Reviewerly/exHarmony huggingface dataset:
A cloud-based REST platform for real-time health resources availability registering, discovering and matching in pandemic crisis conditions

Resources shortage during a time of crisis is a typical but also dangerous situation that health systems need to mitigate. When the health crisis reaches a global level, this shortage becomes more critical and spans across all related resource categories and types (materials, equipment, personnel etc). The aim of this work is to present an online flexible platform, based on a combination of REST API and messaging technologies, that targets at offering a mechanism for swift registration of resource types across affected entities, government or private institutions, individual or corporate donors and volunteers. A full range of functionalities is presented, including dynamic submission of resource availability or demand, matchmaking based on criteria, investigation of supply or demand status, asynchronous alerting, general overview of the situation as well as analytics capabilities. Thus either centralized government coordination or individual corporate or volunteer efforts can be redirected and optimize the distribution and usage of resources.
Do you know what's in those cookies? An analysis of the readability of social media cookie policies

Abstract Spend any time on the web and you will likely be prompted with a disclosure about cookies. The purpose of this exploratory study is to address the readability of cookie policies by examining a sample of popular social media websites operating in the United States. Flesch Reading Ease index and Flesch‐Kincaid Grade Level were used to assess the readability of select social media cookie policies. The results of this study suggest that the cookie policies of these sites are incongruous with the readability requirements for the average U.S. adult. Opportunities for future research to address questions related to coherence as well as the use, function, and purpose of cookies are discussed.
Tag Boosted Hybrid Recommendations for Multimedia Data

Multimedia data is known for its variety and also for the difficulty that comes in extracting relevant features from multimedia data. Owing to which the collaborative recommendation systems have found their foothold in multimedia recommender systems. However, modern-day multimedia sites have tons of user history in the form of user feedback, reviews, votes, comments, and etc. We can use these social interactions to extract useful content features, which can then be used in content based recommendation system. In this paper, we propose a novel hybrid recommender system that combines the content and collaborative systems using a Bayesian model. We substitute the concrete textual content with a sparse tag information. Extensive experiments on real-world dataset show that tags significantly improves the recommendation performance for multimedia data.
Elevated Road Network

Mobile navigation is a critical component in mobile maps. Yawing detection (does a vehicle yaw) is an important task in mobile navigation. In regions containing parallel and close elevated and surface roads, it is hard to detect yawing events using traditional methods, which mainly rely on low-accuracy positions and moving directions. Recognizing whether a vehicle is moving on an elevated road can significantly improve the performance of yawing detection.
End-to-End Data Commodity Delivery Based on Metadata

By investigating the status of data commodity delivery after the completion of payment transactions by mainstream data trading platforms at home and abroad, this paper proposes an end-to-end data commodity delivery solution based on metadata. Designed and developed a secure, automated and standardized data service API interface specification from the two aspects of data commodity content description and acquisition method, and recorded data products, transactions and delivery process information on the blockchain. Effectively support the registration and authentication of data products, transaction process management, data demanders' understanding and use of purchased data, and market supervision.
Automatic Evaluation of Non-task Oriented Dialog Systems by Using Sentence Embeddings Projections and Their Dynamics

Human-Machine interaction through open-domain conversational agents has considerably grown in the last years. These social conversational agents try to solve the hard task of maintaining a meaningful, engaging and long-term conversation with human users by selecting or generating the most contextually appropriated response to a human prompt. Unfortunately, there is not a well-defined criteria or automatic metric that can be used to evaluate the best answer to provide. The traditional approach is to ask humans to evaluate each turn or the whole dialog according to a given dimension (e.g. naturalness, originality, appropriateness, syntax, engagingness, etc.). In this paper, we present our initial efforts on proposing an explainable metric by using sentence embedding projections and measuring different distances between the human-chatbot, human-human, and chatbot-chatbot turns on two different sets of dialogues. Our preliminary results show insights to visually and intuitively distinguish between good and bad dialogues.
Research and Implementation of Mathematical Model for Solar Running Fixing

This paper mainly expounds the mathematical model and implementation method of the solar shift line positioning. First, the process of sun-line positioning is modeled step by step. Finally, a program is written in Visual Basic language to realize the sun-line positioning based on the data recorded by the driver. A verification program is provided. I hope this article will make it easier to locate the sun's line.
Soap: Soaking Capacity Optimization for Multi-Document Summarization

