Reviewerly/exHarmony huggingface dataset:
A New Hybrid KNN Classification Approach based on Particle Swarm Optimization

K-Nearest Neighbour algorithm is widely used as a classification technique due to its simplicity to be applied on different types of data.The presence of multidimensional and outliers data have a great effect on the accuracy of the K-Nearest Neighbour algorithm.In this paper, a new hybrid approach called Particle Optimized Scored K-Nearest Neighbour was proposed in order to improve the performance of K-Nearest Neighbour.The new approach is implemented in two phases; the first phase help to solve the multidimensional data by making feature selection using Particle Swarm Optimization algorithm, the second phase help to solve the presence of outliers by taking the result of the first phase and apply on it a new proposed scored K-Nearest Neighbour technique.This approach was applied on Soybean dataset, using 10 fold cross validation.The experiment results shows that the proposed approach achieves better results than the K-Nearest Neighbour algorithm and it's modified.
Pencegahan Penyebaran Virus Corona di Bandara Menggunakan Artificial Intellegence

Identifying the corona virus on international arrival passengers aims to find out whether the passenger has the corona virus or not. Corona virus is one of the health problems that really needs our attention now. Because this virus can cause death, and the way the virus is transmitted is very easy and fast. There are many things that can be done to reduce the spread of this virus to the territory of Indonesia, one of which is to identify the body temperature of each passenger on international arrivals at the airport. At the airport the identification of this virus is done by using Artificial Intelligence. One of the things that is the goal of developing artificial intelligence (AI) is to help humans. This often means imitating the abilities and intelligence of the person he is trying to help or replace. The tool used is the Thermal Scanner Camera that can see directly the body temperature of the passenger. And there are several kits that have been made to check directly whether there is a corona virus in the human body by using the Rapid Test and PCR Test. This research was conducted to see how important AI (artificial intelligence) is in helping reduce the spread of corona virus. According to some existing data, the use of Thermal Scanner Camera can help reduce the spread of corona virus at airports.
Framework for Securing IoT Ecosystem Using Blockchain: Use Cases Suggesting Theoretical Architecture

Blockchain is a distributed, open record between two customers for all trades. The decentralized nature is possible because each trade is controlled by the understanding of a larger number of customers, who are interested in anything in the system. The authors concentrate and present methods to address real issues of security in IoT. The authors review use cases and organize common issues of security with respect to IoT layered building. Despite the current plans for attacks, hazards and cutting-edge game, we are designing essential IoT security conditions. IoT security problems are also orchestrated against the current game plans discovered in writing and mapped against them. Furthermore, we are trying to discuss how blockchain, which is the fundamental development of Bitcoin, can be the primary enabling impact to deal with immeasurable IoT security problems. Besides, the paper includes open research issues and challenges to IoT security. In this paper, the potential informative applications are focused, along with explorations on how blockchain advancement can be used to address some preparation issues. In the wake of inquiring about security issues, authors present an architecture of IoT blockchain ecosystem. This paper also talks about the highlights and ideal states of blockchain improvement.
Codes with few weights arising from linear sets

In this article we present a class of codes with few weights arising from a special type of linear sets. We explicitly show the weights of such codes, their weight enumerators and possible choices for their generator matrices. In particular, our construction yields linear codes with three weights and, in some cases, almost MDS codes. The interest for these codes relies on their applications to authentication codes and secret schemes, and their connections with further objects such as association schemes and graphs.
SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces

Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time.Unsupervised semantic shift tracing, focal point of SemEval2020, is particularly challenging.Given the unsupervised setup, in this work, we propose to identify clusters among different occurrences of each target word, considering these as representatives of different word meanings.As such, disagreements in obtained clusters naturally allow to quantify the level of semantic shift per each target word in four target languages.To leverage this idea, clustering is performed on contextualized (BERT-based) embeddings of word occurrences.The obtained results show that our approach performs well both measured separately (per language) and overall, where we surpass all provided SemEval baselines. Problem SetupConsider two corpora C 1 and C 2 for a same language but associated with different time stamps (say, respectively, t 1 and t 2 > t 1 ).Let W be a set of target words occurring in both corpora.Each target word w ∈ W might assume multiple meanings, to be called senses, within the two corpora.A pool of experts annotated a representative amount of occurrences with their corresponding senses.The problem we consider is to characterize the semantic shift related to those senses from one corpus to the other without having access to the expert annotations.In particular, we address the two following two subtasks:• Subtask 1: Decide, for each w ∈ W, whether or not w gained or lost at least a sense between t 1 and t 2 .This is a binary decision task.We will denote this subtask as (S1).• Subtask 2: Define, for the elements of W, a measure of their degree of lexical semantic change between t 1 and t 2 and sort these elements consequently.This is a ranking task.This subtask will be referred to as (S2).We 1 describe two different methods able to address both subtasks.As both methods require a preprocessing step based on transformers, let us start from this preliminary operation. PreprocessingFor the proposed approach, we used embeddings derived from BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2018) model to represent the text information in the corpora.BERT uses attention mechanism to learn the contextual relations and reads the input bidirectionally.It is an encoder-only model (as the goal is to generate a language model) opposed to transformers (encoderdecoder model) (Vaswani et al., 2017).BERT is trained on two main objectives, masked language model (MLM) and next sentence prediction (NSP).
Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval

Shape recognition and retrieval is a complex task on non-rigid objects and it can be effectively performed by using a set of compact shape descriptors. This paper presents a new technique for generating normalised contour points from shape silhouettes, which involves the identification of object contour from images and subsequently the object area normalisation (OAN) method is used to partition the object into equal part area segments with respect to shape centroid. Later, these contour points are used to derive six descriptors such as compact centroid distance (CCD), central angle (ANG), normalized points distance (NPD), centroid distance ratio (CDR), angular pattern descriptor (APD) and multi-triangle area representation (MTAR). These descriptors are a 1D shape feature vector which preserve contour and region information of the shapes. The performance of the proposed descriptors is evaluated on MPEG-7 Part-A, Part-B and multi-view curve dataset images. The present experiments are aimed to check proposed shape descriptor's robustness to affine invariance property and image retrieval performance. Comparative study has also been carried out for evaluating our approach with other state of the art approaches. The results show that image retrieval rate in OAN approach performs significantly better than that in other existing shape descriptors.
SGX-MR: Regulating Dataflows for Protecting Access Patterns of Data-Intensive SGX Applications

Abstract Intel SGX has been a popular trusted execution environment (TEE) for protecting the integrity and confidentiality of applications running on untrusted platforms such as cloud. However, the access patterns of SGX-based programs can still be observed by adversaries, which may leak important information for successful attacks. Researchers have been experimenting with Oblivious RAM (ORAM) to address the privacy of access patterns. ORAM is a powerful low-level primitive that provides application-agnostic protection for any I/O operations, however, at a high cost. We find that some application-specific access patterns, such as sequential block I/O, do not provide additional information to adversaries. Others, such as sorting, can be replaced with specific oblivious algorithms that are more efficient than ORAM. The challenge is that developers may need to look into all the details of application-specific access patterns to design suitable solutions, which is time-consuming and error-prone. In this paper, we present the lightweight SGX based MapReduce (SGX-MR) approach that regulates the dataflow of data-intensive SGX applications for easier application-level access-pattern analysis and protection. It uses the MapReduce framework to cover a large class of data-intensive applications, and the entire framework can be implemented with a small memory footprint. With this framework, we have examined the stages of data processing, identified the access patterns that need protection, and designed corresponding efficient protection methods. Our experiments show that SGX-MR based applications are much more efficient than the ORAM-based implementations.
AirSign: Smartphone Authentication by Signing in the Air

In this paper, we propose AirSign, a novel user authentication technology to provide users with more convenient, intuitive, and secure ways of interacting with smartphones in daily settings. AirSign leverages both acoustic and motion sensors for user authentication by signing signatures in the air through smartphones without requiring any special hardware. This technology actively transmits inaudible acoustic signals from the earpiece speaker, receives echoes back through both built-in microphones to “illuminate” signature and hand geometry, and authenticates users according to the unique features extracted from echoes and motion sensors. To evaluate our system, we collected registered, genuine, and forged signatures from 30 participants, and by applying AirSign on the above dataset, we were able to successfully distinguish between genuine and forged signatures with a 97.1% F-score while requesting only seven signatures during the registration phase.
A Systematic Literature Review on Machine Learning for Automated Requirements Classification

