Reviewerly/exHarmony huggingface dataset:
SIS@IIITH at SemEval-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis

Memes are steadily taking over the feeds of the public on social media.There is always the threat of malicious users on the internet posting offensive content, even through memes.Hence, the automatic detection of offensive images/memes is imperative along with detection of offensive text.However, this is a much more complex task as it involves both visual cues as well as language understanding and cultural/context knowledge.This paper describes our approach to the task of SemEval-2020 Task 8: Memotion Analysis.We chose to participate only in Task A which dealt with Sentiment Classification, which we formulated as a text classification problem.Through our experiments, we explored multiple training models to evaluate the performance of simple text classification algorithms on the raw text obtained after running OCR on meme images.Our submitted model achieved an accuracy of 72.69% and exceeded the existing baseline's Macro F1 score by 8% on the official test dataset.Apart from describing our official submission, we shall elucidate how different classification models respond to this task.
Italian Transformers Under the Linguistic Lens

In this paper we describe the early stage application of the Universal Dependencies to an Italian corpus from social media developed for shared tasks related to irony and stance detection.The development of this novel resource (TWITTIR Ò-UD) serves a twofold goal: it enriches the scenario of treebanks for social media and for Italian, and it paves the way for a more reliable extraction of a larger variety of morphological and syntactic features to be used by sentiment analysis tools.On the one hand, social media texts are especially hard to parse and the limited amount of resources for training and testing NLP tools further damages the situation.On the other hand, we thought that adding the Universal Dependencies format to the fine-grained annotation for irony, that was previously applied on TWITTIR Ò, might meaningfully help in the investigation of possible relationships between syntax and semantics of the uses of figurative language, irony in particular.
Sistem Pengecekan Toko Online Asli atau Dropship pada Shopee Menggunakan Algoritma Breadth First Search

The abundance of online shops on web ecommerce Shopee's makes it difficult for consumers to detect the genuineness of an online store. Online store detection app is an application to detect genuine online shop, good rating online shop, and fake online shop, which is useful as a recommendation to consumers in buying a product against an online shop. Detection by implementing the Breadth First Search (BFS) algorithm with Web Scraping techniques against Web e-commerce Shopee with the keyword "Kemeja Pria" with a search number of 5000, generate 1389 online shops data with the detection results of genuine online shop, good rating online shop, and fake online shop respectively as many as 90 online shops (6.5%), 948 online shops (68.3%), and 351 online shops (25.3%). The time it takes for the system to visit each node, from the first node to the 1389 node by applying the Breadth First Search algorithm takes about 2,690,021 second, or about 44.833683333 minutes, with the queue formation process, node browsing, online shop detection, and test results. The results reveal that the Breadth First Search algorithm is a simple algorithm that can be used to perform online store detection with good performance.
A Secured Cloud-based E-voting System using Information Dispersal Algorithm

The introduction of the electronic voting (e-voting) system has received much attention by researchers in recent years.Evoting has been of interest to stakeholders and political party leaders in most countries (that is developed and developing) practicing democracy.Academics and technocrats have delved into technical issues related to e-voting system that could foster its smooth implementation and this have encouraged it's full acceptance in many countries.The challenge however, is how to secure and maintain a trustworthy e-voting system devoid of security breaches especially from hacking and hijacking.The challenge still remains an open area that calls for novel designs into high level security infrastructure that may enhance and improve the security of e-voting systems to gain the full trust, acceptance, and adoption by the citizenry.Ghana deploys computerized system for registration and verification during the electoral process.During the 2016 election, the Electoral Commission of Ghana adopted the electronic transmission of results although the elections were conducted manually.In this paper, a novel secured framework for electronic voting relying on the principle of the Information Dispersal Algorithm (IDA) is proposed.In our approach, upon voting, the voters vote record is encrypted and split for distribution on several virtual cloud servers.At the end of the voting period, the split vote records are reassembled into their original state for counting to take place.The splitting of the vote records and its assembling are performed by the IDA.The paper further discusses the design and implementation of the IDA in a typical e-voting environment.
UI at SemEval-2020 Task 4: Commonsense Validation and Explanation by Exploiting Contradiction

