Reviewerly/exHarmony huggingface dataset:
SkyBridge

Microkernels have been extensively studied over decades. However, IPC (Inter-Process Communication) is still a major factor of runtime overhead, where fine-grained isolation usually leads to excessive IPCs. The main overhead of IPC comes from the involvement of the kernel, which includes the direct cost of mode switches and address space changes, as well as indirect cost due to the pollution of processor structures.
A framework for semi-supervised metric transfer learning on manifolds

A common assumption of statistical learning theory is that the training and testing data are drawn from the same distribution. However, in many real-world applications, this assumption does not hold true. Hence, a realistic strategy, Cross Domain Adaptation (DA) or Transfer Learning (TA), can be used to employ previously labelled source domain data to boost the task in the new target domain. Previously, Cross Domain Adaptation methods have been focused on re-weighting the instances or aligning the cross-domain distributions. However, these methods have two significant challenges: (1) There is no proper consideration of the unlabelled data of target task as in the real-world, an abundant amount of unlabelled data is available, (2) The use of normal Euclidean distance function fails to capture the appropriate similarity or dissimilarity between samples. To deal with this issue, we have proposed a Semi-Supervised Metric Transfer Learning framework called SSMT that reduces the distribution between domains both statistically and geometrically by learning the instance weights, while a regularized distance metric is learned to minimize the within-class co-variance and maximize the between-class co-variance simultaneously for the target domain. Compared with the previous works where Mahalanobis distance metric and instance weights are learned by using the labelled data or in a pipelined framework that leads to a decrease in the performance, our proposed SSMT attempts to learn a regularized distance metric and instance weights by considering unlabelled data in a parallel framework. Experimental evaluation on three cross-domain visual data sets, e.g., PIE Face, Handwriting Digit Recognition on MNIST–USPS and Object Recognition, demonstrates the effectiveness of our designed approach on facilitating the unlabelled target task learning, compared to current state-of-the-art domain adaptation approaches.
FA3C

Deep Reinforcement Learning (Deep RL) is applied to many areas where an agent learns how to interact with the environment to achieve a certain goal, such as video game plays and robot controls. Deep RL exploits a DNN to eliminate the need for handcrafted feature engineering that requires prior domain knowledge. The Asynchronous Advantage Actor-Critic (A3C) is one of the state-of-the-art Deep RL methods. In this paper, we present an FPGA-based A3C Deep RL platform, called FA3C. Traditionally, FPGA-based DNN accelerators have mainly focused on inference only by exploiting fixed-point arithmetic. Our platform targets both inference and training using single-precision floating-point arithmetic. We demonstrate the performance and energy efficiency of FA3C using multiple A3C agents that learn the control policies of six Atari 2600 games. Its performance is better than a high-end GPU-based platform (NVIDIA Tesla P100). FA3C achieves 27.9% better performance than that of a state-of-the-art GPU-based implementation. Moreover, the energy efficiency of FA3C is 1.62x better than that of the GPU-based implementation.
Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF

Clinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather than other languages. Recently, a few researchers have began to study entity recognition in Chinese clinical text.In this paper, a novel deep neural network, called attention-based CNN-LSTM-CRF, is proposed to recognize entities in Chinese clinical text. Attention-based CNN-LSTM-CRF is an extension of LSTM-CRF by introducing a CNN (convolutional neural network) layer after the input layer to capture local context information of words of interest and an attention layer before the CRF layer to select relevant words in the same sentence.In order to evaluate the proposed method, we compare it with other two currently popular methods, CRF (conditional random field) and LSTM-CRF, on two benchmark datasets. One of the datasets is publically available and only contains contiguous clinical entities, and the other one is constructed by us and contains contiguous and discontiguous clinical entities. Experimental results show that attention-based CNN-LSTM-CRF outperforms CRF and LSTM-CRF.CNN and attention mechanism are individually beneficial to LSTM-CRF-based Chinese clinical entity recognition system, no matter whether contiguous clinical entities are considered. The conribution of attention mechanism is greater than CNN.
How useful are earthquake early warnings? The case of the 2017 earthquakes in Mexico city

The paper presents the results of a cross-sectional study regarding participants' perception on the SASMEX system performance following the two earthquakes that hit Mexico City on 7 and 19 September in 2017. The approach has been the application of questionnaires to a sample size of n = 2400. The survey was conducted from 4 October-20 November 2017. The questions included in the questionnaire were based on the criteria of the effectiveness of early warning systems proposed by the United Nations-International Strategy for Disaster Reduction (UN-ISDR). Some of the key conclusions were the following: {a}. Deficiencies have been highlighted in relation to ‘risk knowledge’, ‘monitoring and warning’, ‘dissemination & communication’, and ‘response capability’ of the system; {b}. The results have highlighted the need to issue the warning time to the residents of the city (i.e., the time between the moment the siren sounds and the actual ground shaking); knowing the warning time may be of great value to people during the seismic emergency, at least for the case of Mexico City, however, this may require further research; {c}. More generally, the SASMEX and any earthquake early warning (EEW) system should be people-centred.
Distributed E-Voting and E-Bidding Systems Based on Smart Contract

