Reviewerly/exHarmony huggingface dataset:
Dynamic Hypersphere Embedding Scale Against Adversarial Attacks

Learning robust features against adversarial attacks is a challenging task that requires highly complex models, especially on aerial images, because they are subject to environmental and adversarial changes. Embedding hypersphere normalization, along with adversarial settings, causes performance degradation and enables the feature to overlap. To address this, in this article, we propose a dynamic hypersphere embedding scale (DHS) method that remaps the normalized features to a relative scale to learn robust features. The proposed method combines the benefits of hypersphere embedding without scarifying softmax advantages. The DHS aggregates the normalized features and the non-normalized ones. It uses a hypersphere embedding to enforce maximum-margin to the features that yield shorter magnitude and utilizes a dynamic scale to avoid features overlapping in the case of adversarial attacks. We validate the DHS's effectiveness by embedding the adversarial training attacks such as Projected Gradient Descent (PGD), CW, and DeepFool. Empirical experiments revealed that the DHS improves the model performance by 12% when using the PGD attack, with less computation than legacy hypersphere models. Another set of experiments showed that the DHS does not obfuscate the gradient.
Design a Mechanism for Opinion Mining

Opinion Mining is the task of Information Extraction. One of the important parts of opinion mining is to collect the opinions of the focus groups. The collection of this information is very important as it helps to improve the customer-aligned services and improve the product quality through user and customer opinions. In order to collect the opinions there are various sources are available such as social media blogs, online product reviews, and trending discussions. As these information are available very easily there are new challenging dimensions that come across. Data authenticity is the key element in generating opinion mining. The purpose of this project is to learn the concepts of opinion mining and semantic analysis. The application takes input as opinions of students about various engineering colleges based on parameters such as Branch, faculty, fieldwork/practical, canteen, academics, placements, sports/extracurricular activities, performing subject classification of views using the concept of semantic similarity, and creating a semantic structure of views and then performing sentimental analysis on those received opinions. Finally, presenting the information in an effective manner by categorizing the opinions as positive, negative, and neutral comments along with a semantic net. The project will help students with proper decision-making in order to take admissions in engineering colleges for their bright future.
Hashing-based Batik Image Retrieval using Progressive Multi-Stage Training

This paper presents an effective way to generate binary feature descriptor for batik image retrieval. In this approach, the deep convolutional networks performs the embedding process of Batik image into vector space by exploiting the contrastive learning framework. This embedded vector is then projected into the Hamming space. Then, the batik image retrieval task is performed under the hashing approach. Progressive multi stage training with scrambled image is adopted to maximize the learning capability and capacity of the network. This training enhances the embedding vector representation and distribution. It can produce a more meaningful feature descriptor for the Batik image retrieval task. As documented in the experimental Section, the proposed method is superior compared to the other schemes in the Batik image retrieval system.
Task Space Vector Field Guiding for Motion Planning

The article deals with the problem of planning in the task space in the presence of vector fields, while verifying and validating the constraints in the configuration space. The proposed approach, called the Task Space Vector Field Rapidly-exploring Random Tree (TSVF-RRT) algorithm, extends the Task-Space Rapidly-exploring Random Trees (TS-RRT) algorithm by incorporating vector fields into the task space, while avoiding non-trivial constraints on the configuration space. The planner restricts the search to a lower dimensional space, minimizing the upstream functional. To evaluate the proposed approach, graphical simulations are presented, carried out using a planar manipulator of 10 DOF. Possible advantages that encourage further research in this line are discussed.
DISTiL: DIStributed Industrial Computing Environment for Trustworthy DigiTaL Workflows: A Design Perspective

Digitalisation is continuing to play an essential role in modernising Europe’s industrial capabilities, allowing companies to be well positioned for global competitiveness and sustainability. Data is viewed as an essential resource for economic growth, competitiveness, innovation, job creation and societal progress. As such, EU industry needs to develop highly integrated digital networks that can underpin the creation of innovative digital services. While the convergence of novel digital technologies are viewed as key enablers, their inherent complexity, heterogeneity and dynamicity create challenges for managing workflows and trust at scale. As a result valuable data assets are disparate sitting in silos across systems, roles and business functions and go unutilised. In addition, large volumes of data sit across organisations that can provide a rich cross-pollination of experience to identify common patterns, opportunities, and train robust models to support innovative data-driven services. The work presented here outlines an initial analysis of the system requirements, architectural considerations, and challenges that need to be overcome to realise distributed and trusted digital workflows with a focus on use cases in the domain of smart manufacturing.
On the Robustness of Quality Measures for GANs