This paper describes our submissions into the ComVe challenge, the SemEval 2020 Task 4. This evaluation task consists of three sub-tasks that test commonsense comprehension by identifying sentences that do not make sense and explain why they do not.In subtask A, we use Roberta to find which sentence does not make sense.In subtask B, besides using BERT, we also experiment with replacing the dataset with MNLI when selecting the best explanation from the provided options why the given sentence does not make sense.In subtask C, we utilize the MNLI model from subtask B to evaluate the explanation generated by Roberta and GPT-2 by exploiting the contradiction of the sentence and their explanation.Our system submission records a performance of 88.2%, 80.5%, and BLEU 5.5 for those three subtasks, respectively.
Processing Garden-Path Sentences in Sight Translation: An Experimental Study

Garden-path refers to a type of temporary syntactic ambiguity in which the phrase structure rules allow two possible attachments of a constituent (Fromkin, Rodman &amp;amp; Hymas, 2011, p. 385). This study aims to investigate whether sight translators who are trained on this type of translation perform better in overcoming garden-path effect than other translators who are not trained on sight translation. During the translation process of such sentences, the sight transla-tor is expected to parse the syntactic order of the source text correctly to grasp the meaning, and reformulate the message in the target language applying the necessary transpositions in the word order. An experiment was designed to test the performance differences in sight trans-lation of garden-path sentences. Experimental and control groups were given five garden-path sentences within various contexts and were asked to record their voices. The results showed that trained sight translators tend to receive garden-path sentences as a whole and concentrate on the message of the sentence, making use of certain skills and techniques in reading. In this sense, trained sight translators are more successful in parsing complex sentences with garden-path effect than ordinary readers.
Results of SemTab 2020

SemTab 2020 was the second edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 19th International Semantic Web Conference (ISWC) and the 15th Ontology Matching (OM) Workshop. SemTab provides a common framework to conduct a systematic evaluation of state-of-the-art systems.
Contextualized End-to-End Neural Entity Linking

We propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED). Our model applies task-specific heads on top of shared BERT contextualized embeddings. We achieve state-of-the-art results across a standard EL dataset using our model; we also study our model’s performance under the setting when hand-crafted entity candidate sets are not available and find that the model performs well under such a setting too.
Deep Inside-outside Recursive Autoencoder with All-span Objective

Deep inside-outside recursive autoencoder (DIORA) is a neural-based model designed for unsupervised constituency parsing.During its forward computation, it provides phrase and contextual representations for all spans in the input sentence.By utilizing the contextual representation of each leaf-level span, the span of length 1, to reconstruct the word inside the span, the model is trained without labeled data.In this work, we extend the training objective of DIORA by making use of all spans instead of only leaf-level spans.We test our new training objective on datasets of two languages: English and Japanese, and empirically show that our method achieves improvement in parsing accuracy over the original DIORA.
Design of Enterprise Information System Architecture with Oracle Architecture Development Process (OADP) Case Study in Vocational High Schools

The development of information and communication technology has led to increasingly competitive competition among organizations. The management of the organization is required to build and develop a fast, precise and accurate information system (IS) to assist business activities in achieving organizational goals and providing services to stakeholders, especially in relation to data, information, technology and applications. ORACLE Architecture Development Process (OADP) is a method or frame of reference for building an information architecture oriented to business needs. This consists of the data architecture, applications, technology and implementation plans of the architectures that have been created to support business activities in achieving the organization mission. The OADP describes the data architecture, applications and technology required to support the organization business. Vocational High Schools cannot be separated from the needs of business and information architecture. A lot of information is generated from all processes that occur in the Vocational High School environment every day but the existing data management is not efficient because it has not been thoroughly connected. This may take a long time to process past data or calculate future needs because the data management process is still running separately. Because of these cases, the modeling information architecture in an organizational environment is required as a stage in supporting the running of business processes in the organization.
Multi-area Path Planning for Wireless Sensor Networks Based on Double Populations Ant Colony Optimization Algorithm

Aiming at the problem that the ant colony algorithm(ACO) is slow to converge and easily fall into the local optimal value in the path planning of multi-area wireless sensor networks(WSNs), an improved ant colony optimization algorithm is proposed. First, a grid method was used to model two different obstacle environments. Second, to improve the efficiency of ant search in the early stages set a non-uniform initial pheromone concentration, and introduce new populations to expand the search space of the algorithm and avoid the algorithm falling into a local optimum. Finally, it is proposed that the elite ant pheromone update principle and adaptively adjusts the volatile coefficients to ensure the global search capability and improve the convergence speed of the algorithm. The experimental results show that the algorithm has a high global search capability and significantly faster convergence speed, which verifies the effectiveness and superiority of the algorithm.
Graph automorphisms for compression