Traditional voting and bidding systems largely rely on paperwork and human resources throughout the voting process, which can incur high costs in terms of both time and money. Electronic voting and electronic bidding systems can be used to reduce costs, and many new systems have been introduced. However, most systems require a powerful and trusted third party to guarantee system integrity and security. With developments in blockchain technology, research has begun to highlight the core concept of decentralization. In this study, we introduce the first decentralized electronic voting and bidding systems based on a blockchain and smart contract. We also use cryptographic techniques such as oblivious transfer and homomorphic encryptions to improve privacy protection. Our proposed systems allow voters and bidders to participate in the opening phase and improve participant anonymity, the privacy of data transmission, and data reliability and verifiability. Moreover, compared with other electronic voting and bidding systems, our systems are safer and more efficient.
Photon-Assisted Charge-Parity Jumps in a Superconducting Qubit

We evaluate the rates of energy and phase relaxation of a superconducting qubit caused by stray photons with energy exceeding the threshold for breaking a Cooper pair. All channels of relaxation within this mechanism are associated with the change in the charge parity of the qubit, enabling the separation of the photon-assisted processes from other contributions to the relaxation rates. Among the signatures of the new mechanism is the same order of rates of the transitions in which a qubit loses or gains energy, which is in agreement with recent experiments. Our theory offers the possibility to characterize the electromagnetic environment of superconducting devices at the single-photon level for frequencies above the superconducting gap.
Thermal infrared and visible sequences fusion tracking based on a hybrid tracking framework with adaptive weighting scheme

Object tracking based on single sensor image sequences is now proved to be insufficient when facing complex challenging factors such as occlusions, background clutter, illumination variations, deformation and scale change. Complementary information between thermal infrared and visible image sequences is highly valuable and plays a critical role in tracking under complex scenarios. Previous fusion-before-tracking algorithms are not efficient and accurate enough due to the inevitable introduction of redundant information and considerable computational consumption. In this paper, we propose a robust fusion tracking method that exploits the abovementioned complementary information under a hybrid “tracking-by-detection” framework which consists of two tracking modules—the correlation filter based tracking (CFT) module and histogram based tracking (HIST) module. In CFT module, features extracted from both thermal infrared and visible images such as histogram of oriented gradient (HOG), image intensity and color names, are utilized to generate response maps and then adaptively fused through a denoising fusion scheme. In HIST module, a response map is obtained by adopting RGB color histogram in a statistical tracking model. Then, the response maps of two modules are fused via a new adaptive weighting scheme we proposed. Extensive experimental results on challenging thermal infrared and visible image sequences demonstrate the accuracy and robustness of the proposed method in comparison with several state-of-the-art methods.
An extended TODIM method to rank products with online reviews under intuitionistic fuzzy environment

Recently, in order to help consumers make decisions, ranking products with online reviews has become an interesting topic. However, literatures concerning this topic are really scare. Therefore, the paper proposes an extended TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method to rank products through online reviews. To begin with, the IF (intuitionistic fuzzy) based sentiment word framework and corresponding computation rules are constructed, where intuitionistic fuzzy set (IFS) is used to describe sentiment orientations and emotional intensity. Next, both frequency and attention degree of each feature are considered in calculating the feature weight. In addition, two-additive fuzzy measure, nonlinear programming, and Choquet integral are fully utilized to deal with positive, mutual independent, and negative criteria interactions. Finally, we use a case study to illustrate the proposed method and the results show that the proposed method can be effectively used to rank products through online reviews.
Assessing the impact of processes on the Occupational Safety and Health Management System’s effectiveness using the fuzzy cognitive maps approach

The paper presents research in which the fuzzy cognitive maps (FCM) approach was applied in examining the impact of individual processes in the Occupational Safety and Health Management System (OSH MS) on the effectiveness of this System. For this purpose, an FCM OSH MS model comprising 16 objects has been developed. Fifteen of those objects corresponded to the processes in the OSH MS and one object corresponded to safety performance. Then a series of simulations was carried out, in which it was successively predicted how the improvement of individual processes affected overall safety performance. It was finally proven that safety performance increased most significantly under the influence of improvement of the leadership process. This proves that among all processes in the system, leadership has the greatest positive impact on its effectiveness. Practical implications of such simulation results are discussed herein, and further, extended application of FCM to improve effectiveness of OSH MSs is suggested.
Early childhood education teachers’ professional development towards pedagogical leadership