This work evaluates the robustness of quality measures of generative models such as Inception Score (IS) and Fréchet Inception Distance (FID). Analogous to the vulnerability of deep models against a variety of adversarial attacks, we show that such metrics can also be manipulated by additive pixel perturbations. Our experiments indicate that one can generate a distribution of images with very high scores but low perceptual quality. Conversely, one can optimize for small imperceptible perturbations that, when added to real world images, deteriorate their scores. We further extend our evaluation to generative models themselves, including the state of the art network StyleGANv2. We show the vulnerability of both the generative model and the FID against additive perturbations in the latent space. Finally, we show that the FID can be robustified by simply replacing the standard Inception with a robust Inception. We validate the effectiveness of the robustified metric through extensive experiments, showing it is more robust against manipulation. Code: https://github.com/R-FID-Robustness-of-Quality-Measures-for-GANs
Improving GANs for Long-Tailed Data Through Group Spectral Regularization

Deep long-tailed learning aims to train useful deep networks on practical, real-world imbalanced distributions, wherein most labels of the tail classes are associated with a few samples. There has been a large body of work to train discriminative models for visual recognition on long-tailed distribution. In contrast, we aim to train conditional Generative Adversarial Networks, a class of image generation models on long-tailed distributions. We find that similar to recognition, state-of-the-art methods for image generation also suffer from performance degradation on tail classes. The performance degradation is mainly due to class-specific mode collapse for tail classes, which we observe to be correlated with the spectral explosion of the conditioning parameter matrix. We propose a novel group Spectral Regularizer (gSR) that prevents the spectral explosion alleviating mode collapse, which results in diverse and plausible image generation even for tail classes. We find that gSR effectively combines with existing augmentation and regularization techniques, leading to state-of-the-art image generation performance on long-tailed data. Extensive experiments demonstrate the efficacy of our regularizer on long-tailed datasets with different degrees of imbalance. Project Page: https://sites.google.com/view/gsr-eccv22 .
Few-Shot Segmentation via Rich Prototype Generation and Recurrent Prediction Enhancement

Prototype learning and decoder construction are the keys for few-shot segmentation. However, existing methods use only a single prototype generation mode, which can not cope with the intractable problem of objects with various scales. Moreover, the one-way forward propagation adopted by previous methods may cause information dilution from registered features during the decoding process. In this research, we propose a rich prototype generation module (RPGM) and a recurrent prediction enhancement module (RPEM) to reinforce the prototype learning paradigm and build a unified memory-augmented decoder for few-shot segmentation, respectively. Specifically, the RPGM combines superpixel and K-means clustering to generate rich prototype features with complementary scale relationships and adapt the scale gap between support and query images. The RPEM utilizes the recurrent mechanism to design a round-way propagation decoder. In this way, registered features can provide object-aware information continuously. Experiments show that our method consistently outperforms other competitors on two popular benchmarks PASCAL- $${{5}^{i}}$$ and COCO- $${{20}^{i}}$$ .
A flexible framework for requirement management (FFRM) from software architecture toward distributed agile framework

Abstract GSD is a Global software development environment where data are distributed to more than two sites. These sites may be located on national or continental borders. Every year it expands its development volume, which has become a trend for the software development business. Because of increasing demand, it has become a common type of business. The value of offshore software development has increased immensely. According to the recent predictions, one-quarter of US software-related business shifts offshore, including integration and management-related services. GSD helps explore resources from other countries, increasing knowledge and enhancing operational efficiency. Therefore, globalization has changed the development nature of software. Different organizations face several challenges, such as coordination and requirement ambiguity, during the change management process in GSD. Traceability accommodates these changes in forward and backward direction. However, it gives rise to several challenges like less client involvement because of its distributed nature and challenging to manage the requirement ambiguity due to increased cost. Therefore, the Flexible Framework for Requirement Management (FFRM) must handle the abovementioned issues.
Detection of Face Emotion and Music Recommendation System using Machine Learning

