Reviewerly/exHarmony huggingface dataset:
Reflections on the Doctoral Consortium

This paper provides a reflective commentary on the British HCI Doctoral Consortium from the perspective of the Organising Committee. We discuss the approach to holding a Human Computer Interaction Doctoral Consortium in July 2020 and the outcomes. We reflect on our lived experience pre, during and post event, flagging issues and perspectives that emerged from the day, and possible implications for doctoral consortiums and training for HCI.
Interpreting Locked Photographic Data: The Case of Apollo 17 Photo GPN-2000-00113

Photography can be used for pleasure and art but can also be used in many disciplines of science, because it captures the details of the moment and can serve as a proving tool due to the information it preserves. During the period of the Apollo program (1969 to 1972), the National Aeronautics and Space Administration (NASA) successfully landed humans on the Moon and showed hundreds of photos to the world presenting the travel and landings. This paper uses computer simulations and geometry to examine the authenticity of one such photo, namely Apollo 17 photo GPN-2000-00113. In addition, a novel approach is employed by creating an experimental scene to illustrate details and provide measurements. The crucial factors on which the geometrical analysis relies are locked in the photograph and are: (a) the apparent position of the Earth relative to the illustrated flag and (b) the point to which the shadow of the astronaut taking the photo reaches, in relation to the flagpole. The analysis and experimental data show geometrical and time mismatches, proving that the photo is a composite.
An Analysis of Sustainability of Digital Economy in Select Member Countries of Asian Development Bank

Digital Economy is offering various opportunities these days. As an economic function digital platforms are helping nations to increase their revenues and reduce inequalities among the people. Similarly development banks are also working in this direction by providing finances and reducing infrastructure inefficiencies. In this paper an attempt has been made to assess the sustainability of digital economy among the top beneficiaries of Asian Development Bank. In this regard secondary data has been collected from various sources. T-distribution test is applied to prove the hypothesis. The study concluded that the digital adoption among selected Asian Development Bank members is significantly on par with world countries.
A DEIXIS ANALYSIS ON LALAYAAN SONG LYRIC OF MINAHASA’S MAENGKET DANCE

This study aims to identify and describe a Deixis on Lalayan song lyric Minahasa’s Maengket Dance. It is conducted in order to explain what are the deixis and what were the Deixis meaning used on Lalayaan song lyric Minahasa Maengket Dance. In conducting this study, descriptive method is used. The data are collected through interview and observation, the researchers spent many times to interviewing the informant and note the data that relation with this study. The data are analyzed by following the steps of data reduction, data display and drawing conclusion. the result shows that there are 3 types of deixis and be find 37 deixis that use on Lalayaan song of Minahasa Maengket Dance. It is suggested that understand deixis analysis on Lalayaan song Minahasa Maengket dance should be consider in order to get the real meaning that the researcher or speaker exactly meaning. Keywords : Deixis, Maengket, Lalayaan, Song lyric, Pragmatic
Gradient Descent Resists Compositionality

In this paper, we argue that gradient descent is one of the reasons that make compositionality learning hard during neural network optimization. We find that the optimization process imposes a bias toward non-compositional solutions. This is caused by gradient descent, trying to use all available and redundant information from input, violating the conditional independence property of compositionality. Based on this finding, we suggest that compositionality learning approaches considering only model architecture design are unlikely to achieve complete compositionality. This is the first work to investigate the relation between compositional learning and gradient descent. We hope this study provides novel insights into compositional generalization, and forms a basis for new research directions to equip machine learning models with such skills for human-level intelligence.
TGT: A Novel Adversarial Guided Oversampling Technique for Handling Imbalanced Datasets

With the volume of data increasing exponentially, there is a growing interest in helping people to benefit from their data regardless of its poor quality. One of the major data quality problems is the imbalanced distribution of different categories existing in the data. Such problem would affect the performance of any possible of analysis and mining on the data. For instance, data with an imbalanced distribution has a negative effect on the performance achieved by most traditional classification techniques. This paper proposes TGT (Train Generate Test), a novel oversampling technique for handling imbalanced datasets problem. Using different learning strategies, TGT guarantees that the generated synthetic samples reside in minority regions. TGT showed a high improvement in performance of different classification techniques when was experimented with five imbalanced datasets of different types.
Publisher Correction: Ensemble learning of diffractive optical networks

