Reviewerly/exHarmony huggingface dataset:
Effectiveness of Multiple-choice Questions (MCQS) Discussion as a Learning Enhancer in Conventional Lecture Class of Undergraduate Medical Students

Context: Multiple Choice Questions (MCQs) are one of the commonly used assessment tools in the field of medical education. A few initiatives have been made to use MCQ tests during classes as a mode of test-enhanced learning. However, there is a lack of enough scientific information regarding the use of MCQs as a learning enhancer. Aims: The present study has been undertaken to analyze the effectiveness of MCQ discussion as a learning tool. Methodology: The study was conducted in a Government Medical College in Kerala among Phase 2 MBBS students. They were divided into study and control groups of 61 students each. Online lecture classes were conducted for both groups on six topics of Pathology. For the study group, an additional MCQ discussion was done towards the end of the lecture class. Post-test was conducted on day 1 and after 2 weeks. A questionnaire was given to assess the attitude of students. Scores obtained in the post-tests were used to test the significance. Results: There was a significant difference in the mean score obtained between control and study groups in the post-test conducted on the same day suggesting that the MCQ discussion conducted at the end of the lecture class has added benefit ( P value = 0.04). There was no significant improvement in academic performance when an assessment was made after 2 weeks ( P value = 0.59). More than 80% of students showed a positive perception of this method. Conclusion: MCQ supplementation towards the end of the didactic lecture class is an effective teaching-learning method. Students also express a very positive attitude towards it. MCQs can be considered not only as an assessment tool but as a learning enhancer also.
Hindi Speech-Based Healthcare Chatbot

Health care is critical to lead a healthy life. However, in cases of unhealthy conditions, consulting with the doctor is very difficult. The proposed idea creates a medical or healthcare chatbot using Machine Learning (ML) and Natural Language Processing (NLP). Speech recognition API and Google Translate API are used to recognize the user’s voice and text translation, respectively. Along with NLP, text classification algorithms facilitate fast, cost-effective and scalable solutions. ML algorithms are implemented on the corpus for prediction and classification. Text data classification is essential in order to minimize manual work and spend time reading the report. In this paper, a corpus of nearly 5000 documents is classified based on the symptoms and using five machine learning algorithms. The misclassification error is used in contrast with technical progress. Out of the six experimented algorithms, Gaussian Naïve Bayes classifier shows the highest accuracy score and out performs all others. The combination of sentence similarity and keyword matching algorithms are used to extract appropriate answers to the particular class and display it to the user. In general, users are not conscious of all the medications or the signs and symptoms of the actual condition. For minor issues, users have to take a time-consuming inspection directly at the hospital. A survey states that around 850 million of 1.3 billion people speak Hindi in India. The proposed chatbot is for these people who can only speak or understand the Hindi language. The speech-based healthcare chatbot algorithms are designed with query interpretation and user message comprehension. The machine learning algorithm is implemented to predict the class and with the combination of sentence similarity and keyword matching algorithms to provide the user with relevant answers.
WIR-Transformer: Using Transformers for Wireless Interference Recognition

Wireless interference recognition (WIR) is one of the most indispensable technologies in non-cooperative communication systems. Recently, compared with convolutional networks, Transformers enjoy the ability of extracting global features and have achieved striking performance for WIR. However, the self-attention module in Transformers brings huge computational overhead that hinders deployment on resource-constrained devices. In this letter, we aim to alleviate the problem and propose WIR-Transformer. Specifically, WIR-Transformer introduces the division of regions and independently calculates self-attention in each region, which effectively reduces the complexity. To overcome the bottleneck of information blocking between regions, we propose an information exchange module (IEM). To further reduce the complexity, we introduce a novel patch aggregation module (PAM) to reduce the number of patches and fuse the local information. Experiments demonstrate that the proposed WIR-Transformer achieves higher accuracy as compared to conventional methods for WIR.
Scene Text Recognition: An Overview

Recent years have witnessed increasing interest in recognizing text in natural scenes in both academia and industry due to the rich semantic information carried by text. With the rapid development of deep learning technology, text recognition in natural scene, also known as scene text recognition (STR), has also made breakthrough progress. However, noise interference in natural scene such as extreme illumination and occlusion, as well as other factors, lead huge challenges to it. Recent research has shown promising in terms of accuracy and efficiency. In order to present the entire picture of the field of STR, this paper try to: 1) summarize the fundamental problems of STR and the progress of representative STR algorithms in recent years; 2) analyze and compare the advantages and disadvantages of them; 3) point out directions for future work to inspire future research.
Adversarial Attacks on Machine Learning Systems

