Reviewerly/exHarmony huggingface dataset:
Event Factuality Detection in Discourse

Event factuality indicates whether an event occurs or the degree of certainty described by authors in context. Correctly identifying event factuality in texts can contribute to a deep understanding of natural language. In addition, event factuality detection is of great significance to many natural language processing applications, such as opinion detection, emotional reasoning, and public opinion analysis. Existing studies mainly focus on identifying event factuality by the features in the current sentence (e.g. negation or modality). However, there might be many different descriptions of factuality in a document, corresponding to the same event. It leads to conflict when identifying event factuality only on sentence level. To address such issues, we come up with a document-level approach on event factuality detection, which employs Bi-directional Long Short-Term Memory (BiLSTM) neural networks to learn contextual information of the event in sentences. Moreover, we utilize a double-layer attention mechanism to capture the latent correlation features among event sequences in the discourse, and identify event factuality according to the whole document. The experimental results on both English and Chinese event factuality detection datasets demonstrate the effectiveness of our approach. The performances of the proposed system achieved 86.67% and 86.97% of F1 scores, yielding improvements of 3.24% and 4.78% over the state-of-the-art on English and Chinese datasets, respectively.
Can Information Hiding in Social Media Posts Represent a Threat?

A Unicode homoglyph is one of two or more characters with shapes that appear very similar to the human observer. If used in social media posts, homoglyphs allow users to implement an efficient method for hiding text information, turning anyone's post into a potential carrier of hidden messages.
“Did You Hear That?” Learning to Play Video Games from Audio Cues

Game-playing AI research has focused for a long time on learning to play video games from visual input or symbolic information. However, humans benefit from a wider array of sensors which we utilise in order to navigate the world around us. In particular, sounds and music are key to how many of us perceive the world and influence the decisions we make. In this paper, we present initial experiments on game-playing agents learning to play video games solely from audio cues. We expand the Video Game Description Language to allow for audio specification, and the General Video Game AI framework to provide new audio games and an API for learning agents to make use of audio observations. We analyse the games and the audio game design process, include initial results with simple Q-Learning agents, and encourage further research in this area.
Natural necessity: An introductory guide for ontologists

Natural necessity is the kind of necessity that is supplied by ‘nature’: e.g., necessarily, a glass is broken when it is pressed with a certain force. Relevant topics of natural necessity include causation, dispositions, laws of nature, and counterfactuals. Those subjects are vital for a proper ont ological characterization of various entities of scientific inquiry. They are also intimately connected to some scientifically important epistemic notions such as observation, hypothesis, explanation, and prediction. In the field of formal ontology, different notions of natural necessity have been investigated individually to a differing degree and the close interrelationship between them has been little explored. This paper provides an introductory panoramic study of natural necessity in formal ontology to help ontologists utilize it in their actual practice. As for ontology of natural necessity, fundamental commitments to type-level causation, type-level dispositions, and laws of nature are especially discussed. As for its epistemology, close consideration is given to the link between natural necessity and scientific explanation. The practicality of ontology of natural necessity is also illustrated with a case study of suitable primary choices of natural necessity for upper and domain ontologies and its potential application to some domain-specific tasks.
List Decoding Random Euclidean Codes and Infinite Constellations

We study the list decodability of different ensembles of codes over the real alphabet under the assumption of an omniscient adversary. It is a well-known result that when the source and the adversary have power constraints P and N respectively, the list decoding capacity is equal to z log Ñ. Random spherical codes achieve capacity with constant (as a function of the blocklength) list sizes, and the goal of the present paper is to obtain a better understanding of the smallest achievable list size as a function of the gap to capacity. We show a reduction from arbitrary codes to spherical codes, and derive a lower bound on the list size of typical random spherical codes. We also give an upper bound on the list size achievable using nested Construction-A lattices and infinite Construction-A lattices. We then define and study a class of infinite constellations that generalize Construction-A lattices and prove upper and lower bounds for the same. Other goodness properties such as packing goodness and AWGN goodness of infinite constellations are proved along the way. Finally, we consider random lattices sampled from the Haar distribution and show that if a certain number-theoretic conjecture is true, then the list size grows as a polynomial function of the gap-to-capacity.
Bayesian variable selection in non-homogeneous hidden Markov models through an evolutionary Monte Carlo method

