Reviewerly/exHarmony huggingface dataset:
sthruggle at SemEval-2019 Task 5: An Ensemble Approach to Hate Speech Detection

In this paper, we present our approach to detection of hate speech against women and immigrants in tweets for our participation in the SemEval-2019 Task 5. We trained an SVM and an RF classifier using character bi-and trigram features and a BiLSTM pre-initialized with external word embeddings.We combined the predictions of the SVM, RF and BiLSTM in two different ensemble models.The first was a majority vote of the binary values, and the second used the average of the confidence scores.For development, we got the highest accuracy (75%) by the final ensemble model with majority voting.For testing, all models scored substantially lower and the scores between the classifiers varied more.We believe that these large differences between the higher accuracies in the development phase and the lower accuracies we obtained in the testing phase have partly to do with differences between the training, development and testing data.
Transmission of Still Images Using Low-Complexity Analog Joint Source-Channel Coding

An analog joint source-channel coding (JSCC) system designed for the transmission of still images is proposed and its performance is compared to that of two digital alternatives which differ in the source encoding operation: Joint Photographic Experts Group (JPEG) and JPEG without entropy coding (JPEGw/oEC), respectively, both relying on an optimized channel encoder–modulator tandem. Apart from a visual comparison, the figures of merit considered in the assessment are the structural similarity (SSIM) index and the time required to transmit an image through additive white Gaussian noise (AWGN) and Rayleigh channels. This work shows that the proposed analog system exhibits a performance similar to that of the digital scheme based on JPEG compression with a noticeable better visual degradation to the human eye, a lower computational complexity, and a negligible delay. These results confirm the suitability of analog JSCC for the transmission of still images in scenarios with severe constraints on power consumption, computational capabilities, and for real-time applications. For these reasons the proposed system is a good candidate for surveillance systems, low-constrained devices, Internet of things (IoT) applications, etc.
LaSTUS/TALN at SemEval-2019 Task 6: Identification and Categorization of Offensive Language in Social Media with Attention-based Bi-LSTM model

This paper describes a bidirectional Long-Short Term Memory network for identifying offensive language in Twitter.Our system has been developed in the context of the Se-mEval 2019 Task 6 which comprises three different sub-tasks, namely A: Offensive Language Detection, B: Categorization of Offensive Language, C: Offensive Language Target Identification.We used a pre-trained Word Embeddings in tweet data, including information about emojis and hashtags.Our approach achieves good performance in the three subtasks.
Benchmarking multivariate solvers of SciPy on the noiseless testbed

In this article we benchmark eight multivariate local solvers as well as the global Differential Evolution algorithm from the Python SciPy library on the BBOB noiseless testbed. We experiment with different parameter settings and termination conditions of the solvers. More focus is given to the L-BFGS-B and Nelder-Mead algorithms. For the first we investigate the effect of the maximum number of variable metric corrections used for the Hessian approximation and show that larger values than the default are of advantage. For the second we investigate the effect of adaptation of parameters, which is proved crucial for the performance of the method with increasing dimensionality.
Sistem Pakar Untuk Mengidentifikasi Jenis Kulit Wajah dengan Metode Certainty Factor

&#x0D; The role of facial beauty experts is vital for identifying the kind of facial skin as well as providing the right treatment solution for every kind of facial skin. From this, an expert system is needed to help provide solutions by building an expert system that can identify the type of facial skin by including treatment solutions. The Certainty Factor method makes a selection based on consideration in the calculation process, and this method looks for the highest combination of trust values. At the beginning of the study data collection was carried out on 40 female respondents obtained 100% of respondents did not understand the type of facial skin and 76% said they needed experts, 95% required an expert system application. While the results of the assessment of the application system that had been built by respondents stated that 88% of the system design was excellent and very good 91% of the system was easy to use, and 98% said the operation was by what was needed.
Structure Fusion Based on Graph Convolutional Networks for Node Classification in Citation Networks

Suffering from the multi-view data diversity and complexity, most of the existing graph convolutional networks focus on the networks’ architecture construction or the salient graph structure preservation for node classification in citation networks and usually ignore capturing the complete graph structure of nodes for enhancing classification performance. To mine the more complete distribution structure from multi-graph structures of multi-view data with the consideration of their specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of node classification in a semi-supervised way. SF-GCN can not only exploit the special characteristic of each view datum by spectral embedding preserving multi-graph structures, but also explore the common style of multi-view data by the distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures; we can construct the optimization function of the structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as the adjacent matrix to input graph convolutional networks for node classification in a semi-supervised way. Furthermore, we generalize the structure fusion to structure diffusion propagation and present structure propagation fusion based on graph convolutional networks (SPF-GCN) for utilizing these structure interactions. Experiments demonstrate that the performance of SPF-GCN outperforms that of the state-of-the-art methods on three challenging datasets, which are Cora, Citeseer, and Pubmed in citation networks.
Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

