Reviewerly/exHarmony huggingface dataset:
High-level Programming via Generalized Planning and LTL Synthesis

We look at program synthesis where the aim is to automatically synthesize a controller that operates on data structures and from which a concrete program can be easily derived. We do not aim at a fully-automatic process or tool that produces a program meeting a given specification of the program’s behaviour. Rather, we aim at the design of a clear and well-founded approach for supporting programmers at the design and implementation phases. Concretely, we first show that a program synthesis task can be modeled as a generalized planning problem. This is done at an abstraction level where the involved data structures are seen as black-boxes that can be interfaced with actions and observations, the first corresponding to the operations and the second to the queries provided by the data structure. The abstraction level is high enough to capture intuitive and common assumptions as well as general and simple strategies used by programmers, and yet it contains sufficient structure to support the automated generation of concrete solutions (in the form of controllers). From such controllers and the use of standard data structures, an actual program in a general language like C++ or Python can be easily obtained. Then, we discuss how the resulting generalized planning problem can be reduced to an LTL synthesis problem, thus making available any LTL synthesis engine for obtaining the controllers. We illustrate the effectiveness of the approach on a series of examples.
VISUALIZING MULTILAYERED GEOSPATIAL DATA IN VIRTUAL REALITY TO ASSESS PUBLIC LIGHTING

Abstract. With the improvement and proliferation of virtual reality devices, their use for research and professional activity is broadening, fostering the advent of the field of immersive analytics, as is their acceptance among consumers. Other than the heightened sense of immersion into visualized data they provide, they also make displays of much larger apparent size and different positioning practical than what would be possible otherwise. Drawing on these benefits, we implemented a development of Multiple and Coordinated Displays (MCVs) for geovisualization that stacks different layers of data above each other, tilted for legibility. In a formal experiment, we evaluated it and two other, comparable MCV methods implemented in VR for their usefulness in analyzing public perception and soliciting public feedback regarding urban street lighting. In that field, the direction has recently been shifting from purely systemic development to a participatory approach, thus our investigation was into how a system like this could facilitate participation that can yield actionable results. Previous analysis of interaction data and usability questionnaires reveals preferences for certain systems depending on user characteristics, with the stack system showing a slight advantage over a grid of layers and especially over temporal multiplexing. We show that regardless of MCV variation, participants were able to analyze and provide feedback on public lighting situations that can directly contribute to urbanist work. The MCV approach further aided in understanding their choices, as eye-tracking allowed us to analyze attention to individual data layers.
Certified Side Channels.

We demonstrate that the format in which private keys are persisted impacts Side Channel Analysis (SCA) security. Surveying several widely deployed software libraries, we investigate the formats they support, how they parse these keys, and what runtime decisions they make. We uncover a combination of weaknesses and vulnerabilities, in extreme cases inducing completely disjoint multi-precision arithmetic stacks deep within the cryptosystem level for keys that otherwise seem logically equivalent. Exploiting these vulnerabilities, we design and implement key recovery attacks utilizing signals ranging from electromagnetic (EM) emanations, to granular microarchitecture cache timings, to coarse traditional wall clock timings.
A study on the embarrassment of senders who missend emojis with opposite meanings on social apps: taking WeChat as an example

Abstract With the increasing popularity of social apps, sending emojis has become a very common way of expressing one’s emotions. However, situations often arise when people send the wrong emoji by mistake, or sometimes even an emoji with an opposite meaning, which can cause embarrassment to the sender. Taking WeChat as an example, which is widely used in Chinese communities, this study summarizes 10 types of dialogue situations in which the meaning of an emoji is wrongly sent and 12 types of emotional components that are related to embarrassment. The purpose of this study was to analyze the extent to which the 12 emotional components that are associated with embarrassment actually explain what embarrassment is, as well as the different degrees of embarrassment among the different genders and age groups. The results showed that (1) among the emotional components of embarrassment, shame has the highest explanation degree for embarrassment; (2) males are more likely to be affected by embarrassment than females; and (3) users aged 18–25 and 26–30 years are more likely to be affected by embarrassment than those aged between 31 and 40 when they mistakenly send WeChat emojis. This study provides a reference value for their sustainable psychological impact on social app users.
Bayesian Parameter Inference of Explosive Yields Using Markov Chain Monte Carlo Techniques

