Reviewerly/exHarmony huggingface dataset:
Intermediate-Layer Transferable Adversarial Attack With DNN Attention

The widespread deployment of deep learning models in practice necessitates an assessment of their vulnerability, particularly in security-sensitive areas. As a result, transfer-based adversarial attacks have elicited increasing interest in assessing the security of deep learning models. However, adversarial samples usually exhibit poor transferability over different models because of overfitting of the particular architecture and feature representation of a source model. To address this problem, the Intermediate Layer Attack with Attention guidance (IAA) is proposed to alleviate overfitting and enhance the black-box transferability. The IAA works on an intermediate layer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> of the source model. Guided by the model’s attention to the features of layer <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> , the attack algorithm seeks and undermines the key features that are likely to be adopted by diverse architectures. Significantly, IAA focuses on improving existing white-box attacks without introducing significant visual perceptual quality degradation. Namely, IAA maintains the white-box attack performance of the original algorithm while significantly enhancing its black-box transferability. Extensive experiments on ImageNet classifiers confirmed the effectiveness of our method. The proposed IAA outperformed all state-of-the-art benchmarks in various white-box and black-box settings.
A Study on Finding Emergency Conditions for Automatic Authentication Applying Big Data Processing and AI Mechanism on Medical Information Platform

We had researched an automatic authentication-supported medical information platform [6].The proposed automatic authentication consists of user authentication and mobile terminal authentication, and the authentications are performed simultaneously in patients' emergency conditions.In this paper, we studied on finding emergency conditions for the automatic authentication by applying big data processing and AI mechanism on the extended medical information platform with an added edge computing system.We used big data processing, SVM, and 1-Dimension CNN of AI mechanism to find emergency conditions as authentication means considering patients' underlying diseases such as hypertension, diabetes mellitus, and arrhythmia.To quickly determine a patient's emergency conditions, we placed edge computing at the end of the platform.The medical information server derives patients' emergency conditions decision values using big data processing and AI mechanism and transmits the values to an edge node.If the edge node determines the patient emergency conditions, the edge node notifies the emergency conditions to the medical information server.The medical server transmits an emergency message to the patient's charge medical staff.The medical staff performs the automatic authentication using a mobile terminal.After the automatic authentication is completed, the medical staff can access the patient's upper medical information that was not seen in the normal condition.
Impact of Artificial Intelligence Driven Social Media Marketing on Millennials Dining Out Behavior

The purpose of conducting this research work is for examining the impact of artificial intelligence driven social media marketing on the buying behavior and restaurant patronizing showcased by millennial generation, further the study will evaluate the benefits and usability of AI empowered social media marketing on the performance of casual dining restaurants in context to Uttarakhand.As research design, a survey technique was adopted where primary data was extracted, examined, and interpreted.Participants for the study were the individuals from millennial generation, those who dine frequently in the selected casual dining restaurants in Uttarakhand.As result, practicality of AI tools like Recommendation engines, Chatbots & Sentiment analysis were announced based on the preferences showcased by the millennial respondents.
Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks

Deep neural networks achieve remarkable performance in multiple fields. However, after proper training they suffer from an inherent vulnerability against adversarial examples (AEs). In this work we shed light on inner representations of the AEs by analysing their activations on the hidden layers. We test various types of AEs, each crafted using a specific norm constraint, which affects their visual appearance and eventually their behavior in the trained networks. Our results in image classification tasks (MNIST and CIFAR-10) reveal qualitative differences between the individual types of AEs, when comparing their proximity to the class-specific manifolds on the inner representations. We propose two methods that can be used to compare the distances to class-specific manifolds, regardless the changing dimensions throughout the network. Using these methods, we consistently confirmed that some of the adversarials do not necessarily leave the proximity of the manifold of the correct class, not even in the last hidden layer of the neural network. Next, using UMAP visualisation technique, we projected the class activations to 2D space. The results indicate that the activations of the individual AEs are entangled with the activations of the test set. This, however, does not hold for a group of crafted inputs called the rubbish class. We confirm the entanglement of adversarials with the test set numerically using the soft nearest neighbour loss.
Earthquakes, communities and heritage: Telling stories of resilience through co-designed immersive media