Abstract Detecting automorphisms is a natural way to identify redundant information presented in structured data. When such redundancies are detected they can be used for data compression. In this paper we explore two different classes of graphs to capture this intuitive property of automorphisms. Symmetry-compressible graphs are the first class which introduces the basic concepts but use only global symmetries for the compression. In order for this concept to be more practical, we need to use local symmetries. Thus, we extend the basic graph class with Near Symmetry compressible graphs. Furthermore, we develop two algorithms that can be used to compress practical instances and empirically evaluate them on a set of realistic graphs.
Scale-Adaptive Growing Neural Network Based on Distortion Error Stability and its Application in Image Topological Feature Extraction

Self-organizing neural networks are characterized by topology preservation, dynamic adaptation, clustering, and dimensionality reduction, which prompt their wide application in data mining, knowledge extraction, and image processing. However, existing self-organizing neural networks fail to automatically generate an output space that contains an appropriate number of neurons according to the data input. To address this problem, this paper proposes a growing neural gas (GNG) algorithm with adaptive output network scale, which is called scale-adaptive GNG (SA-GNG) algorithm. The learning process of SA-GNG is divided into two stages: growth and convergence. At the growth stage, distortion error stability is introduced to objectively judge the degree of approximation of the output network to the input space, so that SA-GNG can grow neurons on demand until significant improvement is no longer made for distortion errors. In the convergence stage, neurons are not allowed to be produced, and the similarity between the output network and the input data is improved through continuous learning. SA-GNG promises to autonomously generate an appropriate number of neurons according to the size of the input data, with no need of determining the total number of neurons to be generated in advance, thereby greatly improving its adaptability. As such, the algorithm is especially suitable for the application scenarios where the amount of the data to be input is unknown. The validity and feasibility of the algorithm proposed in this paper are verified by experiments.
Improved Steganography Techniques for Different Types of Secret Data

Steganography is an interesting aspect of data hiding art where the secret data is embedded in a cover media without raising any suspicion about the existence of that data. Over the years, different steganography techniques for digital images have been presented to be applied in different applications such as security and privacy control. Some of these techniques are based on transforming the image from the spatial domain to the transform domain as a pre-processing step before embedding the secret data. This paper presents an improved steganography technique in the transform domain based on studying the effect of changing two major factors on the performance of steganography techniques. The paper illustrates the algorithms that have been implemented for the study purposes. The results of this study proved that the two major factors that can affect the performance of the steganography technique are the transform type and the data embedding method. According to this study an improved steganography technique is presented based on Slantlet transform (SLT) and a new data embedding method. The experimental results proved the efficiency of the proposed technique in comparison with a previous SLT-based steganography technique.
ON THE EXPERIENCE OF REALIZING ADVANCED EDUCATION FOR TRAINING DIGITAL SOCIETY BACHELORS

The paper substantiates the need to create a practical technology for the implementation of advanced education for training of bachelors of digital society on the basis of modern pedagogical formats of communication between a teacher and students. Pedagogical methods and technologies are presented that implement practical implementation of two types of advancing: advancing “ from below” when the content of education is based on the current level of technology development and advancing “ from above” which involves anticipating not specific theoretical knowledge or work skills, but personality characteristics that allow the student adapt successfully in the future. The modern mathematical means and tools for the implementation of intellectual analysis of pedagogical monitoring data are proposed which allow building a scientifically grounded individual educational trajectory. All pedagogical approaches and methods used contribute to the preparation of students for future work activities in the face of rapid obsolescence of knowledge and technologies and correspond to the modern level of development of digital education, which are characterized by qualitative changes in data processing and transmission technologies and the widespread use of information and communication technologies.
Tree Representations in Transition System for RST Parsing