A Bayesian parameter inference problem is conducted to estimate the explosive yield of the first atomic explosion at Trinity in New Mexico.The first of its kind, the study advances understanding of fireball dynamics and provides an improved method for the determination of explosive yield.Using fireball radius-time data taken from archival film footage of the explosion and a physical model for the expansion characteristics of the resulting fireball, a yield estimate is made.Bayesian results from the Markov chain indicate that the estimated parameters are consistent with previous calculation except for the critical parameter that modifies the independent time variable.This unique result finds that this parameter deviates in a statistically significant way from previous predictions.Use of the Bayesian parameter estimates computed is found to greatly improve the ability of the fireball model to predict the observed data.In addition, parameter correlations are computed from the Markov chain and discussed.As a result, the method used increases basic understanding of fireball dynamics and provides an improved method for the determination of explosive yields.
Event Extraction via DMCNN in Open Domain Public Sentiment Information

Event extraction (EE) is a difficult task in natural language processing (NLP). The target of EE is to obtain and present key information described in natural language in a structured form. Internet opinion, as an essential bearer of social information, is crucial. In order to help readers quickly get the main idea of news, a method of analyzing public sentiment information on the Internet and extracting events from news information is proposed. It enables users to quickly obtain information they need. An event extraction method was proposed based on Chinese language public opinion information, aiming at automatically classifying different types of public opinion events by using sentence-level features, and neural networks were applied to extract events. A sentence feature model was introduced to classify different types of public opinion events. To ensure the effective retention of text information in the calculation process, attention mechanism was added to the semantic information, and an effective public opinion event extractor was trained through CNN and LSTM networks. Experiments show that structured information can be extracted from unstructured text, and the purpose of obtaining public opinion event entities, event-entity relationships, and entity attribute information can be achieved.
Data Fusion Algorithm Based on Fuzzy Similarity Weighted Least Square for Positioning with the Global Positioning System

The Global Positioning System, GPS customs solutions to determine the coordinates of the GPS receiver location and the receiver clock offset from data extracted from at least four pseudoranges. The constancy and accuracy are essential requirements in positioning calculation. The Least Squares, LS estimate has been widely used for solving GPS positioning problems. Aside its valuable properties, the LS estimate can be affected by outliers which reflect to its performance in terms of accuracy. In this paper, a new approach is applied to LS estimate to increase its accuracy and reliability. Assuming six or more satellites are observed. First, several sets of measurements are formed by making all possible combinations of observed satellites at least five satellites in each set. Second, the LS estimate approach is applied for each set of measurement to estimate the receiver position. A cluster of each set of measurements is obtained and its statistical properties mean and standard deviation are computed. Grubbs’s outlier algorithm is applied to all clusters to find the outlier measurements. The fusion of position data set is based on the fuzzy similarity between the sets of cluster position where the importance weight of each set of data is extracted. According to the proposed algorithm, software is developed using MATLAB. The proposed algorithm is tested, and the position accuracy is improved. Moreover, it reflects the efficiency and feasibility to real-time data processing and monitoring
Research on Risk Assessment of Port Project Investment and Construction

Xu, X.; Yang, Z.; Jin, L., and Hao, L., 2020. Research on risk assessment of port project investment and construction. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 187-189. Coconut Creek (Florida), ISSN 0749-0208.Compared with general engineering projects, port engineering projects have large investment scale, long cycle, uncertain natural environment in which the port is located, and diversified potential influencing factors, which are all sources of risk for port engineering projects. With the increasing trend of economic globalization and integration, ports are playing an increasingly important role in stimulating regional economy and local economy. However, because port construction is a capital-intensive industry with a long payback period, investment in port projects has to bear greater risks. The construction and operation of port projects often have a significant impact on the local economic and social system and the existing space and natural environment. Based on the introduction of the characteristics and causes of the investment risks of port construction projects, this paper analyzes the potential risk factors of port construction project investment in China, and gives suggestions for the risk evaluation of port construction project investment combined with the characteristics of the investment risks of port construction projects.
Research on Speech Signal Denoising Algorithm Based on Wavelet Analysis

Abstract In this paper, wavelet analysis is used to remove noise superimposed on the speech signal to achieve denoising of the speech signal. The prepared speech signal is superimposed with Gaussian white noise with different signal-to-noise ratios, and is removed by using a forced noise removal processing method and a given threshold denoising processing method; The wavelet soft and hard threshold denoising method is used to separately superimpose different Gaussian white noise speech signals, and the hard threshold and soft threshold denoising are respectively performed, and different wavelets are used for processing; Four threshold selection methods are used to denoise the noisy signal using different wavelets. The denoising effects of these methods are simulated in MATLAB software, and combined with the signal-to-noise ratio of the signal after denoising, the denoising effects of various methods are judged. Experimental results show that soft threshold denoising is slightly better than hard threshold denoising. Among the four threshold selection methods, Rigrsure rule denoising is the best. Although other methods are effective, they are not obvious.
From Discourse to Logic with Stanford CoreNLP and Treebank Semantics

