Reviewerly/exHarmony huggingface dataset:
Addressing Label Sparsity With Class-Level Common Sense for Google Maps

Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting the previous expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations, locations, etc. Techniques for more general, categorical, KG curation do not seem to have made the same transition: the KG research community is still largely focused on logic-based methods that belie the common-sense characteristics of successful KGs. In this paper, we propose a simple yet novel three-tier crowd approach to acquiring class-level attributes that represent broad common sense associations between categories, and can be used with the classic knowledge-base default & override technique, to address the early label sparsity problem faced by machine learning systems for problems that lack data for training. We demonstrate the effectiveness of our acquisition and reasoning approach on a pair of very real industrial-scale problems: how to augment an existing KG of places and offerings (e.g. stores and products, restaurants and dishes) with associations between them indicating the availability of the offerings at those places. Label sparsity is a general problem, and not specific to these use cases, that prevents modern AI and machine learning techniques from applying to many applications for which labeled data is not readily available. As a result, the study of how to acquire the knowledge and data needed for AI to work is as much a problem today as it was in the 1970s and 80s during the advent of expert systems. Our approach was a critical part of enabling a worldwide local search capability on Google Maps, with which users can find products and dishes that are available in most places on earth.
RGBT Tracking by Fully-Convolutional Triple Networks with Cosine Embedding Loss

RGBT tracking has drawn much attention on computer vision in recent years, which aims to fuse complementary information from visible and thermal images for robust object tracking. There are a lot of works paying extensive explorations for fusing features from convolutional networks to integrate modalities, especially modality-specific properties. Although these methods have achieved quite good performance, it is insufficient to represent and interpret the commonness and specificity, as well as the relationship between them, which are significant for RGBT tracking. In this work, we propose a novel triple network to extract Modal-Common, Modal-Specific features, which interprets Modal Common-Specific information from multi-modal images. Besides, a corresponding cosine embedding loss is designed to differentiate features and make them discriminative. For the purpose of perceiving complementary information of modal-specific features, we propose a cross-modal attention-query module, which queries channel attention of another modality and enhances relevant channels itself. Moreover, we conduct an efficient tracker with fully-convolutional siamese network for real-time RGBT tracking. Extensive experiments on two RGBT benchmark datasets has proved the excellent performance and efficiency of our method compared with classic RGB trackers and other state-of-the-art RGBT tracking algorithms.
Numerical investigation of the logarithmic Schrödinger model of quantum decoherence

A logarithmic Schr\"odinger equation with time-dependent coupling to the nonlinearity is presented as a model of collisional decoherence of the wave function of a quantum particle in position space. The particular mathematical form of the logarithmic Schr\"odinger equation has been shown to follow from conditional wave theory, but the validity of the logarithmic Schr\"odinger equation has not yet been investigated numerically for general initial conditions. Using an operator-splitting approach, we solve the nonlinear equation of motion for the wave function numerically and compare it to the solution of the standard Joos-Zeh master equation for the density matrix. We find good agreement for the time-dependent behavior of the ensemble widths between the two approaches, but note curious zero-pinning behavior of the logarithmic Schr\"odinger equation, whereby the zeros of the wave function are not erased by continued propagation. By examining the derivation of the logarithmic Schr\"odinger equation from conditional wave theory, we indicate possible avenues of resolution to this zero-pinning problem.
Service Recommendations Using a Hybrid Approach in Knowledge Graph with Keyword Acceptance Criteria

Businesses are overgrowing worldwide; people struggle for their businesses and startups in almost every field of life, whether industrial or academic. The businesses or services have multiple income streams with which they generate revenue. Most companies use different marketing and advertisement strategies to engage their customers and spread their services worldwide. Service recommendation systems are gaining popularity to recommend the best services and products to customers. In recent years, the development of service-oriented computing has had a significant impact on the growth of businesses. Knowledge graphs are commonly used data structures to describe the relations among data entities in recommendation systems. Domain-oriented user and service interaction knowledge graph (DUSKG) is a framework for keyword extraction in recommendation systems. This paper proposes a novel method of chunking-based keyword extractions for hybrid recommendations to extract domain-specific keywords in DUSKG. We further show that the performance of the hybrid approach is better than other techniques. The proposed chunking method for keyword extraction outperforms the existing value feature entity extraction (VF2E) by extracting fewer keywords.
Image‐level dataset synthesis with an end‐to‐end trainable framework