The development of quality software begins with the correct identification of the system needs. These requirements represent the basis of the subsequent activities in the software life cycle. The correct identification of these requirements in their different categories impacts on the actions taken to meet them. However, this classification can be often time-consuming or error-prone when it comes to large-scale systems, so different proposals have been made to assist in this process automatically. This systematic literature review identifies those applications of Machine Learning techniques in the classification of software requirements. In this regard, 13 articles were identified, from which relevant information on the applied algorithms, their training process, and their evaluation metrics are analyzed. From the results obtained, it is identified that the most recurrent classification algorithms featured on the identified studies are Naive Bayes, Decision Trees, and Natural Language Processing algorithms. The most frequent training datasets are academic databases and collected user reviews.
ManyEnt: A Dataset for Few-shot Entity Typing

We introduce ManyEnt, a benchmark for entity typing models in few-shot scenarios.ManyEnt offers a rich typeset, with a fine-grain variant featuring 256 entity types and a coarse-grain one with 53 entity types.Both versions have been derived from the Wikidata knowledge graph in a semi-automatic fashion.We also report results for two baselines using BERT, reaching up to 70.68% accuracy (10-way 1-shot).
Hybrid vs Ensemble Classification Models for Phishing Websites

Phishing is an internet crime achieved by imitating a legitimate website of a host in order to steal confidential information. Many researchers have developed phishing classification models that are limited in real-time and computational efficiency. This paper presents an ensemble learning model composed of DTree and NBayes, by STACKING method, with DTree as base learner. The aim is to combine the advantages of simplicity and effectiveness of DTree with the lower complexity time of NBayes. The models were integrated and appraised independently for data training and the probabilities of each class were averaged by their accuracy on the trained data through testing process. The present results of the empirical study on phishing website dataset suggest that the ensemble model significantly outperformed the hybrid model in terms of the measures used. Finally, DTree and STACKING methods showed superior performances compared to the other models.
Analysis of WebAssembly as a Strategy to Improve JavaScript Performance on IoT Environments

JavaScript language (JS) has been widely used in recent years applied to browsers-context. Yet JS is being applied to other backgrounds such as server-side programming, mobile applications, games, robotics, and the Internet of Things (IoT). JavaScript is suitable for programming IoT devices due to eventdriven oriented architecture. However, it is an interpreted language, so it has a lower performance than a compiled language. This paper assesses the use of WebAssembly as a strategy to improve the performance of JavaScript applications in the IoT environment. The experiments were performed on a Raspberry Pi using the Ostrich Benchmark Suite. We run the algorithms in JavaScript, WebAssembly, and C language while collecting data about device resource consumption. Our results showed that JavaScript performance could be improved by 39.81% in terms of execution time, a tiny gain in memory usage, and reduced battery consumption by 39.86% when using WebAssembly.
An empirical analysis of existing systems and datasets toward general simple question answering

In this paper, we evaluate the progress of our field toward solving simple factoid questions over a knowledge base, a practically important problem in natural language interface to database. As in other natural language understanding tasks, a common practice for this task is to train and evaluate a model on a single dataset, and recent studies suggest that SimpleQuestions, the most popular and largest dataset, is nearly solved under this setting. However, this common setting does not evaluate the robustness of the systems outside of the distribution of the used training data. We rigorously evaluate such robustness of existing systems using different datasets. Our analysis, including shifting of training and test datasets and training on a union of the datasets, suggests that our progress in solving SimpleQuestions dataset does not indicate the success of more general simple question answering. We discuss a possible future direction toward this goal.
Literature Review of Sentiment Analysis Techniques for Microblogging Site

Any opinion/review given by any of an individual through which the feelings, text message, attitudes and thoughts can be expressed is known as sentiment. The kinds of data analysis which is attained from the news reports, user reviews, social media updates or microblogging sites is called sentiment analysis which is also known as opinion mining. It is an approach which is used to analyse sentiment of input data. The reviews of individuals towards certain events, brands, product or company can be known through sentiment analysis. The responses of general public are collected and improvised by researchers to perform evaluations. The popularity of sentiment analysis is growing today since the numbers of views being shared by people on the microblogging sites are also increasing. All the sentiments can be categorized into three different categories called positive, negative and neutral. Twitter, being the most popular microblogging site, is used to collect the data to perform analysis. Tweepyis used to extract the source data from Twitter. Python language is used in this research to implement the classification algorithm on the collected data. The features are extracted using N-gram modeling technique. The sentiments are categorized among positive, negative and neutral using a supervised machine learning algorithm known as K-Nearest Neighbor.
E-Learning Integration with traditional learning in a university environment: Academic and administrative factors and conditions

Introduction. The paper addresses the problem of the optimal combination of traditional and distance (online) knowledge delivery systems in higher education. The purpose of this study is to identify and describe academic and administrative factors and conditions influencing the integration of traditional and distance learning in a university environment. Materials and Methods. The article reviews and analyses a number of Russian and international studies into distance (online) and traditional learning within the framework of higher education. A survey was conducted at Cherepovets State University (the Russian Federation). Participants of the study were students (n=110) and academic staff (n=20). The observation method was used in order to identify the level of student satisfaction in traditional and distance (online) learning. Results. The authors clarified modern interpretations of distance and traditional education and conducted a historical analysis of these concepts. The study revealed and described academic and administrative factors and conditions contributing to the effective integration of distance and traditional education. The results showed that most of the students and academic staff prefer a traditional education delivery system. In regards to tools and resources of distance (online) learning, most respondents are positive about using Microsoft Teams video conferencing software. Conclusions. The authors identified and clarified the following academic and administrative factors and conditions for the integration of distance and traditional education delivery systems: careful selection of educational online platforms, active use of reflection, a variety of communication channels for feedback, emotional factors, regulations for distance learning, and distance mentoring. Keywords Distance education; Traditional education; Integration; Academic and administrative factors and conditions; Students; Academic staff; Educational online platform
On the Relation between Complexity, Explicitness, Effectiveness of Refactorings and Non-Functional Concerns

Developers need to consistently improve the internal structural quality of a program to address its maintainability and possibly other non-functional concerns. Refactoring is the main practice to improve code quality. Typical refactoring factors, such as their complexity and explicitness (i.e., their self-affirmation), may influence its effectiveness in improving key internal code attributes, such as enhancing cohesion or reducing its coupling, complexity and size. However, we still lack an understanding of whether such concerns and factors play a role on improving the code structural quality. Thus, this paper investigates the relationship between complexity, explicitness and effectiveness of refactorings and non-functional concerns in four projects. We study four non-functional concerns, namely maintainability, security, performance, and robustness. Our findings reveal that complex refactorings indeed have an impactful effect on the code structure, either improving or reducing the code structural quality. We also found that both self-affirmed refactorings and non-functional concerns are usually accompanied by complex refactorings, but tend to have a negative effect on code structural quality. Our findings can: (i) help researchers to improve the design of empirical studies and refactoring-related tools, and (ii) warn practitioners on which circumstances their refactorings may cause a negative impact on code quality.
Cybercrime: Internet Driven Illicit Activities and Behavior

Abstract Cybercrime affects multiple areas of society with nefarious activities ranging from human trafficking to illegal arms sale. The Internet has allowed some nefarious activities to be revived as others have emerged in this new age. In turn, this has become a national security issue for countries as this requires resources to combat this evolving threat. These activities include undermining legitimate services that provide government services to its citizens, such as passport, national identification, tax services, and more. This paper introduces some of these activities, enabling readers to understand this digital criminal world further.
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning

We present a multi-task learning framework to enable the training of one universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-ofspeech tagging and utterance segmentation in a simple deep recurrent setting.We show that these tasks provide positive inductive biases to each other with optimal contribution of each one relying on the severity of the noise from the task.Our live multi-task model outperforms similar individual tasks, delivers competitive performance and is beneficial for future use in conversational agents in psychiatric treatment.
IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals

This paper describes our efforts in tackling Task 5 (Yang et al., 2020) of SemEval-2020.The task involved detecting a class of textual expressions known as counterfactuals and separating them into their constituent elements.Counterfactual statements describe events that have not or could not have occurred and the possible implications of such events.While counterfactual reasoning is natural for humans, understanding these expressions is difficult for artificial agents due to a variety of linguistic subtleties.Our final submitted approaches were an ensemble of various fine-tuned transformer-based and CNN-based models for the first subtask and a transformer model with dependency tree information for the second subtask.We ranked 4 th and 9 th in the overall leaderboard.We also explored various other approaches that involved the use of classical methods, other neural architectures and the incorporation of different linguistic features.