Although fashion-related products account for most of the online shopping categories, it becomes more difficult for users to search and find products matching their taste and needs as the number of items available online increases explosively. Personalized recommendation of items is the best method for both reducing user effort on searching for items and expanding sales opportunity for sellers. Unfortunately, experimental studies and research on fashion item recommendation for online shopping users are lacking. In this paper, we propose a novel recommendation framework suitable for online apparel items. To overcome the rating sparsity problem of online apparel datasets, we derive implicit ratings from user log data and generate predicted ratings for item clusters by user-based collaborative filtering. The ratings are combined with a network constructed by an item click trend, which serves as a personalized recommendation through a random walk. An empirical evaluation on a large-scale real-world dataset obtained from an apparel retailer demonstrates the effectiveness of our method.
PKUSE at SemEval-2019 Task 3: Emotion Detection with Emotion-Oriented Neural Attention Network

This paper presents the system in SemEval-2019 Task 3, "EmoContext: Contextual Emotion Detection in Text".We propose a deep learning architecture with bidirectional LSTM networks, augmented with an emotionoriented attention network that is capable of extracting emotion information from an utterance.Experimental results show that our model outperforms its variants and the baseline.Overall, this system has achieved 75.57% for the microaveraged F1 score.
ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN

In this paper we present our model on the task of emotion detection in textual conversations in SemEval-2019.Our model extends the Recurrent Convolutional Neural Network (RCNN) by using external fine-tuned word representations and DeepMoji sentence representations.We also explored several other competitive pre-trained word and sentence representations including ELMo, BERT and InferSent but found inferior performance.In addition, we conducted extensive sensitivity analysis, which empirically shows that our model is relatively robust to hyper-parameters.Our model requires no handcrafted features or emotion lexicons but achieved good performance with a micro-F1 score of 0.7463.
ВИКОРИСТАННЯ ІКТ ДЛЯ ФОРМУВАННЯ ФАХОВИХ КОМПЕТЕНТНОСТЕЙ У МАЙБУТНІХ УЧИТЕЛІВ УКРАЇНСЬКОЇ МОВИ І ЛІТЕРАТУРИ

The article deals with the problem of the use of ICT means for the formation of professional competences of future teachers of the Ukrainian language and literature. The relevance of using e-learning resources in the system of vocational education of students-philologists is investigated. The essence of the concept of "professional competence of the teacher of the Ukrainian language and literature" is specified. The state of the use of ICT in the professional teaching of bachelors and masters of Ukrainian philology based on the analysis of the works of scientists, the synthesis of educational and methodical ideas, the generalization of their own pedagogical experience, questioning of students is determined; the experience of effective work of students of philology with ICT means in the context of vocational education is considered. The analysis of the use of e-learning tools for the mastery of Ukrainian philology by future teachers-speakers is carried out. The results of the survey and questioning of students-philologists concerning the activity and efficiency of using ICT tools in the professional training are described. It is empirically confirmed that the integration of ICT means into the traditional methodology of teaching the professional disciplines to future teachers of the Ukrainian language and literature makes it possible to improve the attitude of students towards vocational education. It is provided that the blog of the Ukrainian Language Cultural Center at Kyiv Borys Hrinchenko University is a significant educational potential for the professional training of students of philology since it is an important modern tool for maintaining new ways of teaching the language and it contains accessible electronic resources that are useful for the professional growth of future language teachers and affects educational activity of students positively. This article focuses on the educational potential of the Facebook community of the mentioned Center which brings professional education closer to the student, helps him to study, communicates in the Internet, and acquires the ability to continuous education despite the distance between the members of this Facebook community. It is generalized that active activities in the environment of the Facebook community of the Center develops the skills of critical thinking, communication in the virtual space, emotional intelligence, ability to process and structure significant information of the Ukrainian-speaking direction. The prospects of research on the use of ICT means for the professional training of the teacher of the Ukrainian language and literature have been determined.
An information entropy interpretation of photon absorption by dielectric media

