Reviewerly/exHarmony huggingface dataset:
Invariant Information Bottleneck for Domain Generalization

Invariant risk minimization (IRM) has recently emerged as a promising alternative for domain generalization. Nevertheless, the loss function is difficult to optimize for nonlinear classifiers and the original optimization objective could fail when pseudo-invariant features and geometric skews exist. Inspired by IRM, in this paper we propose a novel formulation for domain generalization, dubbed invariant information bottleneck (IIB). IIB aims at minimizing invariant risks for nonlinear classifiers and simultaneously mitigating the impact of pseudo-invariant features and geometric skews. Specifically, we first present a novel formulation for invariant causal prediction via mutual information. Then we adopt the variational formulation of the mutual information to develop a tractable loss function for nonlinear classifiers. To overcome the failure modes of IRM, we propose to minimize the mutual information between the inputs and the corresponding representations. IIB significantly outperforms IRM on synthetic datasets, where the pseudo-invariant features and geometric skews occur, showing the effectiveness of proposed formulation in overcoming failure modes of IRM. Furthermore, experiments on DomainBed show that IIB outperforms 13 baselines by 0.9% on average across 7 real datasets.
Machine learning for intrusion detection in industrial control systems: challenges and lessons from experimental evaluation

Abstract Gradual increase in the number of successful attacks against Industrial Control Systems (ICS) has led to an urgent need to create defense mechanisms for accurate and timely detection of the resulting process anomalies. Towards this end, a class of anomaly detectors, created using data-centric approaches, are gaining attention. Using machine learning algorithms such approaches can automatically learn the process dynamics and control strategies deployed in an ICS. The use of these approaches leads to relatively easier and faster creation of anomaly detectors compared to the use of design-centric approaches that are based on plant physics and design. Despite the advantages, there exist significant challenges and implementation issues in the creation and deployment of detectors generated using machine learning for city-scale plants. In this work, we enumerate and discuss such challenges. Also presented is a series of lessons learned in our attempt to meet these challenges in an operational plant.
Tripartite entropic uncertainty in an open system under classical environmental noise

The uncertainty principle is a remarkable and fundamental feature in quantum mechanics that suggests a significant lower bound to predict the results of arbitrary incompatible observables measured on a particle. In this work, we study the dynamics of a tripartite entropic uncertainty bound and quantum fidelity in a three non-interacting qubits model initially prepared in a maximally entangled pure Greenberger–Horne–Zeilinger state and then subjected to classical environmental noise in different and common environments. Interestingly, we find that the dynamics of the tripartite uncertainty bound and fidelity are strongly affected by the type of system–environment interaction, and the growth speed of the uncertainty bound is strongly influenced by the disorder of the environment. Explicitly, our results show that the uncertainty bound and fidelity can be improved when the qubits are coupled to the noise in a common environment.
A Data Sharing Scheme for GDPR-Compliance Based on Consortium Blockchain

After the General Data Protection Regulation (GDPR) was introduced, some organizations and big data companies shared data without conducting any privacy protection and compliance authentication, which endangered user data security, and were punished financially for this reason. This study proposes a blockchain-based GDPR compliance data sharing scheme, aiming to promote compliance with regulations and provide a tool for interaction between users and service providers to achieve data security sharing. The zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARK) algorithm is adopted for protecting data and ensure that the user’s private data can satisfy the individual requirements of the service provider without exposing user data. The proposed scheme ensures mutual authentication through the Proof of Authority consensus based on the Committee Endorsement Mechanism (CEM-PoA), and prevents nodes from doing evil using the reputation incentive mechanism. Theoretical analysis and performance comparison indicate that the scheme meets the confidentiality, availability, and other indicators. It has superiority in efficiency and privacy protection compared with other schemes.
Natural Language Video Localization with Learnable Moment Proposals