Multi-document summarization (MDS) aims at giving a brief summary for a cluster of related documents. In this paper, we consider the MDS task as an optimization problem with a novel measure named soaking capacity being the objective function. The origin of our method is the classic hypothesis: the summary components are the sinks of information diffusion. We point out that the hypothesis only gives the role of summary but does not cover how well a summary acts as this role. To fill in the gap, soaking capacity is formally defined to quantify the ability of summary to soak up information. We explicitly demonstrate its fitness as an indicator for both the saliency and the diversity goal of MDS. For solving the optimization problem, we propose a greedy algorithm named Soap by adopting a surrogate of soaking capacity to accelerate the computation. Experiments on MDS datasets across various domains show the great potential of Soap as compared with the state-of-the-art MDS systems.
Order versus Chaos

The positional game of Order versus Chaos can be considered a maker-breaker variant. The players Order and Chaos take turns placing circles or crosses on a board, in which the goal of Order is to create a consecutive line of identical symbols of a certain length, while Chaos aims to prevent this. In this paper, we provide some theoretical results on winning strategies for both players on finite boards of varying sizes, as well as on infinite boards. The composition of these strategies was aided by the use of Monte-Carlo Tree Search (MCTS) players, as well as a SAT solver. In addition to these theoretical results, we provide some more experimental results obtained using MCTS.
Bayesian Non-Parametric Ordinal Regression Under a Monotonicity Constraint

Compared to the nominal scale, the ordinal scale for a categorical outcome variable has the property of making a monotonicity assumption for the covariate effects meaningful. This assumption is encoded in the commonly used proportional odds model, but there it is combined with other parametric assumptions such as linearity and additivity. Herein, the considered models are non-parametric and the only condition imposed is that the effects of the covariates on the outcome categories are stochastically monotone according to the ordinal scale. We are not aware of the existence of other comparable multivariable models that would be suitable for inference purposes. We generalize our previously proposed Bayesian monotonic multivariable regression model to ordinal outcomes, and propose an estimation procedure based on reversible jump Markov chain Monte Carlo. The model is based on a marked point process construction, which allows it to approximate arbitrary monotonic regression function shapes, and has a built-in covariate selection property. We study the performance of the proposed approach through extensive simulation studies, and demonstrate its practical application in two real data examples.
TOMATO: A Topic-Wise Multi-Task Sparsity Model

The Multi-Task Learning (MTL) leverages the inter-relationship across tasks and is useful for applications with limited data. Existing works articulate different task relationship assumptions, whose validity is vital to successful multi-task training. We observe that, in many scenarios, the inter-relationship across tasks varies across different groups of data (i.e., topic), which we call within-topic task relationship hypothesis. In this case, current MTL models with homogeneous task relationship assumption cannot fully exploit different task relationships among different groups of data. Based on this observation, in this paper, we propose a generalized topic-wise multi-task architecture, to capture the within-topic task relationship, which can be combined with any existing MTL designs. Further, we propose a new specialized MTL design, topic-task-sparsity, along with two different types of sparsity constraints. The architecture, combined with the topic-task-sparsity design, constructs our proposed TOMATO model. The experiments on both synthetic and 4 real-world datasets show that our proposed models consistently outperform 6 state-of-the-art models and 2 baselines with improvement from $5%$ to $46%$ in terms of task-wise comparison, demonstrating the validity of the proposed within-topic task relationship hypothesis. We release the source codes and datasets of TOMATO at: https://github.com/JasonLC506/MTSEM.
Leak Detection in Carbon Sequestration Projects Using Machine Learning Methods: Cranfield Site, Mississippi, USA

SUMMARY Due to international commitments on carbon capture and storage (CCS), an increase in CCS projects is expected in the near future. Saline aquifers and depleted hydrocarbon reservoirs with good seals and located in tectonically stable zones make an excellent storage formation option for geological carbon sequestration. However, stored carbon dioxide (CO2) takes a long time to convert into diagenetically stable form. Hence, ensuring the CO2 does not leak from these reservoirs in this time period is the key to any successful CCS project. Numerous methods are developed over the past couple of decades to identify the leaks which utilizes various types of geophysical, geochemical and engineering data. We demonstrate the automated leakage detection in CCS projects using pressure data obtained from Cranfield reservoir, Mississippi, USA. Our dataset consists of CO2 injection rates and pressure monitoring data obtained from a pressure pulse test. We first demonstrate the differences between the pressure pulse signal in case of a baseline pulse test and a pulse test with an artificially induced leak onsite. We then use machine learning techniques to automatically differentiate between the two tests. The results indicate that even simple deep learning architectures such as multi-layer feedforward network (MFNN) can identify a leak using pressure data and can be used to raise an early warning flag.
A compensation-based optimization strategy for top dense layer training