Due to their impressive performance, Machine Learning Systems; including Deep Learning networks; are widely used in several application domains including sensitive areas like security applications, autonomous vehicles, medicines, weapon manufacturing and other areas which require high robustness and safety. However, these systems are vulnerable to adversarial attacks. This vulnerability has been investigated by researchers to improve the robustness of Machine Learning models and consequently increase confidence in their use. For this purpose, various attacks against these systems and defenses appeared and were classified. In this work, our objective is to provide a comprehensive review in the Adversarial Machine Learning field of research. Especially; to achieve our goal; we have detailed the Fast Gradient Sign Method as well as its variants, we have cited several adversarial attacks and their generated adversarial examples, we have also explained the similarity constraint and metrics that measure it in addition to other interesting notions like the adversary’s 3D model.
Rule-Based Credit Card Fraud Detection Using User’s Keystroke Behavior

In the digital era, security issue during online shopping is one of the prominent areas of the research for both the sides users and a businessman. In the current scenario, text-based authentication through one-time password is not reliable and also not capable of securing user's transactional details as it can easily be hacked by the hackers through remote surfing of hand-held devices, hidden cameras, credit and debit card cloning, etc. In the present work, keystroke dynamics is applied for minimizing the credit card fraud during online transactions by the users. The identity verification verifies using rule-based expert system, and user's behaviors are also calculated using keystroke dynamics when the user is typing transaction pin. The user's behavior such as typing pattern, time, and speed of a particular user can be recorded using the keyboard without the knowledge of the user and the service provider need not install any hardware devices; therefore, the proposed system is very cost effective, and it improves the security aspect for the online transaction. The proposed experimental model proves that it provides higher security and minimizing the credit card frauds while performing online transactions.
Automatic Spoken Language Acquisition Based on Observation and Dialogue

Human babies are born without knowledge of any specific language. They acquire language directly from observation and dialogue without being limited by the availability of labeled data. We propose spoken language acquisition agents that simulate the process. Such an ability requires multiple types of learning, including 1) word discovery, 2) symbol grounding, 3) message generation, and 4) pronunciation generation. Several studies have targeted one or combined learning types to elucidate human intelligence and aimed to equip spoken dialogue systems with human-like flexible language learning ability. However, their language ability was partially lacking some of the components. Our agents are the first to integrate them all. Our key concept is to design an architecture to integrate unsupervised, self-supervised, and reinforcement learning to utilize clues naturally existing in raw sensory signals and drive the learning based on the agent’s intrinsic motivation. Experimental results show agents successfully acquire spoken language from scratch by interacting with an environment to act by speaking. Our proposed focusing mechanism significantly improves learning efficiency. We also demonstrate that our agents can learn neural vocoder and the concept of logical negation as a part of language acquisition.
Auxiliary Local Variables for Improving Regularization/Prior Approach in Continual Learning

Regularization/prior approach emerges as one of the major directions in continual learning to help a neural network reduce forgetting the learned knowledge. This approach measures the importance of weights for previous tasks and then imposes a constraint on them in the current task without retraining on past data as well as extending the network architecture. However, regularization/prior-based methods face the problem in which weights can be moved intensively to the parameter region obtaining good performance for the current task but getting bad ones for previous tasks. In this paper, we present a novel solution in order to deal with this problem. Rather than using global variables as in the original methods, we add auxiliary local variables for each task that are considered as adjusting factors to suitably change the global ones to this task. As a result, the global variables can be preserved in a good region for all tasks to reduce the forgetting phenomenon. In particular, by imposing a variational distribution on the auxiliary local variables which are employed as multiplicative noise to the input of layers, we can achieve theoretical properties: Uncorrelated likelihoods, correlated pre-activation, and data-dependent regularization which are missing in the existing methods. These properties bring several benefits as follows: (1) Uncorrelated likelihoods between different data instances lead to reduce the high variance of stochastic gradient variational Bayes; (2) correlated pre-activation helps increase the representation ability for each task; and (3) data-dependent regularization guarantees to preserve the global variables in good region for all tasks. Our extensive experiments show that adding the local variables improves the performances of regularization/prior-based methods with significant magnitudes on several datasets. In particular, it makes several standard baselines approach SOTA results.
A Confounder-Free Fusion Network for Aerial Image Scene Feature Representation

