverage in a transformed space. In addition, we present a highly scalable randomized implementation of the proposed algorithm shown to bring about substantial speedups. MOSAIC’s superiority in achieving the desired characteristics of a representative subset all at once while exhibiting remarkable robustness to various outlier types is demonstrated via extensive experiments conducted on both real and synthetic data with comparisons to state-of-the-art algorithms.
TRAMS: A Secure Vehicular Crowdsensing Scheme Based on Multi-Authority Attribute-Based Signature

Recently, vehicular crowdsensing networks have attracted much attention because of their ability to provide efficient and convenient information services for the Internet of Vehicles. How to achieve on-demand message authentication and provide privacy protection of sensing vehicles are challenging in accurate sensing tasks. We propose a secure vehicular crowdsensing scheme based on multi-authority attribute-based signature (TRAMS), which allows the publisher to flexibly customize a fine-grained policy that the potential participants must satisfy and uses attribute-based signature to authenticate sensed messages while protecting the privacy of the sensing vehicle. Also, we propose a multi-authority key management scheme, which can improve vehicle-based sensing efficiency in the Internet of Vehicles. Performance analysis shows that our scheme can not only achieve massage authentication while protecting the privacy of the sensing vehicle, but also ensure fine-grained message authentication to meet the expectation of the publisher on demand. And compared with the single-authority schemes in vehicular communication, our multi-authority TRAMS can achieve efficient message authentication for vehicular crowdsensing applications which require timely task feedback.
The Rise of Decentralized Cryptocurrency Exchanges: Evaluating the Role of Airdrops and Governance Tokens

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Intersection types for a λ-calculus with global store

We study the semantics of an untyped λ-calculus equipped with operators representing read and write operations from and to a global store. We adopt the monadic approach to model side effects and treat read and write as algebraic operations over a monad. We introduce an operational semantics and a type assignment system of intersection types, and prove that types are invariant under reduction and expansion of term and state configurations, and characterize convergent terms via their typings.