Dataset synthesis via virtual engines like Unity is attracting much more attention in recent years due to its low cost at obtaining ground-truth labels. For this kind of work, virtual environments are constructed within the engine to mimic the real-world, either with great manual efforts or learning-based methods. The latter shows superiority over the former when the target real-world scenes are changeable, from which the attributes of environments can be automatically adjusted based on the distribution difference between the synthetic and real-world datasets. However, the non-differentiability of whole pipeline hinders the efficiency of attribute optimization. To this end, this paper proposes to simulate synthetic datasets from a fine-grained perspective, such that the system can be trained at an end-to-end manner. Specifically, it is converted into an image-level data synthesis problem, and designs a constraint using the content loss between two images. As the rendering process of virtual engine is mathematically unknown, which blocks the back propagation of the gradients, a generative model is trained to approximate the engine. As a result, the whole framework becomes fully differentiable and the attributes can be optimized efficiently by gradient descent. Experimental result shows the efficiency of our method in obtaining useful synthetic training datasets. Besides, it is found that the image-level method enables to learn the potential distribution of real-world data, which is hard to be achieved by existing methods. As far as we know, it is the first attempt to finish this task with a differentiable process.
Editorial – Digital Transformation in Higher Education Institutions in a Limited-resource Setting: A Luxury or a Must Despite Challenges?

This is an Editorial and does not have an abstract. Please download the PDF or view the article HTML.
The Underestimated Success Factor: People

This chapter deals with the so-called soft factors; although underestimated by many, they greatly influence a project’s success. The following pages are, therefore, all about the people involved in your project.
Knowledge-Aware Multispace Embedding Learning for Personalized Recommendation

Recommender systems help users filter items they may be interested in from massive multimedia content to alleviate information overload. Collaborative filtering-based models perform recommendation relying on users’ historical interactions, which meets great difficulty in modeling users’ interests with extremely sparse interactions. Fortunately, the rich semantics hidden in items may be promising in helping to describing users’ interests. In this work, we explore the semantic correlations between items on modeling users’ interests and propose knowledge-aware multispace embedding learning (KMEL) for personalized recommendation. KMEL attempts to model users’ interests across semantic structures to leverage valuable knowledge. High-order semantic collaborative signals are extracted in multiple independent semantic spaces and aggregated to describe users’ interests in each specific semantic. The semantic embeddings are adaptively integrated with a target-aware attention mechanism to learn cross-space multisemantic embeddings for users and items, which are fed to the subsequent pairwise interaction layer for personalized recommendation. Experiments on real-world datasets demonstrate the effectiveness of the proposed KMEL model.
Design and Build Monitoring System for Pregnant Mothers and Newborns using the Waterfall Model

In Indonesia, the problems of pregnant women and newborns have not been resolved. The reason is that there is no monitoring system for pregnant women and newborns. This study aims to build a monitoring system for pregnant women and newborns. The monitoring system that will be made is mobile-based that can monitor and provide information about the health of pregnant women and children. The model used to create an information system uses the waterfall model. Meanwhile, to test the system built using the Blackbox test model. The plan that was created was tested directly to the user. As a result, the sequence in the waterfall model is proven to build a monitoring system for pregnant women and newborns. The black box model also showed if the built system did not have errors and was ready to be used. Combining the waterfall and BlackBox models results in a monitoring system for pregnant women and newborns whose entire menu can be used properly. The test was carried out on 25 pregnant women users, and it was found that the accumulated ratings were 89, 87, 88, and 89%. In this case, it is classified as Very Eligible.
Linguistic Approach to Segmenting Source Code

Source code segmentation is the process of dividing the source code of a program into meaningful pieces, such as in preparation for source code analysis (SCA) tasks. Our goal is to segment code based on the semantics of its content. Specifically such that the segments reflect logical locations that are good candidates for the insertion of manually composed comments or automatically generated comments. Instead of focusing on syntactic boundaries for segmentation, such as function and class declarations, we exploit the semantic content of the code. We use code snippets mined from Github as known semantic segments to train a LSTM Neural Network model. It is able to infer locations in the code where a programmer would likely insert comments. The model can operate on any text and performs well across multiple programming languages for detecting candidate segment boundaries within a program. This semantic code segmentation is especially useful for incomplete code repositories under development, which may be also written in more than one programming language. Additionally, our technique supports a detection threshold parameter so users can adjust the number of suggestions provided by our tool.
Research status and prospect of visual image feature point detection in body measurements