A Correction to this paper has been published: https://doi.org/10.1038/s41377-021-00473-1
A Supervised Approach to Aspect Term Extraction Using Minimal Robust Features for Sentiment Analysis

The instinct to know what others feel lays the foundation for the field of sentiment analysis which extracts opinion from text data and categorizes them as positive, negative or neutral. Beyond a report of the consolidated sentiment, the end-user is more interested to know what the product features that are talked about and what is the sentiment of the opinion holder towards each feature/aspect which leads to the task of aspect-level sentiment analysis. In this paper, the focus has been on the aspect extraction task of aspect-level sentiment analysis which extracts the features of the product that has been talked about in the reviews. The experiments have been reported on Bing Liu Customer Review Datasets consisting of five different categories DVD, Canon, MP3, Nikon and Cell phones. The strength of the model lies in the fact that a simple classifier that incorporates handling of imbalanced data, using a minimal set of robust features has been able to achieve comparable results with the state of art in aspect extraction task. The random forest classifier reported the best results across all domains with an F-measure ranging from 85.3 to 89.1.
Balancing training time vs. performance with Bayesian Early Pruning

Pruning is an approach to alleviate overparameterization of deep neural networks (DNN) by zeroing out or pruning DNN elements with little to no efficacy at a given task. In contrast to related works that do pruning before or after training, this paper presents a novel method to perform early pruning of DNN elements (e.g., neurons or convolutional filters) during the training process while preserving performance upon convergence. To achieve this, we model the future efficacy of DNN elements in a Bayesian manner conditioned upon efficacy data collected during the training and prune DNN elements which are predicted to have low efficacy after training completion. Empirical evaluations show that the proposed Bayesian early pruning improves the computational efficiency of DNN training with small sacrifices in performance. Using our approach we are able to achieve a 48.6% faster training time for ResNet-50 on ImageNet to achieve a validation accuracy of 72.5%.
TaskSet: A Dataset of Optimization Tasks

We present TaskSet, a dataset of tasks for use in training and evaluating optimizers. TaskSet is unique in its size and diversity, containing over a thousand tasks ranging from image classification with fully connected or convolutional neural networks, to variational autoencoders, to non-volume preserving flows on a variety of datasets. As an example application of such a dataset we explore meta-learning an ordered list of hyperparameters to try sequentially. By learning this hyperparameter list from data generated using TaskSet we achieve large speedups in sample efficiency over random search. Next we use the diversity of the TaskSet and our method for learning hyperparameter lists to empirically explore the generalization of these lists to new optimization tasks in a variety of settings including ImageNet classification with Resnet50 and LM1B language modeling with transformers. As part of this work we have opensourced code for all tasks, as well as ~29 million training curves for these problems and the corresponding hyperparameters.
GIDS: Anomaly Detection Using Generative Adversarial Networks

Cyber security in modern digital age has become a major challenge before an individual/organization to protect assets from malicious entities. Machine learning technique has been used in advanced intrusion detection system (IDS) which detects new attacks by analyzing existing attacks’ metrics with the help of rich collection of datasets. Generative Adversarial Networks (GAN) has found lots of attentions in recent times, in particular, for forgery detection in image data. GAN also shows its potential that can be used in text-based traffic inspection to check whether the traffic contains any suspicious strings. In this paper, we present an intrusion detection system using GAN, termed as GIDS, that detects anomalies in input strings with a reasonable accuracy. GIDS minimizes the mapping error without using an external encoder. The analysis and experimental results show that GIDS detects anomalies with an accuracy of 83.66%, while keeping false positive rate low.
THE USE OF MULTIMEDIA TECHNOLOGIES IN THE EDUCATIONAL SYSTEM AND TEACHING METHODOLOGY: PROBLEMS AND PROSPECTS

Today, due to the pandemic, the educational system is fully or partially operating at a distance in many countries. This will require students to organize lessons with new teaching methods. That is, it is expedient to use interesting multimedia tools so that the student can adapt to the learning environment and freely understand it. Within the framework of the article, research is carried out on the use and organization of multimedia technologies.multimedia, distance learning, electronic resources, Higher Education, Secondary Education, educational technology.
RankPose: Learning Generalised Feature with Rank Supervision for Head Pose Estimation.

