Reviewerly/exHarmony huggingface dataset:
Fuzzy System-Based Solutions for Traffic Control in Freeway Networks Toward Sustainable Improvement

In the scientific community, the topic of traffic control for promoting sustainable transportation in freeway networks is a relatively new field of research that is becoming increasingly relevant. Sustainability is a critical factor in the design and operation of mobility and traffic systems, which impacts the development of freeway traffic control strategies. According to sustainable notions, freeway traffic controllers should be designed to maximize road capacity, minimize vehicle travel delays, and reduce pollution emissions, accidents, and fuel consumption. The problem is full of uncertainty, there is no way to model the whole system analytically, thus a fuzzy modeling approach seems to be not only adequate but necessary. In this study, a Fuzzy Cognitive Map based model (FCM) and a connected simple Fuzzy Inference System (FIS) are presented, as the tools to analyze freeway traffic data with the goal of traffic flow modeling at a macroscopic level, in order to address congestion-related issues as the core of the sustainability improvement strategies. Besides presenting a framework of Fuzzy system-based controllers in freeway traffic, the results of this work indicated that FIS and FCM are capable of realizing traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing vehicles along alternative paths via the execution of suitable route guidance strategy.
Quantum Variational Multi-class Classifier for the Iris Data Set

Recent advances in machine learning on quantum computers have been made possible mainly by two discoveries. Mapping the features into exponentially large Hilbert spaces makes them linearly separable—quantum circuits perform linear operations only. The parameter-shift rule allows for easy computation of objective function gradients on quantum hardware—a classical optimizer can then be used to find its minimum. This allows us to build a binary variational quantum classifier that shows some advantages over the classical one. In this paper we extend this idea to building a multi-class classifier and apply it to real data. A systematic study involving several feature maps and classical optimizers as well as different repetitions of the parametrized circuits is presented. The accuracy of the model is compared both on a simulated environment and on a real IBM quantum computer.
Towards Adaptive Coaching in Piloting Tasks: Learning Pilots’ Behavioral Profiles from Flight Data

The use of machine learning techniques for safety analysis, incident and accident investigation, fault detection and also for the study of piloting behaviors has gained popularity within the aviation community. This paper present a methodology that uses machine learning for the analysis of piloting behaviors in order to highlight behavioral profiles of piloting (pilot gain) going beyond the norm ’high gain/low gain. Moreover pilot profiles are learned not from the whole data, but from meaningful flight segments to better characterize the change in behaviour style during a flight. These profiles can then be referred to experts in the field for their use in several operational frameworks (piloting assistance, training in a piloting task, monitoring of a piloting activity, etc.).
Demonstrating Feasibility of Blockchain-Driven Carbon Accounting – A Design Study and Demonstrator

Carbon accounting calls for innovative digital infrastructures. The Paris Agreement and the 26th UN Climate Change Conference provide the frame for designing information systems for carbon accounting. This paper explores blockchain as a technology for carbon accounting and, in particular, for the Product Carbon Footprint. This article analyses core architectural designs of carbon footprints, consortia, and smart contract infrastructure. Experiences from the implementation of a carbon footprint blockchain demonstrator are reported. A coffee supply chain exemplifies the approach. Hyperledger Fabric and Minifabric from Hyperledger Labs are the technical frameworks used.
LSB Steganography Using Three Level Arnold Scrambling and Pseudo-random Generator

Cryptographic Image Scrambling is a widely relevant method used to transform images that are coherent, into an incomprehensible form of the image, to make it more tolerant to cryptanalysis. To minimize the possibilities of information detection, we intend to propose a novel solution, that utilizes multilevel Arnold’s transformation along with LSB steganography to further improve the security of the encryption process. Addition of a pseudo-random generator enhances the solution by lowering the penetrability risk and confusing any such attacks from threatening sources. Metrics, like NCPR, UACI and PSNR values between original and stego-image, have been used that confirms the viability of our scrambling technique.
Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals

The ability to sequence unordered events is evidence of comprehension and reasoning about real world tasks/procedures.It is essential for applications such as task planning and multi-source instruction summarization.It often requires thorough understanding of temporal common sense and multimodal information, since these procedures are often conveyed by a combination of texts and images.While humans are capable of reasoning about and sequencing unordered procedural instructions, the extent to which the current machine learning methods possess such capability is still an open question.In this work, we benchmark models' capability of reasoning over and sequencing unordered multimodal instructions by curating datasets from online instructional manuals and collecting comprehensive human annotations.We find current state-of-the-art models not only perform significantly worse than humans but also seem incapable of efficiently utilizing multimodal information.To improve machines' performance on multimodal event sequencing, we propose sequence-aware pretraining techniques exploiting the sequential alignment properties of both texts and images, resulting in >5% improvements on perfect match ratio.
Timing Leakage Analysis of Non-constant-time NTT Implementations with Harvey Butterflies