To improve visual image feature point detection with two-dimensional human body circumference measurements, this paper analyzes and compares several feature point detection algorithms, algorithm based on human body proportion, and deep-learning-based convolutional neural network algorithms. Among them, the principles and application of the Harris corner detection algorithm are described in detail. The progress in image feature points detection based on human body proportions and regions is then summarized and reviewed. Based on the convolutional neural network method, a method of calculating feature points based on the relatively mature human joint point recognition algorithm is proposed, so as to solve the problem of inaccurate positioning in traditional algorithms. Finally, the challenge of feature point detection for clothing volumes is explained. Feature point detection from visual images has significant development prospects in the two-dimensional measurement of body circumference. Related research provides a reliable theoretical reference for realizing the accurate and rapid detection of human feature points in visual images.
Forecasting day-ahead 1-minute irradiance variability from NWP output&amp;#160;

&amp;lt;p&amp;gt;Forecasting day-ahead 1-minute irradiance variability from NWP output&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Accurate forecasts of solar irradiance are required for the large scale integration of solar photovoltaic (PV) systems. Fluctuations of energy generation in the order of minutes can lead to issues on the electricity grid, therefore accurate forecasts of minute-to-minute irradiance variability are required. However, state of the art numerical weather predictions (NWP) deliver forecasts at a much coarser temporal resolution, e.g. hourly averages.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this work we present a methodology to forecast a quantification of minute-to-minute irradiance variability as well as the probability distribution function (pdf), by applying statistical postprocessing and machine learning on hourly NWP ourput. In total, 10 target parameters related to the irradiance variability are forecasted. The algorithm is tested using the NWP HARMONIE-AROME (HA) mesoscale model as input, with 1-minute irradiance observations for 18 locations throughout the Netherlands used as ground truth.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Results show that the proposed algorithm is capable of forecasting the 1-minute irradiance PDF with reasonable resemblence to the observed PDF. Moreover, we show that inaccuracies of the postprocessed result are to a large extent due toerrors in the radiation forecast of the NWP used as input, reducing the average R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; score from 0.75 to .57 for the most relevant targets. The generalizability of the proposed algorithm is demonstrated by training the model on data of a single site and testing the performance on all 18 sites. Surprisingly, we find for 14 sites the model achieves higher accuracy than at the site it was trained on. Including data of all sites in the train set improves the accuracy on 3 of the 6 relevant target parameters while decreasing the accuracy on 1. Finally, we compare this work on post-processing to the next generation weather models based on high resolution Large Eddy Simulation (LES). A case study spanning four days is performed on four days well-captured by the NWP model and results are compared to results from the post-processing algorithm. While LES underestimates values of high irradiance due to lack of 3D radiative effects, it enables detailed analysis of the dynamics at high spatial and temporal resolution unreachable by statistical postprocessing.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The algorithm presented in this work is able to predict intra-hour irradiance variability based on day-ahead NWP output. Thereby moving forward significantly towards improving grid operation, planning, and resilience in relation to large-scale solar PV generation.&amp;lt;/p&amp;gt;
Dynamical decoupling and NNN discrete quantum networks

Perfect State Transfer is known to be possible on complex next-to-nearest-neighbor structures of qubits. The complexity of these networks coupled with the pervasive imperfections of the real world can lead to shortcomings in their practical construction. We analyze several realistic categories of flaws in the networks and show that they can have a harsh effect on the network’s performance. Dynamical decoupling is a known quantum error correction algorithm that can be used for partial or complete mitigation of undesired artefacts in quantum systems. We show here that dynamical decoupling can, in principle, also be used to stabilize the complex next-to-nearest-neighbor networks of qubits. We compare the proposed dynamical decoupling schemes with a baseline of dynamical decoupling scheme that eliminates all but one path to quantum state transfer on the network, thus effectively transforming it to a nearest-neighbor chain.
Towards a Connected Smart Classroom: Case of an Adaptive Teaching Activity