In this article, we discuss the experimental application of iterative co-design, community-based and place-centred approaches in the creation of 3D immersive experiences of places that have been badly damaged or destroyed after natural catastrophic events. We take as our case study the community of Senerchia, hit and partially destroyed by the 1980 Irpinia earthquake in Italy. The main purpose of this paper is to analyse and discuss the co-creation process we undertook with residents of the town for the making of an immersive documentary Italia Terremotata which explores life before, during and after the earthquake. Our approach is based on design-thinking and aims to define an ethical framework for the creation of reflexive and multi-vocal 3D immersive storytelling systems that consider ‘vulnerability’ and ‘empathy’ as central design values of the iterative prototyping and co-creation cycle.
English Speaking’s Barriers of Foreign Learners

The ability to speak English in the era of Fourth Industrial Revolution is necessary for learners. However Gaining the ability of speaking English is still challenging for a lot of learners who live in English as Foreign Language countries. Most of learners find it difficult to speak English due to some barriers. The objective of this study is to explore English speaking’s barriers which face by foreign learners. The method used in this study is literature research method. To collect the data the writer used some references from articles, book, e book, and all the data related to this study. Based on the result and discussion, the writer concluded that the way of students gaining their speaking skill through learning, the exposure to English, the differences in grammar and phonology, anxiety, and motivation are some of barriers that foreign learners face in speaking English.
Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction of Tail-End Labels

Multi-label learning predicts a subset of labels from a given label set for an unseen instance while considering label correlations. A known challenge with multi-label classification is the long-tailed distribution of labels. Many studies focus on improving the overall predictions of the model and thus do not prioritise tail-end labels. Improving the tail-end label predictions in multi-label classifications of medical text enables the potential to understand patients better and improve care. The knowledge gained by one or more infrequent labels can impact the cause of medical decisions and treatment plans. This research presents a variation of concatenated domain-specific language models, multi-BioMed-Transformers, to achieve two primary goals: first, to improve F1 scores of infrequent labels across multi-label problems, especially with long-tail labels; second, to handle long medical text and multi-sourced electronic health records (EHRs), a challenging task for standard transformers designed to work on short input sequences. A vital contribution of this research is new state-of-the-art (SOTA) results obtained using TransformerXL for predicting medical codes. A variety of experiments are performed on the Medical Information Mart for Intensive Care (MIMIC-III) database. Results show that concatenated BioMed-Transformers outperform standard transformers in terms of overall micro and macro F1 scores and individual F1 scores of tail-end labels, while incurring lower training times than existing transformer-based solutions for long input sequences.
Nantong Blue Calico Image Dataset and Its Recognition

Nantong blue calico is a kind of important intangible cultural heritages in China. To better safeguard and inherit it in a digital way, it is necessary to construct a large-scale dataset for Nantong blue calico. As so far, however, we could not find a public dataset for blue calico. The goal of this paper is to give a public image dataset which named <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> tBC consisting of Nantong blue calico patterns and provide a baseline result for the recognition of Nantong blue calico patterns. In this paper, we perform several baseline experiments on the NtBC dataset, including handcrafted and deep feature based classification methods. we compare some handcrafted methods and four kinds of popular convolutional neural networks (CNNs), including ResNet-50, AlexNet, GoogLeNet-V1 and VGGNet-16. Experimental results show that ResNet-50 yields an accuracy of 93.8% in the recognition performance, which shows that it is efficient to classify blue calico patterns through deep learning methods. As a consequence, this result provides the current best baseline result for Nantong blue calico image recognition. We believe our <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> tBC will facilitate future research on Chinese traditional patterns development, fine grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/facebook/react.
Structural refinement types

Static types are a great form of lightweight static analysis. But sometimes a type like List is too coarse – we would also like to work with its refinements like non-empty lists, or lists containing exactly 42 elements. Dependent types allow for this, but they impose a heavy proof burden on the programmer. We want the checking and inference of refinements to be fully automatic.
Liquid proof macros

Liquid Haskell is a popular verifier for Haskell programs, leveraging the power of SMT solvers to ease users' burden of proof. However, this power does not come without a price: convincing Liquid Haskell that a program is correct often necessitates giving hints to the underlying solver, which can be a tedious and verbose process that sometimes requires intricate knowledge of Liquid Haskell's inner workings.
Challenges Encountered by Artificial Intelligence Applications in Today’s Interconnected World