Harvey butterflies and their variants are core primitives in many optimized number-theoretic transform (NTT) implementations, such as those used by the HElib and SEAL homomorphic encryption libraries. However, these butterflies are not constant-time algorithms and may leak secret data when incorrectly implemented. Luckily for SEAL and HElib, the compilers optimize the code to run in constant-time. We claim that relying on the compiler is risky and demonstrate how a simple code modification, naïve compiler misuse, or even a malicious attacker that injects just a single compiler flag can cause leakage. This leakage can reduce the hardness of the ring learning with errors (R-LWE) instances used by these libraries, for example, from $$2^{128}$$ to $$2^{104}$$ .
Crisis in Modern teaching: What went wrong?

Crisis in modern teaching is examined as a result of an inflow of the new generations of university students in the 2010s. This crisis is reflected in demolition of traditional text culture, progressing rejection of the cultural capital passive accumulation, emergence of the alternative educational programs, new requirements to the teaching materials, increasing difficulties in keeping attention of the students, erosion of their sustainable motivation, noticeable decline of the teachers’ authority, persistent attempts to question the assessment systems, spread of new ethic, and strengthening of students’ intentions to defend their rights.
A Novel Network-Node-Embedded Network Cognition Model Based on Knowledge Module for Strengthening the Thinking Capability of Intelligent Network

Network intellectualization enables networks to adapt to the dynamic environment by perceiving, thinking, and interpreting the overall environmental changes like a human. However, the thinking capability of existing intelligent networks is generally limited or even absent. To further strengthen the thinking capability, we propose a novel network-node-embedded network cognition model based on a knowledge module to provide superior network intelligence. The proposed model is designed based on the OODA cycle, which is named from its components of “Observe”, “Orient”, “Decide”, and “Act” modules, and is widely applied to improve the network intellectualization. We firstly reorganize the OODA cycle by extending the perceptive range of the “Observe” module and classifying the other three modules as “Learning Analysis” and “Optimization Decision-making” modules. Furthermore, we innovatively introduce a “Network Knowledge” module using the knowledge graph, which constructs the correlation between the network and its overall environmental changes to generate effective decisions. Besides, the proposed model is designed to be directly embedded in network nodes to verify and execute decisions simultaneously, eliminating the time delay between them. The use cases that illustrate the applicability of the proposed model are presented, and simulation results can demonstrate that the proposed model is feasible and effective.
The Aircraft and Its Manufacturing System: From Early Requirements to Global Design

The design of an aircraft manufacturing system depends highly on the design of the aircraft itself. In this work, we propose an approach based on conceptual modelling and optimization methods that allows to take into account the impact of an aircraft design on its assembly line design. We start by eliciting early requirements in a real industrial use case context. Using Goal Oriented Requirements Engineering, we highlight the dependencies between the systems as well as the key elements that must be optimized to obtain an optimal global system. Then, based on a conceptual model and operational research, we present the tool that we have developed to support the development of an optimal overall system. We analyse the experiments realized on different aircraft designs, and we identify and summarize the lessons learned from this experience.
IIITSurat@LT-EDI-ACL2022: Hope Speech Detection using Machine Learning

This paper addresses the issue of Hope Speech detection using machine learning techniques.Designing a robust model that helps in predicting the target class with higher accuracy is a challenging task in machine learning, especially when the distribution of the class labels is highly imbalanced.This study uses and compares the experimental outcomes of the different oversampling techniques.Many models are implemented to classify the comments into Hope and Non-Hope speech, and it found that machine learning algorithms perform better than deep learning models.The English language dataset used in this research was developed by collecting YouTube comments and is part of the task "ACL-2022:Hope Speech Detection for Equality, Diversity, and Inclusion".The proposed model achieved a weighted F1score of 0.55 on the test dataset and secured the first rank among the participated teams.
Performance Analysis of Thinning Algorithms for Offline-Handwritten Devanagari Words