Face emotion detection has recently attracted a lot of interest because of its uses in computer vision and the field of human-computer interaction. Various methods and applications were suggested and put into use as a result of the ongoing research in this area. In this study, we present an emotion-recognition recommender system that can identify a user’s feelings and offer a selection of suitable songs that might lift his spirits. To gather information and enable us to give the users a selection of music tracks that are effective at lifting the users’ spirits, a quick search was undertaken to learn how music may impact the user mood in the short term. The suggested system recognizes emotions, and if the individual is feeling down, a special playlist including the best kinds of music will be played to lift his spirits. On the other hand, if a favorable mood is recognized, an appropriate playlist will be offered that contains several genres of music that will amplify the pleasant feelings. Principal Component Analysis (PCA) methods and the Fisher Face algorithm are used to implement the suggested recommender system.
Ad-RMS: Adaptive Regional Motion Statistics for Feature Matching Filtering

The Grid-based Motion Statistics (GMS) is a popular feature matching filtering method, and can effectively support 3D reconstruction systems such as ORB-SLAM and has been used effectively in many fields. However, the GMS divides the image into a certain number of grids with a fixed size, which cannot better reflect the feature information of the region. So that when large affine changes occur in the images, the grids cannot delineate a reasonable consistent region. Such a region division leads to errors and even failures in the subsequent process using the grid to reject error feature matching. As a consequence, this paper proposes an adaptive regional motion statistics method based on adaptive region division for region detection to replace the fixed grid division, which enhances the affine invariance of the feature matching filtering algorithm, and verifies the effectiveness of this paper's method through the precision experiments of feature matching and homography matrix.
A new construction method of digital signature scheme based on the discrete logarithm combining find root problem on the finite field F_p

The article proposes a method to build a signature scheme based on a new hard problem, called the logarithm problem with roots on the finite field . Now, this is a hard problem belonging to the class of unsolvable problems, except for the “brute force” method. Therefore, building a digital signature scheme based on the difficulty of this problem will most likely allow improving the security of the digital signature algorithm according to the proposed new method. In addition, the method of building signature schema here can be applied to develop a class of signature algorithms suitable for applications with high requirements for security in practice applications.
An anomaly detection of learning behaviour data based on discrete Markov chain

In order to overcome the problems of large anomaly detection error and long detection time in traditional learning behaviour data anomaly detection methods, this paper proposes a learning behaviour data anomaly detection method based on discrete Markov chain. This method analyses the types of learning behaviour data, and determines the influencing factors of learning behaviour data. With the help of support vector machine, the data extraction range is determined, and the data redundancy is determined to complete the data pre-processing. This paper analyses the basic principle of discrete Markov chain, constructs the discrete Markov chain model, and completes the detection of abnormal learning behaviour data. The experimental results show that the maximum detection error of the proposed method is about 2%, and the detection time is always less than 2.5 s.
THE EFFECTIVENESS OF URL FEATURES ON PHISHING EMAILS CLASSIFICATION USING MACHINE LEARNING APPROACH

Phishing email classification requires features so that the performance obtained produces good accuracy. One of the reasons for the lack of development of models for detecting phishing emails is the complexity of the feature selection. Feature selection is one of the essential parts of getting a good classification result, commonly used features are header, body, and Uniform Resource Locator (URL). Besides the email body text content, the URL is one of the leading indicators that the phishing attack successfully happened. The URL is commonly located on the body of the phishing email to get the victim's attention. It will redirect the victim to a fake website to obtain personal information from the victim. There is a lack of information about how the URL features affect the phishing email classification results. Therefore, this work focuses on using URL features to determine whether an email is phishing or legitimate using machine learning approaches. Two public datasets used in this work are the Online Phishing Corpus and Enron Corpus. The URL features are extracted using the Beautiful Soup library. Two machine learning classifiers used in this work are Support Vector Machine (SVM) and Artificial Neural Network (ANN). The experiments were divided into two based on features used in the classifiers. The first experiment used raw email data with URL features, while the second only used raw email data. The first experiment shows higher accuracy in both classifiers, SVM and ANN. Hence, this research proves that the impact of selecting URL features will increase the performance of the classification.
Fair Formation Control of Multiple Agents Using Ant Colony Optimization

This paper proposes a method to automatically generate consecutive formation transition paths of multiple agents, such as in unmanned aerial vehicle (UAV) shows, considering the reduced total travel distance as well as fair travel distances of individual agents. A UAV show is a type of entertainment where various artistic formation patterns are presented in the air by UAV agents, such as drones. Although several studies discussed the formation transition for UAV shows by considering the minimization of the total travel distance, few of them considered the fairness in moving distances among agents and transitions for long distances, which result in battery runout and shorter performance times Moreover, as a UAV show is performed by several agents, their battery capacities are usually not large; however, the performance time is affected by battery consumption. Our proposed method, which is based on ant colony optimization (ACO), enables agents to present longer performances in 3D space by considering the fairness in travel distance. Experiments showed that agents could perform approximately 20% more formation patterns without collisions with our proposed method based on ACO than those with the conventional method that is also based on ACO.
Combining Transformer with a Discriminator for Anomaly Detection in Multivariate Time Series