Recently, several “smart classroom” architectures have been developed with its specific architecture, most of which are general architectures for teaching and distance learning. Therefore, there is a dire need, as a particular challenge, to model and develop a generic architecture that should integrate into particular features of adaptive e-learning in a single model. This latter aims at making the system more efficient and helpful to learners to advance and complete their learning courses successfully while exploiting the traceability and history of the activities taught. The data related to each learner and the trainer was also exploited to predict the optimal and automatic choice of the learning path to be taken. Today, this challenge is now possible thanks to the rapid evolution of Information and Communication Technologies for Education (ICTE), where a new concept of the Internet has emerged under the name Internet of Things (IoT). This has allowed the development and exploitation of the concept of “intelligent environments” in an educational process, considering the specific aspects of this process (face-to-face education, virtual education, self-training, etc.), into a single model. This was achieved while personalizing the educational content based on the specific needs of the learner specifically putting the latter in the heart of the learning process as the main actor.
Efficient stitching method of tiled scanned microelectronic images

Abstract Sparse features and repetitive textures are frequently presented in microelectronic microscopic images. Therefore, it is challenging for image stitching to meet the requirements of high-speed precision manufacturing. A novel image stitching method for tiled images is proposed to generate panoramic images of microelectronics quickly and accurately. According to the preset scan trajectory, grids were established between adjacent images for feature matching. The clustering algorithm was used to screen reasonable and multiple sets of registrations. Then, all registrations were used as connecting edges, and images were used as nodes, to create a multigraph. The unique registration in multigraph was solved by a non-linear minimization problem with linear constraints. Finally, image transformations were computed in global optimization for rendering panoramic images via image warping. The experimental results show that the proposed method improves the stability and efficiency of image stitching, furthermore, it maintains an equivalent level of precision as the Fiji and microscopy image stitching tool methods.
Brainwave-Augmented Eye Tracker: High-Frequency SSVEPs Improves Camera-Based Eye Tracking Accuracy

In this work, we leverage neural mechanisms of visual attention to improve the accuracy of a commercial eye tracker through the analysis of electroencephalography (EEG) waves. Gaze targets were rendered in a computer screen with imperceptible flickering stimuli (≥ 40Hz) that elicited attention-modulated steady-state visual evoked potentials (SSVEPs). Our hybrid system combines EEG and eye-tracking modalities to overcome accuracy limitations of the gaze-tracker alone. We integrate EEG and gaze data to efficiently exploit their complementary strengths driving a Bayesian probabilistic decoder that estimates the target gazed by the user. Our system’s performance was analyzed across the screen with varying target sizes, spacings and dataset epoch lengths, using data from 10 subjects. Overall, our hybrid approach improves the classification accuracy of the eye tracker alone for all target parameters and dataset epoch lengths in 11 units on average. The system shows a larger impact at peripheral screen regions where performance enhancement is maximal, reaching improvements of over 45 units. The findings of this work demonstrate that the intrinsic accuracy limitations of camera-based eye-trackers can be corrected with the integration of EEG data, and opens opportunities for gaze tracking applications with higher target granularity.
Garbage Classification Using YOLOv3

This paper proposes a method to classify the garbage and thus classify it into two major categories that is biodegradable and non-biodegradable garbage by the use of YOLOv3 model. The model was used to train a dataset of 12,000 images which consisted of the images of both biodegradable and non-biodegradable wastes and the MAP efficiency of the model was 99.57% to detect the images which makes it a fairly accurate model to be used for the classification of garbage. This result of this study shows that it can be used further for classification of garbage in industry where human intervention is not necessary, and automation of segregation can be deployed.
Lessons for a ‘Post-Pandemic' Future

This chapter synthesises findings from the analysis of law – and the application of law – during the COVID-19 pandemic in a range of national contexts, and draws lessons to strengthen preparedness for future emergencies. It ventures that states on the spectrum from democracy to autocracy exhibited a wide variety of approaches that defy easy categorisation and challenge pre-existing assumptions about state responses during emergency. Democracies and autocracies proved equally, and unpredictably, vulnerable to the pandemic in terms of both public health outcomes, and the ‘health’ of their institutions and their capacity to adapt and effectively respond to pandemic conditions. The chapter ventures that good governance and public trust have proved inseparable during the COVID-19 pandemic and must be the focus for investment and development. The chapter makes recommendations in respect of legal reform; the use of expertise; inequality; democratic rehabilitation; global and multilevel governance; and building public trust through rational discourse.