Thinning or skeletonize is useful preprocessing step in pattern recognition systems such as character or word recognition system. In past few decades, various thinning algorithms are reported in literature for such type of systems to make them more reliable, independent of font variations and efficient. In this paper, performance of three thinning algorithms developed by Zhang-Suen [ZSu], Guo-Hall [GHa] and Lee-Kashyab-Chu [LKC] has been analyzed to check their suitability to skeletonize handwritten Devanagari words in terms of various objective (reduction rate, sensitivity measurement and thinness measurement) and subjective (mean opinion score) performance metrics. For the present work, the performance these algorithms has been tested using a handwritten Devanagari words database having 15-word-classes, collected from hundreds of writers. It has been observed that [LKC] thinning algorithm achieved higher reduction rate, thinness measurement and mean opinion score as compared with [ZSu] and [GHa] algorithms which show its more-suitability to skeletonize handwritten Devanagari words. Moreover, slightly-higher value of sensitivity measurement also depicts that resultant skeleton may contain some artifacts, redundant branches and lines caused by noise.
SPIRAL: SPellIng eRror Parallel Corpus for Arabic Language

Linguistic resources (corpus) have become a fundamental component in natural language processing over the last two decades, due to the role they play both in the testing and evaluation phase and in the development phase with the emergence of statistical and machine learning approaches. However, the development of such resources requires considerable time and effort. In this paper, we present the steps to build our parallel corpus SPIRAL, which will be a useful resource for research in the field of spelling error detection and correction in Arabic texts. SPIRAL is the result of a study dedicated to the universe of spelling errors, where we exploited the different taxonomies dedicated to spelling error in Arabic texts, to generate automatically more than 248 million possible erroneous words from 420,000 correctly spelled words.
An Investigation of Artificial Intelligence Application in Auditing

The auditing profession is changing due to the advent and enforcement of artificial intelligence (AI) in its field. The implementation of artificial intelligence is introducing the advantages of taking over manual processes and promoting effective value-added decision making by the auditors. This chapter discusses the development and use of artificial intelligence in auditing from the prospects of education, profession, and ethical implications. It is not sufficient for the auditors to have professional knowledge only but to also develop the acumen for the implementation of artificial intelligence. Undergraduate and postgraduate degrees are being revamped to include knowledge of AI. Additionally, auditors are catching up on AI developments through professional development courses. The audit process benefits from AI due to the coverage of all the transactions instead of relying only on a sample to make a judgment. Auditing firms are investing in contract analysis software to go through complex documents. However, the ethical implications of AI are new and regulatory authorities are still in the process of providing adequate coverage toward the use of AI. The bottom-line of implementing AI processes is that it is complementary to the function of auditing and not a replacement.
Hybrid Crossover Operator in Genetic Algorithm for Solving N-Queens Problem

A non-deterministic algorithm produces a diverse result even using the same input. These problems are classified as NP-class problems, and the N-Queens problem is one of them. In n × n chessboard, placement of queens follows the pattern of permutation; i.e., each queen must be settled on different row and column. The N-Queens problem involves placing n numbers of queens in such a way that no two queens attack one other in the same row, column, or diagonal location, also known as the NP-class problem. To put it in a simple way, we can say that no two queens have the same row, column, or diagonal. In the past, a variety of strategies were employed to find solutions to the N-Queens problem, each with its own set of complexity and drawbacks. In this research, a new hybrid crossover operator is used for solving the N-Queens problem with sizes ranging from 4 to 12.
Themes and Sentiments of Online Comments Under COVID-19: A Case Study of Macau

This study investigates the comments posted in two popular channels on YouTube (February to July 2020) that reveal Macau people’s concerns and feelings under the COVID-19 pandemic in terms of the themes elaborated and sentiments expressed. By themes, Macau people showed their concerns on the epidemic situation, economy, the problems it caused, and how the government reacted to the epidemic. In addition, the theme on Mainland China evolved around whether the Central Government would help Macau carry through this pandemic. Moreover, the sentiment results represent Macau people’s worries and uncertainty towards the pandemic. Our study casts light on understanding the living condition of people being confronted with the epidemic in terms of their linguistic behaviour.
FIBER: Fill-in-the-Blanks as a Challenging Video Understanding Evaluation Framework

Santiago Castro, Ruoyao Wang, Pingxuan Huang, Ian Stewart, Oana Ignat, Nan Liu, Jonathan Stroud, Rada Mihalcea. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.
Secure exchange and effectual verification of educational academic records using hyperledger fabric block chain system