Anomaly detection in multivariate time series has been attracting attention in order to realize continuous stable operation of systems. As systems diversify and monitoring targets become more complex, the number and types of measurements obtained from sensors in the system have dramatically increased. It is necessary to instantly process a large amount of complex multivariate time series in order to determine anomalies with high detection accuracy. In this paper, we proposed TDAD, Transformer with a Discriminator for Anomaly Detection in multivariate time series. Introducing an adversarial training and attention mechanisms has improved extractions of detailed loss and time series features during model training. We compare the performance of TDAD with five other deep learning methods on five publicly available datasets and demonstrate that it can determine anomalies with high accuracy.
MLA-LSTM: A Local and Global Location Attention LSTM Learning Model for Scoring Figure Skating

Video-based scoring using neural networks is a very important means for evaluating many sports, especially figure skating. Although many methods for evaluating action quality have been proposed, there is no uniform conclusion on the best feature extractor and clip length for the existing methods. Furthermore, during the feature aggregation stage, these methods cannot accurately locate the target information. To address these tasks, firstly, we systematically compare the effects of the figure skating model with three different feature extractors (C3D, I3D, R3D) and four different segment lengths (5, 8, 16, 32). Secondly, we propose a Multi-Scale Location Attention Module (MS-LAM) to capture the location information of athletes in different video frames. Finally, we present a novel Multi-scale Location Attentive Long Short-Term Memory (MLA-LSTM), which can efficiently learn local and global sequence information in each video. In addition, our proposed model has been validated on the Fis-V and MIT-Skate datasets. The experimental results show that I3D and 32 frames per second are the best feature extractor and clip length for video scoring tasks. In addition, our model outperforms the current state-of-the-art method hybrid dynAmic-statiC conText-aware attentION NETwork (ACTION-NET), especially on MIT-Skate (by 0.069 on Spearman’s rank correlation). In addition, it achieves average improvements of 0.059 on Fis-V compared with Multi-scale convolutional skip Self-attentive LSTM Module (MS-LSTM). It demonstrates the effectiveness of our models in learning to score figure skating videos.
Piloting Virtual Reality Photo-Based Tours among Students of a Filipino Language Class: A Case of Emergency Remote Teaching in Japan

The State of Emergency declaration in Japan due to the COVID-19 pandemic affected many aspects of society in the country, much like the rest of the world. One sector that felt its disruptive impact was education. As educational institutions raced to implement emergency remote teaching (ERT) to continue providing the learning needs of students, some have opened to innovative interventions. This paper describes a case of ERT where Filipino vocabulary was taught to a class of Japanese students taking Philippine Studies in a Japanese university using a cognitive innovation based on virtual reality, an immersive technology often researched for immersion and presence. Students were divided into three groups to experience six lessons designed around virtual reality photo-based tours at different immersion levels. While the effect of immersion on satisfaction was not found to be statistically significant, presence and satisfaction were found to be correlated. Despite challenges that were encountered, benefits like enjoyment, increased engagement, and perceived learning were reported by the students. Our findings exemplify how emerging multisensory technologies can be used to enhance affective and cognitive dimensions of human experience while responding to gaps created by the spatial limitations of remote learning.
Semantic and Logic Modeling of Disaster Simulation for Multi-agent Systems

Disaster management is a complex collaborative process involving several stakeholders from different domains and requiring preparation for designing action plans.Computer simulation of such collaborative process allows globally assessing the efficiency of such preparation.Studies have shown that multi-agent systems (MAS) are well suited for identifying an optimal strategy or potential issues in the context of one or several action plans.Thus, our approach relies on a MAS for simulating action plans.We consider combining such an approach with Semantic Web technologies, in order to define the conceptual simulation model according to the preparation model of disaster management.The knowledge base is expressed through two different ontologies (semDM to model the preparation results and semMAS to model the simulation) that will be discussed in this paper.On top of these knowledge models, domain-specific constraints allow for checking consistency, and logic rules are used to define the semMAS modeling according to semDM.
An Anatomy of Crypto-Enabled Cybercrimes

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