The transition-based systems in the past studies propose a series of actions to build a right-heavy binarized tree for RST parsing.However, the nodes of the binary-nuclear relations (e.g., Contrast) have the same nuclear type with those of the multi-nuclear relations (e.g., Joint) in the binary tree structure.In addition, the reduce action only construct binary trees instead of multi-branch trees, which is the original RST tree structure.In our paper, we design a new nuclear type for the multi-nuclear relations, and a new action to construct a multi-branch tree.We enrich the feature set by extracting additional refined dependency feature of texts from the Bi-Affine model (Dozat and Manning, 2016).We also compare the performance of two approaches for RST parsing in the transition-based system: a joint action of reduce-shift and nuclear type (i.e., Reduce-SN) vs a separate one that applies Reduce action first and then assigns nuclear type.We find that the new devised nuclear type and action are more capable of capturing the multi-nuclear relation and the joint action is more suitable than the separate one.Our multi-branch tree structure obtains the state-of-the-art performance for all the 18 coarse relations.
Weight Maps Guided Underwater Image Enhancement By Fusion Technique

The underwater image plays a very important role in today's world in order to explore the unknown underwater world.But the images captured underwater are different from those taken in the air atmosphere.There are lots of problems associated such as haziness, blurriness, non-uniformity, color degradation, etc.All this is due to reflection, refraction, and scattering in water.To overcome these problems enhancement of the underwater image is required to get a better quality image.In this paper, the logarithmic image processing model is proposed as an important part of the methodology for underwater image enhancement.The insight to apply the logarithmic image processing model was taken from the human eye visual concept, as it functions in the logarithmic fashion while the camera functions linearly.A single image approach is used in which a degraded image is taken as an input and then various digital image operations are performed on it to enhance the quality of the underwater image.The insight to apply the logarithmic image processing model was taken from the human eye visual concept, as it functions in the logarithmic fashion while the camera functions linearly.The method is based on the fusion of directly derived images from gamma correction, sharpening, and their associated weight maps.Finally, the fused image is processed through Contrast Limited Adaptive Histogram Equalization (CLAHE) to get the enhanced output.The method is applied on two data set of images, subjective evaluation is computed and results are compared with other methods.To do an objective evaluation, the quality of output image measures through parameters and compared with other available methodologies.The experimental results show that the performance of the proposed method is competitive with that of available methods.
The Impact of Innovation on Profitability of Jordanian Services Companies

This research paper attempts to reveal the level of innovation types in Jordanian services companies. It also investigates the relationship between the overall innovation and profitability in these services companies. Innovation types have been measured through a questionnaire distributed to the members of Securities Depository Center SDC - Public Shareholding Companies &amp;ndash; Service Sector. Profitability has been measured through the mean of net profits after taxes for the two years 2018 and 2019. The research findings indicate that innovation types in these companies is slightly below the average level. The results of research also indicate that there is a statistically significant positive impact of overall innovation on profitability of Jordanian services companies. The research concludes that investment in innovation is a key pillar for profitability of business companies.
Text Generation with Content and Structure-Based Preprocessing in Imbalanced Data of Product Review

Spam detection frequently categorizes product reviews as spam and non-spam. The spam reviews may contain texts of fake reviews and non-review statements describing unrelated things about products. Most of the publicly available spam reviews are labelled as fake reviews, while non-spam texts that are not fake reviews could contain non-review statements. It is crucial to notice those non-review statements since they convey misperception to consumers. Non-review statements are hardly found, and those statements of large and long texts often need to be manually labelled, which is time-consuming. Because of the rareness in finding non-review statements, there is an imbalanced condition between non-spam as a major class and spam that consists of the non-review statement as a minor class. Augmenting fake reviews to add spam texts is ineffective because they have similar content to non-spam such as some opinion words of product features. Thus, the text generation of non-review statements is preferable for adding spam texts. Some text generation issues are the frequent neural network-based methods require much learning data, and the existing pre-trained models produce texts with different contexts to non-review statements. The augmented texts should have similar content and context represented by the structure of the non-review statement. Therefore, we propose a text generation model with content and structure-based preprocessing to produce non-review statements, which is expected to overcome imbalanced data and give better spam detection results in product reviews. Structure-based preprocessing identifies the feature structures of non-opinion words from part-of-speech tags. Those features represent the context of spam reviews in unlabeled texts. Then, content-based preprocessing appoints selected topic modeling results of non-review statements from fake reviews. Our experiments resulted an improvement on the metric value of ± 0.04, called as BLEU (Bi-Lingual Evaluation Understudy) score, for the correspondence evaluation between generated and trained texts. The metric value indicates that the generated texts are not quite identical to the trained texts of non-review statements. However, those additional texts combined with the original spam texts gave better spam detection results with an increasing value of more than 40% on average recall score.