We measured photon absorption in dielectric media and proposed the photon-version Beer-Lambert's law to quantify the absorption. We used a Hong-Ou-Mandel interferometer and 810 nm twin-photons. We found that the depth ratio of the null point in the interference patterns of the interferometer agreed with the classical transmittance of the samples. We established a statistical model of the photon absorption process and proposed an information entropy interpretation to understand the meaning of the Beer-Lambert law. Comparisons of the results of the photon absorption experiments with classical experiments demonstrate the validity of our model and interpretation.
IMPACT OF ARTIFICIAL INTELLIGENCE ON AGRICULTURAL, HEALTHCARE AND LOGISTICS INDUSTRIES

This qualitative research study was conducted to illustrate the relationships between Artificial Intelligence (AI) and non-tech businesses. AI is a broad branch of computer science. In information technology, the intelligent machine is a compliant and logical agent that recognizes its environment and takes full advantage of opportunities to achieve something. This paper provides detailed examples using AI outside of IT. The main method which is used for this research is literary analysis. The article consists of explanations about artificial intelligence in general, its impacts on logistics and transportation, agriculture and healthcare industries. Moreover, in this article, the methods used to leverage the developments of aforementioned industries are also mentioned and discussed.
Multi-Threshold Image Segmentation Based on Improved Grey Wolf Optimization Algorithm

Threshold selection is the key to image threshold segmentation. The threshold determines the good or bad of the image segmentation results. As the number of thresholds increases, the computational process of image segmentation becomes more and more complicated. In order to select a better threshold for image segmentation, an improved Grey Wolf Optimization (IGWO) algorithm is proposed in this paper. The good point set method is applied to the GWO to generate the initial population, the weight is introduced in the position update of gray wolf hunting process, and the algorithm is used to solve the global optimization problem with the Kapur partition function as the objective function. The IGWO algorithm has good global convergence and computational robustness, can effectively avoid falling into local optimum, and is especially suitable for solving high-dimensional and multi-peak complex function problems, and can be well integrated into the image segmentation process. The results of theoretical analysis and simulation experiments show that compared with the image segmentation results of particle swarm optimization (PSO), the algorithm has better segmentation effect when multiple segmentation thresholds are selected. The segmentation efficiency, the threshold range obtained by optimization is more stable, and the segmentation quality is higher.
A Selective Biogeography-Based Optimizer Considering Resource Allocation for Large-Scale Global Optimization

Biogeography-based optimization (BBO), a recent proposed metaheuristic algorithm, has been successfully applied to many optimization problems due to its simplicity and efficiency. However, BBO is sensitive to the curse of dimensionality; its performance degrades rapidly as the dimensionality of the search space increases. In this paper, a selective migration operator is proposed to scale up the performance of BBO and we name it selective BBO (SBBO). The differential migration operator is selected heuristically to explore the global area as far as possible whist the normal distributed migration operator is chosen to exploit the local area. By the means of heuristic selection, an appropriate migration operator can be used to search the global optimum efficiently. Moreover, the strategy of cooperative coevolution (CC) is adopted to solve large-scale global optimization problems (LSOPs). To deal with subgroup imbalance contribution to the whole solution in the context of CC, a more efficient computing resource allocation is proposed. Extensive experiments are conducted on the CEC 2010 benchmark suite for large-scale global optimization, and the results show the effectiveness and efficiency of SBBO compared with BBO variants and other representative algorithms for LSOPs. Also, the results confirm that the proposed computing resource allocation is vital to the large-scale optimization within the limited computation budget.
Unfocused plenoptic metric modeling and calibration

For unfocused plenoptic imaging systems, metric calibration is generally mandatory to achieve high-quality imaging and metrology. In this paper, we present an explicit derivation of an unfocused plenoptic metric model associating a measured light field in the object space with a recorded light field in the image space to conform physically to the imaging properties of unfocused plenoptic cameras. In addition, the impact of unfocused plenoptic imaging distortion on depth computation was experimentally explored, revealing that radial distortion parameters contain depth-dependent common factors, which were then modeled as depth distortions. Consequently, a complete unfocused plenoptic metric model was established by combining the explicit metric model with the imaging distortion model. A three-step unfocused plenoptic metric calibration strategy, in which the Levenberg-Marquardt algorithm is used for parameter optimization, is correspondingly proposed to determine 12 internal parameters for each microlens unit. Based on the proposed modeling and calibration, the depth measurement precision can be increased to 0.25 mm in a depth range of 300 mm, ensuring the potential applicability of consumer unfocused plenoptic cameras in high-accuracy three-dimensional measurement.
Sentence Similarity Computation in Question Answering Robot