Background: In recent years, early childhood education (ECE) has faced many reforms that have led to new requirements for pedagogy. Due to this evolution, there is, in turn, an increasing need to enhance the professional competence of ECE teachers through further training.Purpose: This article examines ECE teachers’ interpretations of the changes in their pedagogical thinking during a further training course for ECE teachers in Finland. The aim of this qualitative research was to investigate the professional development that can be related to the further training. The context of the study was an 18-month long ECE teachers’ further training course called ‘Pedagogy of Early Childhood Education during Changing Practices’.Sample: The study involved 32 ECE teachers participating in the further training course. The data consisted of the written responses of the further training participants in respect of their interpretation of pedagogy.Design and methods: The research was carried out using a participatory action approach. The written data were collected from the teacher participants. Thematic analysis was firstly conducted inductively from the data. In the second phase, content analysis was applied using abductive reasoning. The theory-based content analysis was conducted using pedagogical leadership aspects of the theory of human capital.Results: The features of professional development that were identified were explored through the lens of pedagogical leadership and grouped into four dimensions: increased knowledge, awareness of the quality of previously implemented pedagogy, developmental skills, and ability to make the case for ECE pedagogy.Conclusions: The research identified connections and relationships between the further training and professional development in pedagogical leadership. The significance of and demand for a participatory action research approach to further training for ECE teachers are discussed.
PROCESSING OCEANOGRAPHIC DATA BY PYTHON LIBRARIES NUMPY, SCIPY AND PANDAS

The study area is located in western Pacific Ocean, Mariana Trench.The aim of the data analysis is to analyze the potential influence of how various geological and tectonic factors may affect the geomorphological shape of the Mariana Trench.Statistical analysis of the data set in marine geology and oceanography requires an adequate strategy on big data processing.In this context, current research proposes a combination of the Python-based methodology that couples GIS geospatial data analysis.The Quantum GIS part of the methodology produces an optimized representative sampling dataset consisting of 25 cross-section profiles having in total 12,590 bathymetric observation points.The sampling of the geospatial dataset are located across the Mariana Trench.The second part of the methodology consists of statistical data processing by means of high-level programming language Python.Current research uses libraries Pandas, NumPy and SciPy.The data processing also involves the subsampling of two auxiliary masked data frames from the initial large data set that only consists of the target variables: sediment thickness, slope angle degrees and bathymetric observation points across four tectonic plates: Pacific, Philippine, Mariana, and Caroline.Finally, the data were analyzed by several approaches: 1) Kernel Density Estimation (KDE) for analysis of the probability of data distribution; 2) stacked area chart for visualization of the data range across various segments of the trench; 3) spacial series of radar charts; 4) stacked bar plots showing the data distribution by tectonic plates; 5) stacked bar charts for correlation of sediment thickness by profiles, versus distance from the igneous volcanic areas; 6) circular pie plots visualizing data distribution by 25 profiles; 7) scatterplot matrices for correlation analysis between marine geologic variables.The results presented a distinct correlation between the geologic, tectonic and oceanographic variables.Six Python codes are provided in full for repeatability of this research.
Semi-supervised Training for End-to-end Models via Weak Distillation

End-to-end (E2E) models are a promising research direction in speech recognition, as the single all-neural E2E system offers a much simpler and more compact solution compared to a conventional model, which has a separate acoustic (AM), pronunciation (PM) and language model (LM). However, it has been noted that E2E models perform poorly on tail words and proper nouns, likely because the end-to-end optimization requires joint audio-text pairs, and does not take advantage of additional lexicons and large amounts of text-only data used to train the LMs in conventional models. There has been numerous efforts in training an RNN-LM on text-only data and fusing it into the end-to-end model. In this work, we contrast this approach to training the E2E model with audio-text pairs generated from unsupervised speech data. To target the proper noun issue specifically, we adopt a Part-of-Speech (POS) tagger to filter the unsupervised data to use only those with proper nouns. We show that training with filtered unsupervised-data provides up to a 13% relative reduction in word-error-rate (WER), and when used in conjunction with a cold-fusion RNN-LM, up to a 17% relative improvement.
Classifying code comments in Java software systems

