Reviewerly/exHarmony huggingface dataset:
Toward Campus Mail Delivery Using BDI

Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture tend to be mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University. Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. In this paper, we discuss how we linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use casesand demonstrated the system performance in an analogue environment. ROS handles the connections to the low-level sensors and actuators, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is sent to the reasoning system as perceptions using ROS. These perceptions are then deliberated upon, and an action string is sent back to ROS for interpretation and driving of the necessary actuator for the action to be performed. In this paper we present our current implementation, which closes the loop on the hardware-software integration and implements a subset of the use cases required for the full system. We demonstrated the performance of the system in an analogue environment.
Lottery and Auction on Quantum Blockchain

This paper proposes a protocol for lottery and a protocol for auction on quantum Blockchain. Our protocol of lottery satisfies randomness, unpredictability, unforgeability, verifiability, decentralization and unconditional security. Our protocol of auction satisfies bid privacy, posterior privacy, bids’ binding, decentralization and unconditional security. Except quantum Blockchain, the main technique involved in both protocols is quantum bit commitment.
Immersive Virtual Reality Implementations in Developmental Psychology

In recent years, immersive virtual reality technology (IVR) has seen a substantial improvement in its quality, affordability, and ability to simulate the real world. Virtual reality in psychology can be used for three basic purposes: immersion, simulation, and a combination of both. While the psychological implementations of IVR have been predominately used with adults, this review seeks to update our knowledge about the uses and effectiveness of IVR with children. Specifically, its use as a tool for pain distraction, neuropsychological assessment, and skills training. Results showed that IVR is a useful tool when it is used either for immersive or simulative purposes (e.g., pain distraction, neuropsychological assessment), but when its use requires both simulation (of the real world) and immersion (e.g., a vivid environment), it is trickier to implement effectively.
A Comparison of Learning Rate Selection Methods in Generalized Bayesian Inference

Generalized Bayes posterior distributions are formed by putting a fractional power on the likelihood before combining with the prior via Bayes’s formula. This fractional power, which is often viewed as a remedy for potential model misspecification bias, is called the learning rate, and a number of data-driven learning rate selection methods have been proposed in the recent literature. Each of these proposals has a different focus, a different target they aim to achieve, which makes them difficult to compare. In this paper, we provide a direct head-to-head empirical comparison of these learning rate selection methods in various misspecified model scenarios, in terms of several relevant metrics, in particular, coverage probability of the generalized Bayes credible regions. In some examples all the methods perform well, while in others the misspecification is too severe to be overcome, but we find that the so-called generalized posterior calibration algorithm tends to outperform the others in terms of credible region coverage probability.
FCENet: An Instance Segmentation Model for Extracting Figures and Captions From Material Documents

A critical ideology of the existing Material Genome Project refers to the application of data and artificial intelligence to facilitate material innovation.The lack of data hinders the development of novel materials.The figures and captions in the material literature cover essential information regarding the entire document and have sufficient image sample data for research.Accordingly, how to extract figures and captions from the literature is critical to solve the lack of data.Though some PDF parsing tools are capable of extracting information from documents, they generally identify a document's figures by parsing the document into a concrete structure.As impacted by the inconsistency of the form of different journals, they commonly achieve wrong recognition results.Thus, an efficient figure and caption extraction network FCENet is proposed in the present study.Inconsistent with other extraction tools, this study first attempts to adopt instance segmentation models to detect figures and their captions, and then extract them.FCENet developed in this study builds upon BlendMask and introduces a horizontal and vertical attention module.This study splits the BlendMask detection head into two branches, i.e., figure detection and caption detection, which increases final detection accuracy and speed.This study collects nearly 3000 material documents for model training and testing.As revealed from the last experiments and results, the performance of FCENet is significantly compared with that of other existing instance segmentation models.Its box and mask mAP (mean Average Precision) are 8.51% and 12.59% higher than those of BlendMask, respectively.This study hopes that considerable material image data can be acquired via FCENet and sufficiently support image data for machine learning and data mining in the material area.
Automated Lay Language Summarization of Biomedical Scientific Reviews

Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the biomedical literature to the general population. This problem can be framed as a type of translation problem between the language of healthcare professionals, and that of the general public. In this paper, we introduce the novel task of automated generation of lay language summaries of biomedical scientific reviews, and construct a dataset to support the development and evaluation of automated methods through which to enhance the accessibility of the biomedical literature. We conduct analyses of the various challenges in performing this task, including not only summarization of the key points but also explanation of background knowledge and simplification of professional language. We experiment with state-of-the-art summarization models as well as several data augmentation techniques, and evaluate their performance using both automated metrics and human assessment. Results indicate that automatically generated summaries produced using contemporary neural architectures can achieve promising quality and readability as compared with reference summaries developed for the lay public by experts (best ROUGE-L of 50.24 and Flesch-Kincaid readability score of 13.30). We also discuss the limitations of the current effort, providing insights and directions for future work.
Would you describe a leopard as yellow? Evaluating crowd-annotations with justified and informative disagreement

Semantic annotation tasks contain ambiguity and vagueness and require varying degrees of world knowledge.Disagreement is an important indication of these phenomena.Most traditional evaluation methods, however, critically hinge upon the notion of inter-annotator agreement.While alternative frameworks have been proposed, they do not move beyond agreement as the most important indicator of quality.Critically, evaluations usually do not distinguish between instances in which agreement is expected and instances in which disagreement is not only valid but desired because it captures the linguistic and cognitive phenomena in the data.We attempt to overcome these limitations using the example of a dataset that provides semantic representations for diagnostic experiments on language models.Ambiguity, vagueness, and difficulty are not only highly relevant for this use-case, but also play an important role in other types of semantic annotation tasks.We establish an additional, agreement-independent quality metric based on answer-coherence and evaluate it in comparison to existing metrics.We compare against a gold standard and evaluate on expected disagreement.Despite generally low agreement, annotations follow expected behavior and have high accuracy when selected based on coherence.We show that combining different quality metrics enables a more comprehensive evaluation than relying exclusively on agreement.
Secure image hiding in speech signal by steganography-mining and encryption

&lt;span&gt;Information hiding techniques are constantly evolving due to the increased need for security and confidentiality. This paper proposes a working mechanism in three phases. The first phase includes scrambling the values of the gray image depending on a series of keys that are generated using a quantum chaotic map. The second phase generates hybrid keys by mixing a Zaslavsky and a 3D Hanon map that are used to encrypt the gray image values produced after the scramble. Finally, in the third phase, a new algorithm is suggested to hide the encrypted gray image at random locations within a speech file. This algorithm includes the LSB algorithm to determine the hidden bits and the zero-crossing K-means algorithm in selecting locations mining in a scattered manner so that hackers cannot easily retrieve the hidden data of any hacked person. Also used a fractional fourier transform to choose magnitude value as specific data to hide encoded image data. The measures MSE, PSNR, NSCR, and UACI are using to measure the work efficiency in the encryption algorithm, and in measuring the efficiency of the hidden algorithm, use the measures SNR, PSNR, and MSE. The results of the paper are encouraging and efficient compared to other algorithms that performed the same work. Hence our results show the larger the image dimensions used, the better the values.&lt;/span&gt;
PoliTeam @ AMI: Improving Sentence Embedding Similaritywith Misogyny Lexicons for Automatic Misogyny Identificationin Italian Tweets

We present a multi-agent classification solution for identifying misogynous and aggressive content in Italian tweets. A first agent uses modern Sentence Embedding techniques to encode tweets and a SVM classifier to produce initial labels. A second agent, based on TF-IDF and Misogyny Italian lexicons, is jointly adopted to improve the first agent on uncertain predictions. We evaluate our approach in the Automatic Misogyny Identification Shared Task of the EVALITA 2020 campaign. Results show that TF-IDF and lexicons effectively improve the supervised agent trained on sentence embeddings.
NITS-Hinglish-SentiMix at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using an Ensemble Model

Sentiment Analysis is the process of deciphering what a sentence emotes and classifying them as either positive, negative, or neutral.In recent times, India has seen a huge influx in the number of active social media users and this has led to a plethora of unstructured text data.Since the Indian population is generally fluent in both Hindi and English, they end up generating code-mixed Hinglish social media text i.e. the expressions of Hindi language, written in the Roman script alongside other English words.The ability to adequately comprehend the notions in these texts is truly necessary.Our team, rns2020 participated in Task 9 at SemEval2020 intending to design a system to carry out the sentiment analysis of code-mixed social media text.This work proposes a system named NITS-Hinglish-SentiMix to viably complete the sentiment analysis of such code-mixed Hinglish text.The proposed framework has recorded an F-Score of 0.617 on the test data.
Socio-Economic Factors On Sector-Wide Systematic Risk Of Information Security Breaches: Conceptual Framework