Hidden Markov models (HMMs) are dynamic mixture models applied to time series in order to classify the observations into a small number of homogeneous groups, to understand when change points occur, and to model data heterogeneity through the switching between subseries with different state-dependent parameters. In the most general case, HMMs have an unobserved Markov chain whose transition probabilities are time-varying and dependent on exogenous variables through multinomial logit functions. When many covariates are available it is worthwhile selecting the subsets of variables which might affect most each row of the transition matrices. A Bayesian method for the stochastic selection of subsets of covariates is proposed by developing a novel evolutionary Monte Carlo algorithm. The methodology is illustrated and shown to be effective by performing experiments and comparisons on both synthetic data sets and a real multivariate time series with covariates.
Node-Sensitive Graph Fusion via Topo-Correlation for Image Retrieval

Various kinds of features prove to be effective for content-based image retrieval. However, due to the diversity of image contents, a descriptor may achieve impressive performance on specific images while becoming invalid on others. Although some efforts have been made to combine features as complementary counterparts, proper weighting scheme is still a challenge for fast and accurate retrieval. In this paper, we propose an effective fusion method, termed as Topo-correlation (Topo), where the importance of each feature is measured by cross-view correlations on local affinity graphs. Specifically, the weights of similarities are node-sensitive as well as modality-sensitive, thus boosting the results of good cues while depressing adverse factors for individual images. By estimating the consensus of similarity scores with regard to a query-driven criterion, the weighted graphs are generated efficiently with low computational complexity. Extensive experimental results on four benchmarks demonstrate the superiority of the proposed approach over the state-of-the-art methods.
Advanced System of Planning and Optimization of Cargo Delivery and Its IoT Application

This work is aimed at researching and developing a system that not only solves transportation problems with time windows and cargo capacity constraints, but will also take into account and plan real-time workflow events, adhering to which drivers and companies are more profitable and reduce transport costs. The implementation of the proposed multistage hybrid algorithm allows to quickly accomplish goals and hours of service (HOS), for each driver individually, allows to automatically assess the risks and, if necessary, dynamically change, exclude and pick up orders. In addition, an overview of methods and algorithms for transport planning and optimization is carried out. Also, the IoT application of this system of planning and optimization of cargo delivery using electronic registration devices (ELDs) is considered.
Sharing of Vulnerability Information Among Companies – A Survey of Swedish Companies

Software products are rarely developed from scratch and vulnerabilities in such products might reside in parts that are either open source software or provided by another organization. Hence, the total cybersecurity of a product often depends on cooperation, explicit or implicit, between several organizations. We study the attitudes and practices of companies in software ecosystems towards sharing vulnerability information. Furthermore, we compare these practices to contemporary cybersecurity recommendations. This is performed through a questionnaire-based qualitative survey. The questionnaire is divided into two parts: the providers' perspective and the acquirers' perspective. The results show that companies are willing to share information with each other regarding vulnerabilities. Sharing is not considered to be harmful neither to the cybersecurity nor their business, even though a majority of the respondents consider vulnerability information sensitive. However, the companies, despite being open to sharing, are less inclined to proactively sharing vulnerability information. Furthermore, the providers do not perceive that there is a large interest in vulnerability information from their customers. Hence, the companies' overall attitude to sharing vulnerability information is passive but open. In contrast, contemporary cybersecurity guidelines recommend active disclosure and sharing among actors in an ecosystem.
Balancing Turn-Based Games With Chained Strategy Generation

Probabilistic model checking can overcome much of the complexity inherent in balancing games. Game balancing is the careful maintenance of relationships between the ways in which a game can be played, to ensure that no single way is strictly better than all others, and that players are offered a wide variety of ways to play successfully. We introduce a novel approach toward automating game balancing using probabilistic model checking called chained strategy generation (CSG). This involves generating chains of adversarial strategies, which mimic the way players adapt their approach during repeated plays of a game. We use CSG to map out the evolving metagame. The trends identified can allow game developers to identify strategies, which will be too strong, and ways of playing the game, which a player may want to use, but are never viable for successful competitive play. We introduce a case study, a game called RPGLite, and use CSG to compare five candidate configurations for the game. We show how to determine which configurations of RPGLite lead to a more fair and interesting experience for players. We also identify unexpected trends in how the strategies evolve. Our approach introduces a new technique for improving game development and player experience.
Applying Feature‐Based Systems and Software Product Line Engineering in Unclassified and Classified Environments