Code comments are a key software component containing information about the underlying implementation. Several studies have shown that code comments enhance the readability of the code. Nevertheless, not all the comments have the same goal and target audience. In this paper, we investigate how 14 diverse Java open and closed source software projects use code comments, with the aim of understanding their purpose. Through our analysis, we produce a taxonomy of source code comments; subsequently, we investigate how often each category occur by manually classifying more than 40,000 lines of code comments from the aforementioned projects. In addition, we investigate how to automatically classify code comments at line level into our taxonomy using machine learning; initial results are promising and suggest that an accurate classification is within reach, even when training the machine learner on projects different than the target one. Data and Materials [ https://doi.org/10.5281/zenodo.2628361 ].
Alternative Avenues for IoT

We report on the findings of a co-speculative design inquiry that investigates alternative visions of the Internet of Things (IoT) for the home. We worked with 16 people living in non-stereotypical homes to develop situated and personal concepts attuned to their home. As a prompt for co-speculation and discussion, we created handmade booklets where we took turns overlaying sketched design concepts on top of photos taken with participants in their homes. Our findings reveal new avenues for the design of IoT systems such as: acknowledging porous boundaries of the home, exposing neighborly relations, exploring diverse timescales, revisiting agency, and embracing imaginary and potential uses. We invite human-computer interaction and design researchers to use these avenues as starting points to broaden current assumptions embedded in design and research practices for domestic technologies. We conclude by highlighting the value of examining divergent perspectives and surfacing the unseen.
Life-Affirming Biosensing in Public

"Smart city" narratives promise IoT data-driven innovations leveraging biosensing technologies. We argue this overlooks a potential benefit of city living: affirmation. We designed the Heart Sounds Bench, which amplifies the heart sounds of those sitting on it, as well as recording and playing back the heart sounds of previous sitters. We outline our design intent to invite rest, reflection, and recognition of others' lives in public space. We share results from a study with 19 participants. Participants expressed feeling connected to a shared life energy including others and the environment, and described heart sounds as feeling intimate yet anonymous. Finally, we elaborate the concept of life-affirmation in terms of recognition of others' lives, feeling connection, and respecting untranslatable differences with opacity, as a way of helping "smart city" designs embrace a multiplicity of desires.
Implicit Communication of Actionable Information in Human-AI teams

Humans expect their collaborators to look beyond the explicit interpretation of their words. Implicature is a common form of implicit communication that arises in natural language discourse when an utterance leverages context to imply information beyond what the words literally convey. Whereas computational methods have been proposed for interpreting and using different forms of implicature, its role in human and artificial agent collaboration has not yet been explored in a concrete domain. The results of this paper provide insights to how artificial agents should be structured to facilitate natural and efficient communication of actionable information with humans. We investigated implicature by implementing two strategies for playing Hanabi, a cooperative card game that relies heavily on communication of actionable implicit information to achieve a shared goal. In a user study with 904 completed games and 246 completed surveys, human players randomly paired with an implicature AI are 71% more likely to think their partner is human than players paired with a non-implicature AI. These teams demonstrated game performance similar to other state of the art approaches.
Development of an ANN based corrective algorithm of the operational ECMWF global horizontal irradiation forecasts

This paper proposes a corrective algorithm for improving the accuracy of global horizontal irradiation (GHI) forecasts obtained from the numerical weather prediction (NWP) model of the European Centre for Medium-range Weather Forecast (ECMWF). Firstly, GHI forecasts were compared with experimental values from two ground-based stations located at the south of Portugal (Évora and Sines), and the influence of Sun-Earth geometry and atmospheric variables on the differences between predictions and measurements was analysed in order to identify the most relevant parameters. These differences are shown to be correlated mainly with the clearness index, solar zenith angle, mean air temperature, relative air humidity and total water column. Since the ECMWF model directly or indirectly provides all these variables, it is possible to estimate the bias of the predicted GHI as a function of forecast data taking as reference the measurements, which means that an algorithm based on correlations of such parameters can be used to correct forecasts in an operational time horizon. With that goal, an artificial neural network (ANN) based algorithm was developed in this work to improve GHI predictions, including also as input the global solar irradiation predicted by a reference clear sky model. The internal structure of the ANN was optimised, and a spatial and temporal downscaling procedure was also developed to obtain half-hour irradiation values. This algorithm was tested against the original ECMWF forecasts and a persistence model for four locations with different orography and climate as well as for various sky conditions (overcast, partly cloudy and clear sky), showing that it successfully improves the model predictions. Higher values of a Global Performance Index (GPI) based on seven statistical indicators and Forecast Score (FS) were found for the algorithm simulations, e.g. respectively 1.066 and 0.348 when considering all the locations and cloud cover conditions, while for the original ECMWF predictions the GPI is −1.874 and the FS is 0.282. This algorithm is useful if integrated into energy management tools of solar energy systems, namely low/medium temperature solar thermal and photovoltaic systems.