This paper aims to propose a conceptual framework for investigating the impact of Socio-economic Breach Contingency Factors (BCFs) on banking sector's systematic risk.The study adopts an event study methodology comprising 200 global incidents of Information Security (IS) breaches occurring in listed banks.The Socio-economic BCFs will be proxied by (1) Cyber Crime Fear in a Country and (2) Information & Communication Technology (ICT) Penetration in a Country.The proposed framework aims to validate the nexus of socio-economic factors and systematic risk through which shareholders and bank managers can assess the risk of possible market value losses at the incident of IS breach in the banking sector.Considering the scenario of IS breach, this paper will make a significant contribution to the theory of efficient market hypothesis by way of extension and integration of theories.EMH under the context of IS breach has been limited by the BCFs of attack type, firm type, and industry type.
Browser Forensics on Web-based Tiktok Applications

Tiktok application is an application that is being used a lot, so it causes many criminal acts such as defamation, cybercrime, and cyberbullying.This study uses a forensic method as a standard in the research phase to reveal evidence of pollution crimes that occur in the TikTok web browser application.The method used in this research is the National Institute of Standard Technology (NIST).This method has 4 stages used for reference in the analysis of evidence, namelyanalysis collection, examination,and reporting.This research uses a laptop which is used as experimental material in the scenario.This case uses a Chrome web browser in public mode where a laptop is logged into Tiktok with the Chrome browser used to post videos.This research uses tools, forensicnamely FTK Imager, Browser History Capture / Viewer, Video Cache Viewer.The results obtained from this study are data or evidence from a post that has been deleted in the TikTok web browser application.The results obtained from the process of this research stage are in the form of text, username, photos / videos, and links from posts that the suspect has deleted.Based on the toolsused in this study, 80% managed to get butki goods and 20% failed, namely video.The evidence found can then be used to report and assist in the trial process.
Exploiting Ontology Recommendation Using Text Categorization Approach

Semantic Web is considered as the backbone of web 3.0 and ontologies are an integral part of the Semantic Web. Though an increase of ontologies in different domains is reported due to various benefits which include data heterogeneity, automated information analysis, and reusability, however, finding an appropriate ontology according to user requirement remains cumbersome task due to time and efforts required, context-awareness, and computational complexity. To overcome these issues, an ontology recommendation framework is proposed. The Proposed framework employs text categorization and unsupervised learning techniques. The benefits of the proposed framework are twofold: 1) ontology organization according to the opinion of domain experts and 2) ontology recommendation with respect to user requirement. Moreover, an evaluation model is also proposed to assess the effectiveness of the proposed framework in terms of ontologies organization and recommendation. The main consequences of the proposed framework are 1) ontologies of a corpus can be organized effectively, 2) no effort and time are required to select an appropriate ontology, 3) computational complexity is only limited to the use of unsupervised learning techniques, and 4) due to no requirement of context awareness, the proposed framework can be effective for any corpus or online libraries of ontologies.
Cybersecurity and Cyber Forensics: Machine Learning Approach

The proliferation of cloud computing and internet of things has led to the connectivity of states and nations (developed and developing countries) worldwide in which global network provide platform for the connection.Digital forensics is a field of computer security that uses software applications and standard guidelines which support the extraction of evidences from any computer appliances which are perfectly enough for the court of law to use and make a judgment based on the comprehensiveness, authenticity and objectivity of the information obtained.Cybersecurity is of major concerned to the internet users worldwide due to the recent form of attacks, threat, viruses, intrusion among others going on every day among internet of things.The aim of this work is make a systematic review on the application of machine learning algorithms to cybersecurity and cyber forensics, systematic survey method was used on recent application of machine learning algorithms on cyber forensics and cyber security based on this findings it is observed that cybersecurity is based on confidentiality, integrity and validity of data, it is also noted that there are ten steps to cybersecurity; network security, user education and awareness, malware prevention, removable media control, secure configuration, managing user privileges, incident management, monitoring and home and mobile working and pave away for further research directions on the application of deep learning, computational intelligence, soft computing to cybersecurity and cyber forensics.
CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations

This paper describes the winning contribution to SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern.We present an ensemble model that makes predictions based on context-free and context-dependent word representations.The key findings are that (1) context-free word representations are a powerful and robust baseline, (2) a sentence classification objective can be used to obtain useful contextdependent word representations, and (3) combining those representations increases performance on some datasets while decreasing performance on others.
Construction of network open teaching platform of analytical chemistry based on facial recognition and artificial intelligence

The theoretical and experimental teaching part of analytical chemistry is relatively boring, so it is difficult to improve the teaching results of analytical chemistry through the traditional network teaching mode. In this paper, based on the actual situation and based on the facial recognition algorithm, this paper uses artificial intelligence algorithm to build an open network teaching platform for analytical chemistry. Moreover, this paper improves the traditional adaptive genetic algorithm, and introduces a directional perturbation operator to non-linearize the adaptive adjustment probability. In algorithm design and simulation experiments, an improved genetic algorithm based on t distribution is used as an example to prove the effectiveness of the method in this paper. The platform constructed in this article is mainly applied to the teaching of analytical chemistry. The teaching of analytical chemistry includes theoretical teaching and experimental teaching. In addition, according to the actual needs of teaching, this article conducts research and analysis on the platform through theoretical teaching and experimental teaching. The research results show that the constructed teaching platform has good performance.
Improved method on image stitching based on optical flow algorithm

With the rapid development of technologies based on virtual reality, image stitching is widely used in various fields such as broadcasting, games, education, and architecture. Image stitching is a method for connecting multiple images to produce a high-resolution image and a wide field of view image. It is common for most of the stitching methods to find and match the feature in the image. However, these stitching methods have the disadvantage that they cannot create a perfect 360-degree panoramic image because the depth of the projected area varies depending on the position and direction between adjacent cameras. Therefore, we propose an advanced stitching method to improve the deviation due to the difference in the depth of each area using the pixel value of the input image after the feature-based stitching. After the feature-based stitching method has been performed, the pixel values of overlapping areas in the image are calculated as an optical flow algorithm, then finely distorted, and then corrected to ensure that the image overlaps correctly. Through experiments, it was confirmed that the problem that was deviated from the feature-based stitching was solved. Besides, as a result of performance evaluation, it was proved that the proposed stitching method using an optical flow algorithm is capable of real-time and fast service.
Cross-lingual Annotation Projection in Legal Texts

We study annotation projection in text classification problems where source documents are published in multiple languages and may not be an exact translation of one another.In particular, we focus on the detection of unfair clauses in privacy policies and terms of service.We present the first English-German parallel asymmetric corpus for the task at hand.We study and compare several language-agnostic sentence-level projection methods.Our results indicate that a combination of word embeddings and dynamic time warping performs best.
Blockchain

Many applications are being built using the immutability and robustness of blockchain. Blockchain is a new class of information technology that combines cryptography and a distributed ledger that already exists. The model is composed of a group of computers that collaborate towards maintaining a secured database without storing the data at any central unit. It is the technology behind all the crypto currencies like Bitcoin, Litecoin, Ethereum, and now finding its way to record everything possible. This paper focuses on the basic framework of blockchain model, its pre-requisites, and challenges of blockchain. Various current real-time applications of the technology are also discussed. Finally, an application area has been proposed that can be used to create a huge database of the citizens of the country and facilitate them with ease of access to their personal data. It will open new ways of data analysis at a nationwide scale.
PGSG at SemEval-2020 Task 12: BERT-LSTM with Tweets’ Pretrained Model and Noisy Student Training Method

The paper presents a system developed for the SemEval-2020 competition Task 12 (OffensEval-2): Multilingual Offensive Language Identification in Social Media.We achieve the second place (2nd) in sub-task B: Automatic categorization of offense types and are ranked 55th with a macro F1-score of 90.59 in sub-task A: Offensive language identification.Our solution is using a stack of BERT and LSTM layers, training with the Noisy Student method.Since the tweets data contains a large number of noisy words and slang, we update the vocabulary of the BERT large model pre-trained by the Google AI Language team.We fine-tune the model with tweet sentences provided in the challenge.