Abstract Global aerospace and defense companies are reaping the benefits of feature‐based systems and software product line engineering and management (FBPLE) in those situations where production must seamlessly span unclassified and classified environments (Gregg et al. 2014) (Gregg et al. 2015) (Krueger et al. 2014) (Lanman et al. 2011). These benefits include leveraging company talent while awaiting access to classified material; leveraging employees who are members of other sovereign states; and optimizing system production and maintenance for export / import. In this whitepaper we present the architectural design and accompanying business processes for a PLE factory and its artifacts that comprise unclassified and classified digital assets 1 . These digital assets are used in automated generation of unclassified and classified product instances. All production activities occur within a single logical enterprise spanning multiple information systems comprising multiple security zones 2 .
A simple framework to leverage state-of-the-art single-image super-resolution methods to restore light fields

This paper describes a simple framework allowing us to leverage state-of-the-art single image super-resolution (SISR) techniques into light fields, while taking into account specific light field geometrical constraints. The idea is to first compute a representation compacting most of the light field energy into as few components as possible. This is achieved by aligning the light field using optical flow and then by decomposing the aligned light field using singular value decomposition (SVD). The principal basis captures the information that is coherent across all the views, while the other basis contain the high angular frequencies. Super-resolving this principal basis using an SISR method allows us to super-resolve all the information that is coherent across the entire light field. In this paper, to demonstrate the effectiveness of the approach, we have used the very deep super resolution (VDSR) method, which is one of the leading SISR algorithms, to restore the principal basis. The information restored in the principal basis is then propagated to restore all the other views using the computed optical flow. This framework allows the proposed light field super-resolution method to inherit the benefits of the SISR method used. Experimental results show that the proposed method is competitive, and most of the time superior, to recent light field super-resolution methods in terms of both PSNR and SSIM quality metrics, with a lower complexity. Moreover, the subjective results demonstrate that our method manages to restore sharper light fields which enables to generate refocused images of higher quality.
Picking Path Optimization of <i>Agaricus bisporus</i> Picking Robot

The design and application of the mushroom picking robot will greatly reduce the labor cost, and it has become one of the research hotspots in the world. Therefore, we independently developed an A. bisporus (a kind of mushroom) picking robot and introduced its functional principle in this paper. At the same time, in order to improve the picking efficiency of the picking robot, a picking path optimization algorithm based on TSP model is proposed. Firstly, based on the TSP model, a picking route model for A. bisporus was established to determine the storage location of each A. bisporus . Then, an improved simulated annealing (I-SA) search algorithm is proposed to find the optimal path sequence. By improving the path initialization module, path generation module, and temperature drop module, the I-SA search algorithm can optimize the picking path in a short time. Finally, in order to improve the stability and reduce the running time of the I-SA search algorithm, a parallel optimization method for global search (“rough exploration”) and local search (“precision exploration”) is proposed. Through simulation experiments, the I-SA search algorithm can search stable and excellent path solution in a relatively short time. Through field experiments on mushroom base, the efficiency of picking A. bisporus can be improved by 14% to 18%, which verifies the effectiveness of the I-SA search algorithm.
Collaborative Filtering Recommendation Algorithm Based on User Characteristics

This paper proposes a collaborative filtering algorithm based on user characteristics. The algorithm is mainly aimed at the problems of data sparsity and cold start in traditional collaborative filtering algorithm. First, through the analysis of user behavior, the time-interest weight function is introduced to improve the modified cosine similarity formula; then both the user preference degree and trust degree are introduced to improve the accuracy of the recommendation results. Finally, the experimental results based on hetrec2011 dataset show that the accuracy of the recommendation generated by the improved collaborative filtering algorithm is significantly improved compared with the traditional recommendation algorithm. The problems of data sparsity and cold start are effectively alleviated.
Assessing Bayes Factor Surfaces Using Interactive Visualization and Computer Surrogate Modeling