This paper provides a solution to the educational certification issue by employing the blockchain network. The proposed permissioned blockchain network is implemented in hyperledger fabric. The proposed system provides various services to issuing institutions: verifying organisation; identification and authentication of the issuer; verify and securely share academic records to the recipients; and stores the academic records in the blockchain in a distributed manner, ensuring the privacy of stored records of the recipient. When compared to Ethereum, hyperledger fabric provides additional functionalities like efficient parallelism, concurrency, multiple transaction executions, and efficient commitments of the transaction into the ledger. The experimental analysis of the proposed system has been executed to test the performance of invoking and query transactions using Hyperledger Caliper. We analyse the throughput and transaction latency of the proposed work as well. The experimental results exhibit the proposed system achieves better transaction processing power and security compared to existing systems.
Enhanced Representation with Contrastive Loss for Long-Tail Query Classification in e-commerce

Query classification is a fundamental task in an e-commerce search engine, which assigns one or multiple predefined product categories in response to each search query.Taking clickthrough logs as training data in deep learning methods is a common and effective approach for query classification.However, the frequency distribution of queries typically has long-tail property, which means that there are few logs for most of the queries.The lack of reliable user feedback information results in worse performance of long-tail queries compared with frequent queries.To solve the above problem, we propose a novel method that leverages an auxiliary module to enhance the representations of long-tail queries by taking advantage of reliable supervised information of variant frequent queries.The long-tail queries are guided by the contrastive loss to obtain category-aligned representations in the auxiliary module, where the variant frequent queries serve as anchors in the representation space.We train our model with real-world click data from AliExpress and conduct evaluation on both offline labeled data and online AB test.The results and further analysis demonstrate the effectiveness of our proposed method.
Challenges and Future Directions in the Implementation of Quantum Authentication Protocols

Quantum computing is a powerful concept in the technological world that is critically valued in information security due to its enhanced computation powers. Researchers have developed algorithms that allow quantum computers to hack into information security concepts that were previously considered difficult, if not impossible, including asymmetric key cryptography and elliptic curve cryptography. Studies have been done to focus on improving security protocols through quantum computing to counter these vulnerabilities. One such focus is on the topic of quantum authentication (QA). However, while several QA protocols have been theorized, only a few have been implemented and further tested. Among the protocols, we selected and implemented five quantum authentication protocols to determine their feasibility in a real-world setting. In this late-breaking work, we discuss the difficulties and obstacles developers might face while implementing authentication protocols that use quantum computing.
Classification of Histopathological Breast Cancer Images using Pretrained Models and Transfer Learning

In the current situation, the timely diagnosis of cancer helps to increase the survival rate of the patients. The most common cancer in women is breast cancer. The histopathological images of the breast help in the diagnosis of breast cancer. In this work, the histopathological stained images are used to build a pretrained deep learning model for the prediction of breast cancer. The major pretrained models like Inception V3, AlexNet, MobileNetV2, VGG16, and ResNet are used for model building. For breast cancer segmentation of histopathological images, segmented regions are obtained using both U-Net and R2U-Net models. For classification, pretrained models like Inception V3, MobileNet, AlexNet, VGG net, and ResNet were used. The highest accuracy was for ResNet of 89% and the least accuracy for MobileNet of 78% for breast cancer classification using histopathological images.
Processor Security: Detecting Microarchitectural Attacks via Count-Min Sketches

The continuous quest for performance pushed processors to incorporate elements such as multiple cores, caches, acceleration units, or speculative execution that make systems very complex. On the other hand, these features often expose unexpected vulnerabilities that pose new challenges. For example, the timing differences introduced by caches or speculative execution can be exploited to leak information or detect activity patterns. Protecting embedded systems from existing attacks is extremely challenging, and it is made even harder by the continuous rise of new microarchitectural attacks (e.g., the Spectre and Orchestration attacks). In this article, we present a new approach based on count-min sketches for detecting microarchitectural attacks in the microprocessors featured by embedded systems. The idea is to add to the system a security checking module (without modifying the microprocessor under protection) in charge of observing the fetched instructions and identifying and signaling possible suspicious activities without interfering with the nominal activity of the system. The proposed approach can be programmed at design time (and reprogrammed after deployment) in order to always keep updated the list of the attacks that the checker is able to identify. We integrated the proposed approach in a large RISC-V core, and we proved its effectiveness in detecting several versions of the Spectre, Orchestration, Rowhammer, and Flush + Reload attacks. In its best configuration, the proposed approach has been able to detect 100% of the attacks, with no false alarms and introducing about 10% area overhead, about 4% power increase, and without working frequency reduction.