In recent years, technological and scientific advancements in artificial intelligence (AI), along with one of its key subcategories, i.e., machine learning (ML) are growing exponentially. For example, the remarkable achievements in artificial intelligence occurred in 2021 included an AI model that can meritoriously integrate natural language processing with computer vision to create images from textual captions, and an AI framework that can diagnose dementia in short time, besides other captivating inventions. This research work, AI and ML are investigated, and some key features are presented, including their relevant subfields, methods, industries, main stages, and most importantly the technical challenges that encounter the importantly the technical challenges that encounter the mainly on the current challenges encountered by artificial intelligence and machine learning applications, where the pair of intelligence and machine learning applications, where the pair of ethics and transparency in AI are highlighted in terms of their meanings, and their importance to AI/ML. Subsequently, a framework for AI design and implementation is introduced entitled “PESTEL” which stands for Political, Economic, Social, Technological, Environmental, and Legal. It is predicted that the outcome of this research work will facilitate the incessant espousal of AI/ML in today’s interconnected world.
A Curriculum Learning Based Multi-agent Reinforcement Learning Method for Realtime Strategy Game

Real-time strategy games are one of the important scenarios for studying multi-agent reinforcement learning, and there have been some researchers who have achieved some results in the field. However, limited by problems such as the complexity of the environment, these methods not only take up a large number of computational resources but also require a long training time. We based on the idea of curriculum Learning, the training process of real-time strategy game models is turned into an incremental level training. It can reduce the time cost on model training and the amount of resources required. In this study, we use StarCraft 2 as a simulation environment for real-time strategy games, and use PPO as the base algorithm to design a reinforcement learning model training method that incorporates the idea of curriculum Learning. We hope that this study is a guide to improve the efficiency of multi-agent reinforcement learning.
Construction of Online Teaching Mode of College Sports Based on Neural Network Technology

Online teaching is becoming familiar in recent days for bridging the educational gap and long-distance coverage. The online teaching modes provide better visualization and representation of different subjects, including sports and physical education classes. Data-to-representation provides a better teaching model without interrupting the knowledge-based performance. For leveraging this feature, this article introduces a Conveyance-dependent Teaching Mode (CTM) paradigm. The proposed paradigm employs neural learning to improve the performance of different online sports sessions. This learning assigns rewards for different training modes based on student assessment and data representation. The flawless data and student performance assessments are balanced for improving teaching modes. The reward is updated using the sports curriculum, teacher recommendations, and student interests. Based on the assessment, the rewards are identified using the activation function. This generates the rewards for different sports training sessions improving the data accuracy. Further metrics such as representation, analysis rate, and time factors are verified to improve the performance of the proposed paradigm.
Genetic algorithm with a new round-robin based tournament selection: Statistical properties analysis

A round-robin tournament is a contest where each and every player plays with all the other players. In this study, we propose a round-robin based tournament selection operator for the genetic algorithms (GAs). At first, we divide the whole population into two equal and disjoint groups, then each individual of a group competes with all the individuals of other group. Statistical experimental results reveal that the devised selection operator has a relatively better selection pressure along with a minimal loss of population diversity. For the consisting of assigned probability distribution with sampling algorithms, we employ the Pearson's chi-square and the empirical distribution function as goodness of fit tests for the analysis of statistical properties analysis. At the cost of a nominal increase of the complexity as compared to conventional selection approaches, it has improved the sampling accuracy. Finally, for the global performance, we considered the traveling salesman problem to measure the efficiency of the newly developed selection scheme with respect to other competing selection operators and observed an improved performance.
Data Availability Statements in <i>The Journal of Organic Chemistry</i>, <i>Organic Letters</i>, and <i>ACS Organic &amp; Inorganic Au</i>

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTData Availability Statements in The Journal of Organic Chemistry, Organic Letters, and ACS Organic & Inorganic AuGéraldine Masson*Géraldine MassonInstitut de Chimie des Substances Naturelles, Université Paris Saclay, CNRS, UPR2301, 1 Avenue de la Terrasse, Gif-sur-Yvette, 91198 Cedex, France*Email: [email protected]More by Géraldine Massonhttps://orcid.org/0000-0003-2333-7047, Marisa C. Kozlowski*Marisa C. KozlowskiDepartment of Chemistry, Roy and Diana Vagelos Laboratories, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States*Email: [email protected]More by Marisa C. Kozlowskihttps://orcid.org/0000-0002-4225-7125, and Scott J. Miller*Scott J. MillerDepartment of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States*Email: [email protected]More by Scott J. Millerhttps://orcid.org/0000-0001-7817-1318Cite this: Org. Lett. 2022, 24, 37, 6697–6699Publication Date (Web):September 12, 2022Publication History Received26 August 2022Published online12 September 2022Published inissue 23 September 2022https://doi.org/10.1021/acs.orglett.2c02899Copyright © 2022 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views2250Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (3 MB) Get e-AlertsSUBJECTS:Elements,Gold,Optical properties,Organic chemistry,Quality management Get e-Alerts
Data-Driven Prediction of Experimental Hydrodynamic Data of the Manta Ray Robot Using Deep Learning Method

To precisely control the manta ray robot and improve its swimming and turning speed, the hydrodynamic parameters corresponding to different motion control variables must be tested experimentally. In practice, too many input control parameters will bring thousands of groups of underwater experiments, posing challenges to the duration and operability of the engineering project. This study proposes a generative adversarial network model to reduce the experimental period by predicting the hydrodynamic experimental time-series data of forces and torques in the three-coordinate directions in a Cartesian coordinate system through different combinations of motion control parameters. The motion control parameters include the rotation amplitude, frequency, and phase difference of the four steering gears which drive the pectoral fins. We designed the prototype and experimental platform and obtained 150 sets of experimental data.To prevent overfitting, the size of the dataset was expanded to 2250 groups by slicing time series, and the subsequences of varying lengths were extended to the same length by LSTM. Finally, the GAN model is used to predict the hydrodynamic time series corresponding to the different motion parameters. The results show that the GAN model can accurately predict the input both from the validation set and the unlearned interpolated motion parameters. This study will save experimental time and cost and provide detailed hydrodynamic experimental data for the precise control of manta rays.
Designing Distributed Chi-Fuzzy Rule based Classification System

Fuzzy Rule based Classification Systems (FRBCSs) are an important area of fuzzy logic and fuzzy sets. FRBCSs provides interpretable models with good classification rate. In the presence of large number of instances with high dimension in training data, the classical Chi’s FRBCSs’ rulebase becomes huge causing the classical model to be computationally expensive which also leads to the degraded interpretability. In this work, we propose the ‘Distributed Chi Fuzzy Rule Based Classification Systems’ (DCHI-FRBCSs). The proposed approach distributes the n-dimensional data into n-nodes where each node forms its own 1-dimensional rules for the dimension. The output from each node is the rule weights which are then aggregated to form the final rule weights, the class with highest final rule weight is the final output of the model. Aggregators play an important role in combining the information from several sources. Andness-directed OWA selects desired level of andness between attributes for OWA aggregator. In this paper, we explore an extension of DCHI-FRBCSs called ‘Andess-directed Distributed Chi Fuzzy Rule Based Classification System’ (α-DCHI-FRBCS) which incorporates andness-directed OWA operator as the aggregator function. Results over UCI datasets verify the superiority of our methodology.
Towards Interval-Valued Fuzzy Approach to Video Streaming Traffic Classification

This paper contributes to classifying video streaming traffic by exploring concepts of Interval-Valued Fuzzy Logic. This approach extends related works by considering the uncertainties generated by variations in network conditions and the parameter imprecision affecting the behavior of network flow, which increases the complexity to reach high accuracy in identifying the network traffic. Some evaluations using the interval-valued logic approach for video streaming traffic classification are presented using application tools and datasets to validate the proposal.
The System to Visualize Certified Workloads of Peer-Review with Blockchain

Peer-reviews are maintained by voluntary activities, which are invisible for evaluating workloads. However, the voluntary activities have become heavier burdens following the increase of scientific publications. Peer-reviews are important to maintain the current evaluation system for science and technology. In this research, regarding workloads of peer-review tasks, the prototype system with blockchain proposes to make invisibles visible. Blockchain has been discussed to be utilized for certifying documentations such as educational background and for guaranteeing research data. This research explores new possibilities about the visualization of conventional peer-review tasks and the function to show researchers’ peer-review contributions to their employers.