Abstract Computing semantic similarity between sentences or texts is vital in many natural language processing (NLP) tasks such as search, query suggestion, and question answering (QA). Many methods have been developed, based on lexical matching, distributional semantics, etc. However, lexical features, like string matching, fail to capture semantic similarity. In this research, our focus lies on the implementation of distributional representations and how to tune parameters when obtaining representations of words with commonly used word embedding techniques, e.g., Word2Vec and GloVe. We conduct experiments in the setting of Chinese semantic sentence matching tasks on the finance-domain. We examine the goodness of word embedding by both the cosine similarity of semantically similar sentence pairs and semantically dissimilar pairs. Based on our experiments, Word2Vec performs better than GloVe in the sense that Chinese character embedding from Word2Vec yield larger disparity of cosine distances between similar sentence pairs and dissimilar pairs. Also we report the optimal parameters for Word2Vec continuous bag-of-word (CBOW) through our trials, with window size being 6 and embedding dimension being 400, which can be good initial values for other projects.
Pri-RTB: Privacy-preserving real-time bidding for securing mobile advertisement in ubiquitous computing

Real-time bidding (RTB), one of the major trading mechanisms used for online advertising, allows the advertiser to make an impression-level bid decision. However, the security and privacy concerns of RTB are gaining increasing attention with the recent enforcement of the European Union General Data Protection Regulation (GDPR). In this study, we present a novel privacy-preserving RTB (Pri-RTB) protocol, which aims at preserving both data privacy and utility by performing additively homomorphic encryption on the user profile. To our knowledge, this is the first work to address the privacy issue of the RTB paradigm. We present a formal proof for the security of Pri-RTB under the assumption of the decisional Diffie–Hellman (DDH) problem. We analyze and elaborate the superiority of Pri-RTB over other related works. We also developed a prototype for Pri-RTB and conducted several experiments to evaluate its feasibility and efficiency under different parameters. Our experiments demonstrate that Pri-RTB can work highly efficiently in a practical setting (for example, a scenario with approximately 200 advertisers per auction).
UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

This paper proposes an improved image high fidelity compression algorithm based on the generative adversarial networks (GANs) to deal with the problem that the UAV image has a large amount of data which is not conducive to post-processing.By adding an encoder in front of the generator, the disaster area image transmitted by UAV is compressed to meet the requirements of the generator.After the compressed image is trained together with the real image through the discriminator, the quality of the compressed image is constantly improved.This image compression algorithm can fully synthesize the codes of non-major areas such as trees and rivers in the image, and try to retain the codes of important areas such as houses and roads.The experimental results show that the proposed compression method in this paper has a higher compression ratio than the traditional compression method for the disaster area image, and can obtain images with strong sense of hierarchy.
Goal Recognition Design in Deterministic Environments

Goal recognition design (GRD) facilitates understanding the goals of acting agents through the analysis and redesign of goal recognition models, thus offering a solution for assessing and minimizing the maximal progress of any agent in the model before goal recognition is guaranteed. In a nutshell, given a model of a domain and a set of possible goals, a solution to a GRD problem determines (1) the extent to which actions performed by an agent within the model reveal the agent’s objective; and (2) how best to modify the model so that the objective of an agent can be detected as early as possible. This approach is relevant to any domain in which rapid goal recognition is essential and the model design can be controlled. Applications include intrusion detection, assisted cognition, computer games, and human-robot collaboration.&#x0D; A GRD problem has two components: the analyzed goal recognition setting, and a design model specifying the possible ways the environment in which agents act can be modified so as to facilitate recognition. This work formulates a general framework for GRD in deterministic and partially observable environments, and offers a toolbox of solutions for evaluating and optimizing model quality for various settings. &#x0D; For the purpose of evaluation we suggest the worst case distinctiveness (WCD) measure, which represents the maximal cost of a path an agent may follow before its goal can be inferred by a goal recognition system. We offer novel compilations to classical planning for calculating WCD in settings where agents are bounded-suboptimal. We then suggest methods for minimizing WCD by searching for an optimal redesign strategy within the space of possible modifications, and using pruning to increase efficiency. We support our approach with an empirical evaluation that measures WCD in a variety of GRD settings and tests the efficiency of our compilation-based methods for computing it. We also examine the effectiveness of reducing WCD via redesign and the performance gain brought about by our proposed pruning strategy.