We address the challenging problem of RGB image-based head pose estimation. We first reformulate head pose representation learning to constrain it to a bounded space. Head pose represented as vector projection or vector angles shows helpful to improving performance. Further, a ranking loss combined with MSE regression loss is proposed. The ranking loss supervises a neural network with paired samples of the same person and penalises incorrect ordering of pose prediction. Analysis on this new loss function suggests it contributes to a better local feature extractor, where features are generalised to Abstract Landmarks which are pose-related features instead of pose-irrelevant information such as identity, age, and lighting. Extensive experiments show that our method significantly outperforms the current state-of-the-art schemes on public datasets: AFLW2000 and BIWI. Our model achieves significant improvements over previous SOTA MAE on AFLW2000 and BIWI from 4.50 to 3.66 and from 4.0 to 3.71 respectively. Source code will be made available at: this https URL.
Analyzing Online Shopping Behaviors via a new Data-Driven Hesitant Fuzzy Approach

Understanding online shopping behaviors is crucial for the survival of many firms.Modeling the customers' online shopping behaviors is a complex problem that involves uncertainty, hesitancy, and imprecision since different generations have different attitudes toward e-commerce.In this study, a new data-driven, hesitant fuzzy cognitive map methodology evaluates the different generations' , namely, generations X, Y, and Z, online shopping behaviors.The model is constructed based on the technology acceptance model, diffusion of innovation theory, and extended unified theory of acceptance and technology use.The relations and the level of relations among the parameters are defined by using a data-driven approach.Utilizing a statistical approach enables us to define the relations among the parameters and customer behaviors better.The study's objective is to reveal the impact of different conditions on the customers' online shopping behaviors and help the decision-makers with their online shopping strategies.The statistical model has limitations since it does not reflect the hesitancy and imprecision inherent in customers' online shopping behaviors.We utilize hesitant fuzzy cognitive maps to reflect uncertainty and hesitancy and analyze different scenarios with this map.Different cognitive maps and three scenarios are developed for every generation type, and the customer behaviors are observed through these hesitant fuzzy cognitive maps.
Discrete Elephant Algorithms for Target Detection in Complex and Unknown Environments

Multi-agent systems as an active research axis in artificial intelligence (AI) have known a tremendous progress these last decades. Their main strength resides in the cooperation that makes agents intelligent as a group. The paradigm of multi-robot systems derives from the agent technology field. In this paper, we are interested in developing a multi-robot system for target search in complex and unknown environments because of the numerous applications that exist for this issue and that are present in the modern life. As AI tools, we exploit recent swarm intelligent algorithms such as EHO and ESWSA, as they have shown effectiveness and efficiency for several applications. As a first contribution, we propose discrete versions of these algorithms as the addressed issue is a discrete problem. Then based on these algorithms, we will build a robust multi-robot system capable to find a maximum number of targets by avoiding potential obstacles of complex and unknown environments. Experiments have been performed on public datasets and the results are very promising.
RETRACTED: The Bottlenecks and Countermeasures in the Transformation of Public English Teachers in Newly Upgraded Undergraduate Colleges based on the Analysis of Big Data

Abstract Since 2000, a number of newly-built undergraduate colleges have been established in China. From the perspective of the number of schools and the scale of training talents, newly-built undergraduate colleges account for about one-third of the total amount of higher-level undergraduate education from the analysis of big data. Under the new situation that the country is vigorously promoting the transformation of English teachers, with the upgrading of the national industrial structure and the development of emerging industries, it is particularly urgent to train high-level technical and skilled talents. In October 2015, the Ministry of Education, the National Development and Reform Commission, and the Ministry of Finance promulgated the “Guiding Opinions on Guiding the Transformation of Some Local Undergraduate Universities to Applied Types.”
An Optimized Digital Watermarking Approach in Wavelet Domain Based on Whale Optimization Algorithm for Color Image

A newly discovered color watermarking approach is instigating in this paper which is rely upon whale optimization algorithm. YIQ color space is more applicable for the human visual system in the real time applications so the color cover image is changed from RGB color transformation to YIQ color transformation. Moreover, perform three-level discrete wavelet transform - singular value decomposition to luminance component (Y). In the image watermark embedding approach, perform DWT to a watermark gray image after executing the scrambling encryption process using generalized Arnold transform (GAT). The proposed algorithm uses whale optimization algorithm to pick the precise multi-scaling factors. The results from simulation predicated that the proposed watermarking pattern is improved and satisfied the properties of watermarking such as imperceptibility and robustness.
Deep Face Recognizer Privacy Attack: Model Inversion Initialization by a Deep Generative Adversarial Data Space Discriminator