This paper describes combining the parsing of Stanford CoreNLP with the transformations of Treebank Semantics to realise a system for taking raw text as input to reach logical representation output. The analysis converts tree content into the structures of a formal language which is then processed against a locally constrained global calculation. The calculation resolves the interpretation, including the accessibility of antecedents for pronouns and definites, but, most in particular, it is the system that decides predicate valency through self-regulation of the calculation to ensure results from a minimum of explicit input.
Simulating shot noise of color underwater images

This paper considers methods for simulating color underwater images based on real terrestrial images. Underwater image simulation is widely used for developing and testing methods for improving underwater images. A large group of existing methods uses the same deterministic image transformation model ignoring the presence of noise in images. The paper demonstrates that this significantly affects the overall quality of underwater images simulation. It is shown both theoretically and numerically that the accuracy of the signal-to-noise ratio of underwater images simulated using a deterministic transformation decreases with increasing distance to the object. To solve this problem, a new model of image transformation for simulating underwater images based on terrestrial images is proposed, which considers the presence of noise in the image and is compatible with all simulating methods from the group under consideration. The paper presents the results of the simulation based on the existing and proposed models, showing that at long distances, the new results are better consistent with real data.
Facial Expression Recognition Method Based on Convolution Neural Network Combining Attention Mechanism

Facial expression recognition is one of the research hotspots in the field of computer vision. Aiming at the problems that the current machine learning method extracts facial features are less robust and the traditional convolutional neural network can not fully extract the expression features, a residual network model integrating CBAM attention mechanism is proposed. Given an intermediate feature map, a attention map is generated on the channel domain and the spatial domain of the feature map respectively by our module, and multiplied by the original feature map to obtain a recalibrated feature map. In the training process, the improved loss function A-Softmax is used to generate the angular interval by manipulating the feature surface, so that the different class features learned have angular intervals in the angular space, and the similar features are more closely clustered. Experiments on FER2013 and JAFFE dataset show that the proposed method effectively improves the feature expression ability of the network, enhances the ability to distinguish different facial expression features, and achieves good recognition performance.
HFF: Hybrid Feature Fusion Model for Click-Through Rate Prediction

Deep neural network (DNN) which is applied to extract high-level features plays an important role in the Click Through Rate (CTR) task. Although the necessity of high-level features has been recognized, how to integrate high-level features with low-level features has not been studied well. There are some works fuse low- and high-level features by simply sum or concatenation operations. We argue it is not an effective way because they treat low- and high-level features equally. In this paper, we propose a novel hybrid feature fusion model named HFF. HFF model consists of two different layers: feature interaction layer and feature fusion layer. With feature interaction layer, our model can capture high-level features. And the feature fusion layer can make full use of low- and high-level features. Comprehensive experiments on four real-world datasets are conducted. Extensive experiments show that our model outperforms existing the state-of-the-art models.
Integration of Diffractive Optical Neural Networks with Electronic Neural Networks

We demonstrate jointly-trained hybrid (optical-electronic) networks, where diffractive optical neural networks act as a trainable front-end mitigating the information loss at a low-resolution image sensor to improve the overall classification accuracy of the hybrid network.
EgoCom: A Multi-Person Multi-Modal Egocentric Communications Dataset

Multi-modal datasets in artificial intelligence (AI) often capture a third-person perspective, but our embodied human intelligence evolved with sensory input from the egocentric, first-person perspective. Towards embodied AI, we introduce the Egocentric Communications (EgoCom) dataset to advance the state-of-the-art in conversational AI, natural language, audio speech analysis, computer vision, and machine learning. EgoCom is a first-of-its-kind natural conversations dataset containing multi-modal human communication data captured simultaneously from the participants' egocentric perspectives. EgoCom includes 38.5 hours of synchronized embodied stereo audio, egocentric video with 240,000 ground-truth, time-stamped word-level transcriptions and speaker labels from 34 diverse speakers. We study baseline performance on two novel applications that benefit from embodied data: (1) predicting turn-taking in conversations and (2) multi-speaker transcription. For (1), we investigate Bayesian baselines to predict turn-taking within 5 percent of human performance. For (2), we use simultaneous egocentric capture to combine Google speech-to-text outputs, improving global transcription by 79 percent relative to a single perspective. Both applications exploit EgoCom's synchronous multi-perspective data to augment performance of embodied AI tasks.
Balanced multi-image demons for non-rigid registration of magnetic resonance images