The stochastic gradient descent (SGD) method plays a central role in training deep convolutional neural networks (DCNNs). The recent advances in the field of optimization methods for DCNNs follow the direction of gradients. The innovations mainly lie in adopting different techniques to manage the history of gradients or automatically adapt the step size. In contrast, in this paper we propose a novel optimization approach for training the top dense layer of DCNN. It primarily utilizes the orientation directly pointing to the optimal values of parameters instead of the direction of gradients to navigate the parameters updates. The Moore–Penrose inverse method has been adopted to determine the difference between the current parameters and the optimal parameters, and the parameter updates are driven along this direction to compensate such a difference. Subsequently, the parameters have been fine-tuned along the direction of the classical gradient. Experiments have been conducted on extensively selected benchmark datasets. The results indicate that the proposed approach obtains a higher convergence rate and lower minimum loss compared to other state-of-the-art optimization methods. Furthermore, with the same DCNN architectures, the performance improvement margin between the proposed optimization method and other state-of-the-art optimization approaches is highly significant.
A Lightweight Real-Time Semantic Segmentation Network for Equipment Images in Space Capsule

The combination of semantic segmentation technology and augmented reality technology can provide auxiliary information when astronauts train in augmented reality mode, which will greatly improve the training efficiency and reduce mishandling for astronauts. However, the equipment in space capsule have the characteristics of irregular shape, similar texture and small target while the mixed reality application requires high real-time performance, the above factors bring challenges to the context consistency, accuracy and real-time of semantic segmentation. In response to the challenges, referring to [3], one of the best lightweight real-time segmentation networks, a new network is specially designed for our application. Experimental results show that the designed network can obtain competitive segmentation results on target dataset and better real-time performance than classic networks such as [3]. Overall, the designed network meets the requirement.
Medication Code Recognition using Convolutional Neural Network

In this paper, a new automatic method for expiration code of medical products using convolutional neural network (CNN) is presented. The input image is enhanced using unsharp masking method. Then the image is binarized using local adaptive thresholding technique (LATT) and thinned using morphological operator. Also, characters of the image are extracted using bounding box technique. Finally, a set of characters (A-Z) and digits (0-9) is boiled. The dataset of characters feed an adopted architecture of CNN in order to recognize expiration code of the medication. The proposed approach is tested on large datasets of characters under various conditions of complexities. The experimental results demonstrate the robustness of our approach. The developed system achieves approximately 93% accuracy on character recognition.
Predicting Video Affect via Induced Affection in the Wild

Curating large and high quality datasets for studying affect is a costly and time consuming process, especially when the labels are continuous. In this paper, we examine the potential to use unlabeled public reactions in the form of textual comments to aid in classifying video affect. We examine two popular datasets used for affect recognition and mine public reactions for these videos. We learn a representation of these reactions by using the video ratings as a weakly supervised signal. We show that our model can learn a fine-graind prediction of comment affect when given a video alone. Furthermore, we demonstrate how predicting the affective properties of a comment can be a potentially useful modality to use in multimodal affect modeling.
The correlation filter tracking algorithm using multi-feature background perception

For visual object tracking in the block motion blur deformation background interference and other issues, put forward in combination with characteris-tics of multiple characteristic scale estimate of background perception related filter tracking algorithm, it through in the object area will be the basis of original algorithm and expand, increase extraction Histogram of Oriented Gradient (HOG) and the characteristics of Color Names (CN) to learn more background information filter, improve object localization accuracy. On this basis, the binary matrix is reasonably constructed to improve the effective response of the filter to the object region on the premise of effectively sup-pressing the background, and the size of the object is estimated by using the training scale filter. Experimental and simulation results show that the pro-posed algorithm can solve the problems such as background interference of occlusive motion blur deformation in tracking. In OTB-100 datasets, the ac-curacy and success rate of proposed algorithm are improved by 1.3% and 1.4% respectively. In the background interference sequence of occluding mo-tion blur deformation of OTB-100 datasets, the accuracy of the proposed al-gorithm is 1.9%, 4.0%, 4.3% and 3.4% higher than that of the Backline-Aware Correlation Filters (BACF) algorithm. The FPS can reach 13.7, this result can show that it has high theoretical value and engineering value.
A survey on ‘Apache Storm performance optimization using tuning of parameters’