A variety of Machine Learning (ML) applications involve data of privacy-sensitive content. Face recognizer is one of them which due to training by user identities face images, it is inherent to user face image as a critical biometric data. A face recognition system can be subject to privacy attacks even though it deploys a complex model structure like a deeplearning-based one. Because as the ML models advance to carry more complexity in structure and parameters, the privacy attack trends develop too. Model Inversion Attack (MIA) pioneered by Fredrikson et al [1] was applied on a shallow neural network of face recognizer, and its capability of privacy leakage was approved. Their work was on a white-box scenario wherein besides the model structure, the privacy-non-sensitive data of the users are partially available and used by the attacker for generation and leakage of the user identity face images. The present work improves the extension of MIA to deep learning models of face recognizers while performing without any data of the users. Despite the complexity of the deep models as an obstacle, this work improves the capability of MIA in this matter. To aim this goal, it initializes its training procedure by a seed image approved by a GAN-trained discriminator of face image data-space via its output probability value. In targeting two users’ identities by MIA, the proposed technique approves its efficiency on a deep face recognition system. The recognition rates of the images generated by MIA associated with GAN data-space discriminator (GAN-DD) are higher than sole MIA and demonstrate efficiency improvement of deep MIA.
DELFOS: A Model for Multitemporal Analysis based on Contexts History

This paper presents Delfos, a model for multi-temporal analysis based on historical contexts. The article describes the model architecture, implementation, and tests with two real datasets to evaluate the functionality and its potential to real applications. The first scenario used historical data from an electric power transmission substation, and the second with data from the performance analysis of futsal athletes. The results obtained through inference rules perform with 100% accuracy of the Profiles creation and that these are a better representation of the present, than only the current context data of the application. The results on the prediction of contexts were relevant in the case of the existence of behavior patterns in the contexts of the analyzed entities. For the application related to the electric power transmission substation, the assertiveness was between 86% and 92% in a 12-hour interval. In the application of performance analysis of futsal athletes, we made predictions about the athletes movement, with results varying between 72% and 92% for the time of up to 20 seconds. The results were encouraging and show potential for implementing Delfos in real-life situations.
Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image

&lt;span id="docs-internal-guid-94842888-7fff-2ae1-cd5c-026943b95b7f"&gt;&lt;span&gt;Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy. &lt;/span&gt;&lt;/span&gt;
Adaptation of Architecture Analyses: An IoT Safety and Security Flaw Assessment Approach

Almost everything is connected nowadays or will be in the near future.This trend, called Internet of Things (IoT) or Cyber Physical Systems (CPS), is able to enhance multiple areas e.g. an individual's life, complex industrial processes or common medical treatments.Though, these improvements frequently affect safety and security critical topics.While this development has many advantages to bring, many challenges arise as well.Most approaches focus on safety and security analyses or monitoring tools determined to apply during run time.These approaches do not consider that plenty of the most dangerous vulnerabilities have to be addressed in the design phase already.Hence, we present an approach to adapt architecture analyses of IoT related areas to provide a holistic tool to assess flaws and possible countermeasures to design a save and secure CPS system.
RETRACTED: The Establishment of Artificial Intelligence Platform of Internet of Things Based on Computer Technology

Abstract With the continuous development and application of Internet technology, China has gradually stepped into the era of AI of Hings. Now people’s life is full of Internet product of artificial intelligence and the Internet of things application is the Internet Products of the two main direction of the polymerization in the A10. For example the present stage our country use of voice recognition, face recognition and unmanned are the application of artificial intelligence technology. On the other hand, the Internet of things as the product of the information technology revolution for the third time, development is also very quickly in recent years. Therefore, faced with these two emerging technologies, we began to try to combine the two to form the ARTIFICIAL intelligence Internet of Things. This paper mainly introduces the current development of Smart Internet of things at home and abroad, as well as the challenges in its future development.
Research on College Physics Online Teaching System Based on Course Group Construction

Abstract College physics course based on online teaching system can make the boring theoretical derivation process more intuitive and convenient with the help of the unique characteristics of multimedia technology and tools, so that students’ learning efficiency can be significantly improved, so it has high research value. Based on this, this paper first analyses the current situation and existing problems of college physics course teaching, then studies the necessity of constructing college physics online teaching platform, and finally gives the construction strategy of college physics online teaching system based on the construction of curriculum group.