Given an untrimmed video and a natural language query, Natural Language Video Localization (NLVL) aims to identify the video moment described by the query.To address this task, existing methods can be roughly grouped into two groups: 1) propose-and-rank models first define a set of hand-designed moment candidates and then find out the best-matching one.2) proposal-free models directly predict two temporal boundaries of the referential moment from frames.Currently, almost all the propose-and-rank methods have inferior performance than proposal-free counterparts.In this paper, we argue that propose-and-rank approach is underestimated due to the predefined manners: 1) Hand-designed rules are hard to guarantee the complete coverage of targeted segments.2) Densely sampled candidate moments cause redundant computation and degrade the performance of ranking process.To this end, we propose a novel model termed LP-Net (Learnable Proposal Network for NLVL) with a fixed set of learnable moment proposals.The position and length of these proposals are dynamically adjusted during training process.Moreover, a boundary-aware loss has been proposed to leverage frame-level information and further improve the performance.Extensive ablations on two challenging NLVL benchmarks have demonstrated the effectiveness of LPNet over existing state-of-the-art methods 1 .
Piano Performance and Music Automatic Notation Algorithm Teaching System Based on Artificial Intelligence

Artificial intelligence is a subject that studies all kinds of human intelligent activities and their laws. It is developed on the basis of the cohesion of many disciplines such as computer science, politics, information system, neurophysiology, psychology, philosophy, and language. This paper aims to study how to build a computer or intelligent machine, including hardware and software, imitate and expand the human brain to perform thinking functions such as thinking, programming, arithmetic, and learning, solve complex problems that need to be handled by professionals, and better apply the artificial intelligence assistance system to the teaching of piano performance. In this paper, Prolog language and music-assisted learning system based on the ARM and SA algorithm are proposed, the principle and operation process of music automatic recording technology are deeply studied, and the system data of artificial intelligence are summarized and analyzed by using internal database, so as to find out the implementation principle and law of piano automatic recording system. So that the artificial intelligence assistant system can be better applied to music teaching. The experimental results show that, in the teaching system of piano performance and music automatic notation algorithm, the utilization rate of artificial intelligence auxiliary technology has reached 56.81 and is growing rapidly. Therefore, we can find that the artificial intelligence assistant system plays an important role in the teaching system of piano performance and automatic music notation.
The Middle East: What Money Can't Buy

Science and technology in the contemporary Middle East from Morocco to Iran constitutes an extraordinary paradox. This chapter focuses on the development of indigenous research &amp; development capabilities rather than the terms or nature of technology transfer. It argues that without such capabilities, whether utilized for military or civilian purposes, no effective transfer can be said to have occurred. The chapter adopts the conceptual framework of the economist Nathan Rosenberg that development consists of technological &quot;learning by doing&quot;; the ability to adapt, improvise, innovate, modify, and eventually to re-export imported technologies. Homes for research can be provided by universities, government ministries, public sector corporations, autonomous government labs, local private firms, foreign firms with local or regional branches, or various combinations and permutations. Several governments such as Egypt and Kuwait have established multi-purpose autonomous research institutes. Those in Egypt have been plagued by the problems of low salaries, part-time researchers, lack of users, sinecure posts, and lack of continuity.
PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning

The recent outbreak of novel Coronavirus disease or COVID-19 is declared a pandemic by the World Health Organization (WHO). The availability of social media platforms has played a vital role in providing and obtaining information about any ongoing event. However, consuming a vast amount of online textual data to predict an event's trends can be troublesome. To our knowledge, no study analyzes the online news articles and the disease data about coronavirus disease. Therefore, we propose an LDA-based topic model, called PAN-LDA (Pandemic-Latent Dirichlet allocation), that incorporates the COVID-19 cases data and news articles into common LDA to obtain a new set of features. The generated features are introduced as additional features to Machine learning(ML) algorithms to improve the forecasting of time series data. Furthermore, we are employing collapsed Gibbs sampling (CGS) as the underlying technique for parameter inference. The results from experiments suggest that the obtained features from PAN-LDA generate more identifiable topics and empirically add value to the outcome.
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models

Large pre-trained vision-language (VL) models can learn a new task with a handful of examples and generalize to a new task without fine-tuning.However, these VL models are hard to deploy for real-world applications due to their impractically huge sizes and slow inference speed.To solve this limitation, we study prompt-based low-resource learning of VL tasks with our proposed method, FEWVLM, relatively smaller than recent fewshot learners.For FEWVLM, we pre-train a sequence-to-sequence transformer model with prefix language modeling (PrefixLM) and masked language modeling (MaskedLM).Furthermore, we analyze the effect of diverse prompts for few-shot tasks.Experimental results on VQA show that FEWVLM with prompt-based learning outperforms Frozen (Tsimpoukelli et al., 2021) which is 31× larger than FEWVLM by 18.2% point and achieves comparable results to a 246× larger model, PICa (Yang et al., 2021).In our analysis, we observe that (1) prompts significantly affect zero-shot performance but marginally affect few-shot performance, (2) models with noisy prompts learn as quickly as hand-crafted prompts given larger training data, and (3) MaskedLM helps VQA tasks while PrefixLM boosts captioning performance.Our code is publicly available at https://github.com/woojeongjin/FewVLM
Hybrid AES-ECC Model for the Security of Data over Cloud Storage