A new approach is introduced for non-rigid registration of a pair of magnetic resonance images (MRI). It is a generalization of the demons algorithm with low computational cost, based on local information augmentation (by integrating multiple images) and balanced implementation. Specifically, a single deformation that best registers more pairs of images is estimated. All these images are extracted by applying different operators to the two original ones, processing local neighbors of each pixel. The following five images were found to be appropriate for MRI registration: the raw image and those obtained by contrast-limited adaptive histogram equalization, local median, local entropy and phase symmetry. Thus, each local point in the images is supplemented by augmented information coming by processing its neighbor. Moreover, image pairs are processed in alternation for each iteration of the algorithm (in a balanced way), computing both a forward and a backward registration. The new method (called balanced multi-image demons) is tested on sagittal MRIs from 10 patients, both in simulated and experimental conditions, improving the performances over the classical demons approach with minimal increase of the computational cost (processing time around twice that of standard demons). Specifically, a simulated deformation was applied to the MRIs (either original or corrupted by additive Gaussian or speckle noises). In all tested cases, the new algorithm improved the estimation of the simulated deformation (squared estimation error decreased by about 65% in the average). Moreover, statistically significant improvements were obtained in experimental tests, in which different brain regions (i.e., brain, posterior fossa and cerebellum) were identified by the atlas approach and compared to those manually delineated (in the average, Dice coefficient increased of about 6%). The conclusion is that a balanced method applied to multiple information extracted from neighboring pixels is a low cost approach to improve registration of MRIs.
Multi-Stage Dense CNN Demosaicking With Downsampling and Re-Indexing Structure

Multi-stage CNN is composed of several networks and loss functions. Compared to a normal single-stage CNN, a multi-stage CNN can control the processing in each network. In image demosaicking, it is utilized to follow the flow of conventional non-CNN demosaicking algorithms. This paper proposes a 4-stage CNN demosaicking method. The first stage obtains a pre-estimated full color image from an input Bayer image instead of non-CNN pre-processing. The second stage processes the color planes independently while the third stage processes the combination for two colors. The final stage obtains the full-color outputs from all outputs of the former stages. To achieve better performance, the proposed method introduces dense structures for both inside and outside of every network. Another contribution is dealing with the discontinuity between a pixel and its neighboring pixels in Bayer images by a downsampling and re-indexing structure for feature maps. These contributions lead to higher accuracy and prevent false colors. Some results through demosaicking experiments show that the proposed method can achieve better quality images with less false colors and better PSNR than state-of-the-art CNN demosaicking methods.
Alternative Mutation Operators in Collaborative Neurodynamic Optimization

Collaborative neurodynamic optimization (CNO) is a global search technique that integrates the neurodynamic optimization with a swarm intelligence algorithm. Diversity is a key point for global optimization. Mutation operator is employed in CNO to ensure diversity. In the existing literature, the diversification performance of different mutation operators is unknown. In this paper, four mutation operators, Gaussian, Cauchy, Lévy and wavelet mutations, are analyzed to compare the performances of mutation operators. Simulation results on four benchmark multimodal functions are discussed.
Scalable Writing Pedagogy for Strengthening Cohesion with Interactive Visualization

The current pedagogy on cohesion in writing relies primarily on traditional static charts to depict topical connections across sentences. However, since charts are manually generated, they are not practical for inspecting a text during the revision process. We have developed a visualization tool called OnTopic, designed to guide students' attention to their composing choices that support coherent writing. OnTopic uses natural language processing algorithms to dynamically visualize the topical structure of the student's draft and thereby help students' confront and learn to account for their composing decisions. This paper reports on a pilot study that evaluated OnTopic in a series of writing workshops. The results suggest that most students were able to successfully assess their writing from the perspective of topical structure, and develop a plan for specific revisions.
The Use of HPT Model to Analyze the Issue of Public School Teacher Turnover in China

The issue of teacher turnover in public schools is becoming more and more concerned. Teacher turnover not only means that the quality of teaching is not guaranteed, but also greatly weakens the competitiveness of public schools. This paper analyzed the issue by using HPT model from the perspective of performance technology. The analysis showed that environmental factors and personal factors accounted for the loss of teachers, and there were corresponding sub-factors under each factor. In the light of those factors, five interventions were designed to improve the performance of teacher turnover. The application of evaluation ensured the effectiveness and sustainability of interventions.