There are several ways one can use to optimize the performance of Apache Storm. Optimizing the performance of Apache Storm has been a necessary but time-consuming task as there are hundreds of parameters that can be tuned and almost thousands of combinations of possible configurations. So this is a survey of all research work done in the field of real time data processing technologies. The various ways to optimize the performance of Apache Storm used by researchers are parameter tuning, task scheduling, resource scheduling and more. Following is the deep survey of all these approaches and their respective performance optimized.
Machine Learning: A Practical Approach on the Statistical Learning

Machine learning is such a rapidly developing and evolving field, any book which attempts to explain the theory behind it is always welcome.In the subtitle the statistical learning theory is the ke...
Paradigm Shift in Chinese Information Design (2009–2019)

OVERVIEWThe fourth industrial revolution is at a critical period, and changes in information technology have brought about changes in communication methods and media. At present, Mainland China is leading in information technology and is the largest manufacturing economy in the world. Meanwhile, the emerging paradigm of Chinese information design is forming. This research adopts a mixed qualitative and quantitative research methodology and applies case analysis, theoretical deduction and content analysis. One of the objectives of this study is to decode the emerging paradigm of Chinese information design, the other one is to create a set of models to describe the design paradigm. In practice, the understanding of the emerging paradigm can clarify the new development direction of information design for practitioners. In theory, the design paradigm description models can provide researchers with a set of theoretical models for design paradigm research.
Video compression approach for apron surveillance in bad weather

Surveillance occupies an important position in intelligent airport development. However, surveillance video storage puts much pressure on the airport. Apron surveillance video is a common type of airport surveillance video. In this paper, we propose a video compression approach for apron surveillance in bad weather. In this approach, the video storage structure has changed and is divided into three layers: a static layer, an object layer and an environment layer. Such a storage structure can reduce the video storage space. The approach also stores semantic information that can be used in subsequent research. To validate our approach, we conduct experiments on different apron surveillance videos. Experimental results show that the proposed approach can outperform the widely used video coding standards H.264 and MPEG-4 in terms of the SSIM index and PSNR.
Simultaneous Coronal Hole Segmentation from Solar EUV Images using the Image Co-segmentation Technique

Accurate detection of coronal holes (CHs) is an important issue in the domain of solar physics, since, CHs plays an vital role in controlling the overall space weather and generation of solar wind. In this paper a novel application of image co-segmentation (Co-seg) has been proposed for accurate detection of coronal hole simultaneously in two solar disk images of different band. For the task of Co-seg circular Hough transform inspired active contour method (ACM) have been proposed. Here the circular Hough transform has been introduced to setup the initial contours for the ACM based Co-seg. In later stage, the initiated contours has been evolved based on the energy function by incorporating the color features. At the final stage, when the contours do not deform any more is considered as the final contours position and the regions inside the contours is extracted as the CHs regions from both the images under consideration. The experiment has been conducted on few benchmark datasets and the corresponding results have been compared with the results of other existing techniques. The compared outcomes validate the performance of the applied technique in detection of coronal holes in solar disk.
On the Training and Testing Data Preparation for End-to-End Text-to-Speech Application

In the past five years, end-to-end (E2E) text-to-speech (TTS) application has become of interest for many researches related to speech generation from text. This has been the result of the fast-pace development of artificial intelligence and machine learning, not only in image processing, but also in audio and signal processing. Often, the training and testing datasets of TTS applications comprise of thousands of data in forms of text lines and the corresponding audio files which amount up to tens of recording hours. Thus, it is extremely time-consuming for preparing training and testing dataset using manual approach. This work presents an approach for automatic data preparation that is used in Tacotron, Tacotron-2-based Mozilla TTS engine. The well-labeled dataset namely FPT Open Vietnamese Speech Dataset having over 25,000 text lines and recorded audio files is demonstrated in this work. On average, the algorithm takes approximately 45-70 μs to process one text line.
Efficient Imitation Learning for Game AI

Automation testing is an important approach for bug detection and analysis of software applications, especially for computer games. This paper proposes an efficient imitation method to learn game strategy from a small set of manually recorded game samples, which only takes one hour to complete training. This work can be divided into four steps. Firstly, we collect a set of manually recorded data composed of image frames and user actions. The discriminative region is extracted from image to eliminate noise. Then, data alignment is applied to solve the problem of action delay. Due to the large variation of image samples from different classes, data resampling is performed to avoid bias. Finally, these samples are fed into a fast and lightweight network with LSTM structure, which is designed to boost speed of prediction. Our work discards the dependence of game internal interface and performs well in real time with CPU, which has been verified in a variety of commercial games.