A cloud computing environment provides a cost-effective way for the end user to store and access private data over remote storage using some Internet connection. The user has access to the data anywhere and at any time. However, the data over the cloud do not remain secure all the time. Since the data are accessible to the end user only by using the interference of a third party, it is prone to breach of authentication and integrity of the data. Moreover, cloud computing allows simultaneous users to access and retrieve their data online over different Internet connections, which leads to the exposure, leakage, and loss of a user’s sensitive data in different locations. Many algorithms and protocols have been developed to maintain the security and integrity of the data using cryptographic algorithms such as the Elliptic Curve Cryptography (ECC). This paper proposes a secure and optimized scheme for sharing data while maintaining data security and integrity over the cloud. The proposed system mainly functions by combining the ECC and the Advanced Encryption Standard (AES) method to ensure authentication and data integrity. The experimental results show that the proposed approach is efficient and yields better results when compared with existing approaches.
Automatic Handgun Detection with Deep Learning in Video Surveillance Images

There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training.
Cyber-resilience of Critical Cyber Infrastructures: Integrating digital twins in the electric power ecosystem

This contribution presents a model for cyber-resilience of critical cyber infrastructures (CCI) based on the implementation of a digital twin. It addresses the risks associated with the integration of computational, communication and physical aspects of CCIs. We focus specifically on cybersecurity in the electric power sector due both to its salience and to the potential risks associated to failures in guaranteeing resilience. Informed by the literature on information security management, situational awareness (SA) and common operational picture (COP), we derive an overarching model to provide CCIs’ actors with increased cyber situational awareness, common understanding of incidents and enhanced response capacity. On the practical side, the model seeks to minimize response time and to reduce the impact of cyber-attacks on the organizations and on society as a whole. We develop a process model and validate three design propositions through a formative evaluation in the context of a digital twin implementation in the EU electrical power sector. We discuss the implications of this model for further research as well as practical applications for the electrical power sector.
Answering Open-Domain Questions of Varying Reasoning Steps from Text

We develop a unified system to answer directly from text open-domain questions that may require a varying number of retrieval steps.We employ a single multi-task transformer model to perform all the necessary subtasks-retrieving supporting facts, reranking them, and predicting the answer from all retrieved documents-in an iterative fashion.We avoid crucial assumptions of previous work that do not transfer well to real-world settings, including exploiting knowledge of the fixed number of retrieval steps required to answer each question or using structured metadata like knowledge bases or web links that have limited availability.Instead, we design a system that can answer open-domain questions on any text collection without prior knowledge of reasoning complexity.To emulate this setting, we construct a new benchmark, called B QA, by combining existing one-and twostep datasets with a new collection of 530 questions that require three Wikipedia pages to answer, unifying Wikipedia corpora versions in the process.We show that our model demonstrates competitive performance on both existing benchmarks and this new benchmark.We make the new benchmark available at https: //beerqa.github.io/.The Lord of the Rings à "150 million copies" Q.The Ingerophrynus Gollum is named after a character in a book that sold how many copies?
Satisfaction of E-Learners with Electronic Learning Service Quality Using the SERVQUAL Model

Electronic learning (E-learning) is an innovative learning tool that provides an opportunity for technology-driven distance teaching.E-learning not only enhances learning quality, but it has also become the only medium of learning during the COVID-19 pandemic, when people have been forced to stay at home.Due to the pandemic, most countries have initiated online learning to continue the learning process, thus lessening the study gap.Against this backdrop, it is imperative to explore the perception and satisfaction level of e-learners regarding e-learning tools.In this study, a quantitative approach was conducted on the students of two leading public universities (graduates and postgraduates) to identify the impact of the determinants of the SERVQUAL model (reliability, responsiveness, assurance, empathy, and website design), as well as 'learning content', on overall perceived quality and satisfaction.In total, 895 respondents participated in the study, and data were analyzed with Amos 23 to confirm the hypotheses, utilizing structural equation modeling.The findings reveal that all the variables of the measurement model had a significant effect on perceived service quality and thus user satisfaction except for responsibility.The results make a significant contribution to those decision-makers and university authorities attempting to ensure e-learners' satisfaction.
A Smart Contract-Based Dynamic Consent Management System for Personal Data Usage under GDPR