Bayesian model selection provides a natural alternative to classical hypothesis testing based on p-values. While many articles mention that Bayesian model selection can be sensitive to prior specification on parameters, there are few practical strategies to assess and report this sensitivity. This article has two goals. First, we aim to educate the broader statistical community about the extent of potential sensitivity through visualization of the Bayes factor surface. The Bayes factor surface shows the value a Bayes factor takes as a function of user-specified hyperparameters. Second, we suggest surrogate modeling via Gaussian processes to visualize the Bayes factor surface in situations where computation is expensive. We provide three examples including an interactive R shiny application that explores a simple regression problem, a hierarchical linear model selection exercise, and finally surrogate modeling via Gaussian processes to a study of the influence of outliers in empirical finance. We suggest Bayes factor surfaces are valuable for scientific reporting since they (i) increase transparency by making instability in Bayes factors easy to visualize, (ii) generalize to simple and complicated examples, and (iii) provide a path for researchers to assess the impact of prior choice on modeling decisions in a wide variety of research areas. Supplementary materials for this article are available online.
CityTalk: Robots That Talk to Tourists and Can Switch Domains During the Dialogue

The paper describes CityTalk, aWilcock, Graham spoken dialogue system for Nao robots that provide information for tourists about local hotels, shops and restaurants. An important feature is that the robots switch domains smoothly during the dialogue if the user asks about a new domain, for example talking about hotels using a hotel database and then talking about restaurants using a restaurant database if the user asks about restaurants. CityTalk is the first robot dialogue system to use PyDial, a research toolkit for statistical dialogue systems. A topic tracker detects from user utterances when a domain switch is required. Clarification questions appropriate for different domains are based on domain-specific ontologies. CityTalk includes new domains including Tokyo Waterfront restaurants and hotels.
Rectangular domain curl polynomial set for optical vector data processing and analysis

Rectangular pupils are employed in many optical applications such as lasers and anamorphic optics, as well as for detection and metrology systems such as some Shack−Hartmann wavefront sensors and deflectometry systems. For optical fabrication, testing, and analysis in the rectangular domain, it is important to have a well-defined set of polynomials that are orthonormal over a rectangular pupil. Since we often measure the gradient of a wavefront or surface, it is necessary to have a polynomial set that is orthogonal over a rectangular pupil in the vector domain as well. We derive curl (called C) polynomials based on two-dimensional (2-D) versions of Chebyshev polynomials of the first kind. Previous work derived a set of polynomials (called G polynomials) that are obtained from the gradients of the 2-D Chebyshev polynomials. We show how the two sets together can be used as a complete representation of any vector data in the rectangular domain. The curl polynomials themselves or the complete set of G and C polynomials has many interesting applications. Two of those applications shown are systematic error analysis and correction in deflectometry systems and mapping imaging distortion.
Syntharch: Interactive Image Search with Attribute-Conditioned Synthesis

The use of interactive systems has been found to be a promising approach for content-based image retrieval, the task of retrieving a specific image from a database based on its content. These systems allow the user to refine the set of results iteratively until the target is reached. In order to proceed with the search efficiently, conventional methods rely on some shared knowledge between the user and the system, such as semantic visual attributes of the images. Those approaches demand the images to be semantically labeled and introduce a semantic gap between the two parties' understanding. In this paper, we explore an alternative approach to interactive image search where feedback is elicited exclusively in visual forms, therefore eliminating the semantic gap and allowing for a generalized version of the method to operate on unlabeled databases. We present Syntharch, a novel interactive image search approach which uses synthesized images as options for feedback, instead of asking textual questions to gain information on the relative attribute values of the target image. We further demonstrate that by using synthesized images rather than real images retrieved from the database as feedback options, Syntharch causes less confusion to the user. Finally, we establish that our proposed search method performs similarly or better in comparison to the conventional approach.
Freestyle, a randomized version of ChaCha for resisting offline brute-force and dictionary attacks

This paper introduces Freestyle, a randomized, and variable round version of the ChaCha cipher. Freestyle demonstrates the concept of hash based halting condition, where a decryption attempt with an incorrect key is likely to take longer time to halt. This makes it resistant to key-guessing attacks i.e. brute-force and dictionary based attacks. Freestyle uses a novel approach for ciphertext randomization by using random number of rounds for each block of message, where the exact number of rounds are unknown to the receiver in advance. Due to its inherent random behavior, Freestyle provides the possibility of generating up to 2256 different ciphertexts for a given key, nonce, and message; thus resisting key and nonce reuse attacks. This also makes cryptanalysis through known-plaintext, chosen-plaintext, and chosen-ciphertext attacks difficult in practice. Freestyle is highly customizable, making it suitable for both low-powered devices and security-critical applications. It is ideal for: (i) applications that favor ciphertext randomization and resistance to key-guessing and key reuse attacks; and (ii) situations where ciphertext is in full control of an adversary for carrying out an offline key-guessing attack.