The increasing number and complex content of aerial images have made some recent methods based on deep learning not fit well with different aerial image processing tasks. The coarse-grained feature representation proposed by these methods is not discriminative enough. Besides, the confounding factors in the datasets and long-tailed distribution of the training data will lead to biased and spurious associations among the objects of aerial images. This study proposes a confounder-free fusion network (CFF-NET) to address the challenges. Global and local feature extraction branches are designed to capture comprehensive and fine-grained deep features from the whole image. Specifically, to extract the discriminative local feature and explore the contextual information across different regions, the models based on gated recurrent units (GRUs) are constructed to extract features of the image region and output the important weight of each region. Further, the confounder-free object feature extraction branch is proposed to generate reasonable visual attention and provide more multi-grained image information. It also eliminates the spurious and biased visual relationships of the image on the object-level. Finally, the output of the three branches is combined to obtain the fusion feature representation. Extensive experiments are conducted on the three popular aerial image processing tasks: image classification, image retrieval, and image captioning. It is found that the proposed CFF-NET achieve reasonable and state-of-the-art results, including high-level task such as aerial image captioning.
Re-CorC-ing KeY: Correct-by-Construction Software Development Based on KeY

Deductive program verification is a post-hoc quality assurance technique following the design-by-contract paradigm where correctness of the program is proven only after it was written. Contrary, correctness-by-construction (CbC) is an incremental program construction technique. Starting with the functional specification, the program's correctness is guaranteed by application of a small set of refinement rules. Even though CbC is supposed to lead to code with a low defect rate and improve the traceability of errors, it is not widespread. One of the main reasons is insufficient tool support which we addressed with our tool CorC. CorC provides support for CbC-based program construction with the KeY program verifier as backend prover for checking correctness of refinement rule applications. However, CorC was limited to constructing single method bodies restricting its applicability. In this work, we introduce and discuss CorC 2.0, which extends CorC 's programming model with objects as used in object-oriented programming. We integrate CorC into a development process that allows to use post-hoc verification and CbC interchangeably to construct correct programs, and scale the applicability of CbC on the architectural level in our tool extension ArchiCorC. We developed three object-oriented case studies and evaluated the verification effort and the usability of CorC in comparison to post-hoc verification.
On-the-Fly Deformations for Keyword Spotting

Modern Keyword Spotting systems rely on deep learning approaches to build effective neural networks which provide state-of-the-art results. Despite their evident success, these deep models have proven to be sensitive with respect to the input images; a small deformation, almost indistinguishable to the human eye, may considerably alter the resulting retrieval list. To address this issue, we propose a novel "on-the-fly" approach which deforms an input image to better match the query image, aiming to stabilize the aforementioned sensitivity. Results on the IAM dataset verify the effectiveness of the proposed method, which outperforms existing Query-by-Example approaches. Code is available at https://github.com/georgeretsi/defKWS/ .
Differential Evolution with Biased Parameter Adaptation for ICSI-OC 2022 Competition

In this paper the differential evolution based algorithm is proposed for solving ICSI-OC'2022 benchmark functions. The presented NL-SHADE-LM algorithm uses several important modifications, such as biased parameter adaptation with generalized Lehmer mean with bias towards larger scaling factors, improved archive set update strategy, crossover rate sorting, as well as rank-based selective pressure. The performed computational experiments show that the proposed algorithm achieves highly competitive results.
Does the Effect of Financial Development on Economic Growth Depend on the Real Sector in Sub-Saharan Africa?