A massive amount of sensitive personal data is being collected and used by scientists, businesses, and governments. This has led to unprecedented threats to privacy rights and the security of personal data. There are few solutions that empower individuals to provide systematic consent agreements on distinct personal information and control who can collect, access, and use their data for specific purposes and periods. Individuals should be able to delegate consent rights, access consent-related information, and withdraw their given consent at any time. We propose a smart-contract-based dynamic consent management system, backed by blockchain technology, targeting personal data usage under the general data protection regulation. Our user-centric dynamic consent management system allows users to control their personal data collection and consent to its usage throughout the data lifecycle. Transaction history and logs are recorded in a blockchain that provides trusted tamper-proof data provenance, accountability, and traceability. A prototype of our system was designed and implemented to demonstrate its feasibility. The acceptability and reliability of the system were assessed by experimental testing and validation processes. We also analyzed the security and privacy of the system and evaluated its performance.
Digitization of handwritten Devanagari text using CNN transfer learning – A better customer service support

Devanagari script is one of the bases of various language scripts in India. With the growth of computing and technology, manual systems are replaced by automated one. The purpose of this research is to automate the existing manual system for digitization of Devanagari script with the use of an automated approach so that it saves time, antique data. The prescriptions given by the expert doctors and the treatments which are present in ancient Vedic literature are useful for handling patients with serious diseases. Digitization helps in easy access, manipulation, and longer storage of this data. Unlike Western languages such as English, Devanagari, is a famous script in India which does not have formal digitization tools. This work employs the best suited techniques that are useful to enhance the recognition rate and configures a Convolutional Neural Network (CNN) for effective Devanagari handwritten text recognition (DHTR). This approach uses Devanagari handwritten character dataset (DHCD) which is a vigorous open dataset with 46 classes of Devanagari characters and each of this class has two thousand different images. After recognition, conflict resolution is subtle for effective recognition therefore, this approach provides an arrangement to the user to handle the conflicts. This approach obtains promising results in terms of accuracy and training time.
Transfer Learning-enabled Action Recognition for Human-robot Collaborative Assembly

Human-robot collaboration (HRC) is critical to today’s tendency towards high-flexible assembly in manufacturing. Human action recognition, as one of the core prerequisites for HRC, enables industrial robots to understand human intentions and to execute planning adaptively. However, existing deep learning-based action recognition methods rely heavily on a huge amount of annotation data, which may not be effective or realistic in practice. Therefore, a transfer learning-enabled action recognition approach is proposed in this research to facilitate robot reactive control in HRC assembly. Meanwhile, a decision-making mechanism for robotic planning is introduced as well. Lastly, the proposed approach is evaluated in an aircraft bracket assembly scenario to reveal its significance.
Leveraging Domain Knowledge in Multitask Bayesian Network Structure Learning

Network structure learning algorithms have aided network discovery in fields such as bioinformatics, neuroscience, ecology and social science. However, challenges remain in learning informative networks for related sets of tasks because the search space of Bayesian network structures is characterized by large basins of approximately equivalent solutions. Multitask algorithms select a set of networks that are near each other in the search space, rather than a score-equivalent set of networks chosen from independent regions of the space. This selection preference allows a domain expert to see only differences supported by the data. However, the usefulness of these algorithms for scientific datasets is limited because existing algorithms naively assume that all pairs of tasks are equally related. We introduce a framework that relaxes this assumption by incorporating domain knowledge about task-relatedness into the learning objective. Using our framework, we introduce the first multitask Bayesian network algorithm that leverages domain knowledge about the relatedness of tasks. We use our algorithm to explore the effect of task-relatedness on network discovery and show that our algorithm learns networks that are closer to ground truth than naive algorithms and that our algorithm discovers patterns that are interesting.