This study focused on the moderating role of the real sector on the financial development-economic growth nexus using panel data consisting of 38 sub-Saharan African countries for the period 1986–2015. This is necessary because of the potential discontinuities mediating finance-growth nexus that previous studies have not investigated. The study used the generalised method of moment as the estimation technique. The study found that none of the financial development indicators produces a positive effect on economic growth. Likewise, all four measures of the real sector failed to exert a positive impact on economic growth. The system GMM revealed that industrial value-added and agriculture value-added enhanced the effect of financial development on economic growth, while total factor productivity and service value-added failed to enhance the effect of financial development on economic growth. The study concluded that a well-developed real sector is required for the optimum impact of financial development on economic growth.
Integration of Software Architecture in Requirements Elicitation for Rapid Software Development

Software Architecture describes system components and their connections.Requirement elicitation catering the perspective of software architecture is quite challenging and relatively less explored research area for the rapid software development.It has gain growing interest due to reusability of existing modules with less cost and quick developmental time.Software architecture in the context of requirement engineering is an abstraction of software system performing a particular task with the help of group of executable architectural components.In this paper, systematic literature review is adapted as a methodology to explore software architectural elements that provides better performance and simplicity in requirement engineering.We analyzed, reviewed and listed the strategies, tools & techniques along with state-of-the-art mechanisms, pros and cons and application areas.Architectural components that are already implemented in the requirement elicitation process for effective software architectural design are briefly analyzed.Purpose of the paper is to explore and discuss the elements that make software architecture more integral and flexible for traceability of requirements.Another purpose is to identify relation between the software requirements and architecture along with exploring the components to bridge gap between requirements and architecture by critically evaluating industrially and academically proposed methods, tools and frameworks.We also highlighted the open research challenges of Software architecture in requirement elicitation for better software development.In the later section, a resource bank is created acting as a valuable model that encompasses targeted relevant groups, sub-groups with latest software architecture tools & techniques, methods and framework sources to facilitate effective requirement engineering.
Path Optimization and Control of Mobile Robot Using Modified Cuckoo Search Algorithm

Cuckoo search (CS) algorithm is a nature-inspired metaheuristic optimization algorithm. This algorithm is rooted on the brood parasitism of cuckoo bird. The modified algorithm is applied on path optimization of mobile robot in unknown space. The optimization algorithm is getting the primary inputs from the sensors of mobile robot. The inputs are obstacles distance and heading angle. For authentication of proposed algorithm, simulation in V-rep software and real-time experiment through Khepera-III robot is performed. The results acquired by the simulation and experiments are equated with each other and observed a good promise between both the results as the difference in results is less than 5%.
Real-Time Citizen Problem Detection From Twitter Data Using Naive Bayes Classifier

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
A Traceability Localization Method of Acoustic Attack Source for MEMS Gyroscope

The malicious acoustic attack can easily cause the failure of micro-electro mechanical systems (MEMS) sensors. It can use ultrasonic waves to attack the MEMS gyroscope over long distances. This type of attack is highly hidden and harmful. Therefore, the purpose of this letter is to locate malicious sources and facilitate the eradication of the threat. We designed a prototype system to simulate an ultrasonic attack on an MEMS gyroscope, which can collect and analyze the state of the gyroscope after being attacked. Furthermore, we proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> -standard deviation-based feature extraction, and then designed a neural-network-based traceability location method to seek for attacker’s location. The experimental results indicate that our approach can effectively and accurately locate the malicious acoustic source location.
Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based extraction methods are implemented in online systems. Moreover, our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition. To this end, we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums. Subsequently, we carried out detailed experiments for character recognition, as detailed in the results. The proposed CNN model outperformed as compared to previously published approaches.
Cryptocurrency, the Future of India

While some discerning investors are increasingly becoming disenchanted with virtual currencies like Bitcoin as a result of recent price drops, others believe it is too early to declare it a dead end. In this study, we try to find an answer to the burning question: to invest or not to invest? The paper looks at a variety of facets of cryptocurrency platforms in an effort to address the research's key questions, "Will cryptocurrency be the next money platform?" and "Will cryptocurrency be the next currency platform?" "Is it possible to use virtual currency platforms?" In this paper, we try to elaborate these issues. For this, we collect primary as well as secondary data for the analysis. The primary data was collected from Delhi. The respondents were asked question about cryptocurrency to understand the preliminary impression of cryptocurrency's use, development, trustworthiness, and future expectations. Due to the vast amount of cryptocurrency that is flowing through multiple systems, the massive expansion and growth of using and implementing cryptocurrencies, and the possibilities that cryptocurrencies provide, the finding of our research suggests that cryptocurrency is quite likely to be the future currency platform.
A Novel Multi-Scale Architecture Driven by Decoupled Semantic Attention Transfer For Person Image Generation

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI