Reviewerly/exHarmony huggingface dataset:
User-Avatar Relationships in Various Contexts

Avatars are in use when interacting in virtual environments in different contexts, in collaborative work, as well as in gaming and also in virtual meetings with friends. Therefore it is important to understand how the relationship between user and avatar works. In this study, an online survey is used to determine how the perception of an avatar changes in different contexts by relating it to existing avatar relationship typologies. Additionally, it is determined whether in each context a realistic, abstract or comic-like representation is preferred by the participants. One result was a preference of low poly representations in the work context, which are associated with the perception of the avatar as a tool. In the context of meeting friends, a realistic representation is perceived as more appropriate, which is perceived as an accurate self-representation. In the gaming context, the results are less clear, which can be attributed to different gaming preferences. Here, unlike in the other contexts, a comic-like representation is also perceived as appropriate, which is associated with the perception of the avatar as a friend. A symbiotic user-avatar relationship is not directly related to any form of representation, but always lies in the midfield, which is attributed to the fact that it represents a whole spectrum between other categories.
NOSnoop: An Effective Collaborative Meta-Learning Scheme Against Property Inference Attack

Collaborative learning has been used to train a joint model on geographically diverse data through periodically sharing knowledge. Although participants keep the data locally in collaborative learning, the adversary can still launch inference attacks through participants’ shared information. In this article, we focus on the property inference attack during model training and design a novel defense mechanism, namely, NOSnoop, to defend such an attack. We propose a collaborative meta-learning architecture to learn the common knowledge over all participants and utilize the natural advantage of meta-learning to hide the sensitive property data. We consider both irrelevant property and relevant property preservation in NOSnoop. For irrelevant property preservation, we utilize the inherent advantage of meta-learning to hide the sensitive property data in meta-training support data set. Thus, the adversary cannot capture the key information related to the sensitive properties and cannot infer victim’s private property successfully. For relevant property preservation, an adversarial game is further proposed to reduce the inference success rate of the adversary. We conduct comprehensive experiments to evaluate the effectiveness of NOSnoop. When hiding the sensitive property data in meta-training support data set, NOSnoop achieves an inference AUC score as low as 0.4984 for irrelevant property preservation, meaning the adversary cannot distinguish whether the training batch has the sensitive property data or not. When preserving the relevant property, NOSnoop is able to achieve an inference AUC score of 0.5091 without compromising model utility.
Realization of Deutsch–Jozsa Algorithm in Rydberg Atoms by Composite Nonadiabatic Holonomic Quantum Computation with Strong Robustness Against Systematic Errors

Abstract Deutsch–Jozsa (DJ) algorithm, as the first example of quantum algorithm, performs better than any classical algorithm for distinguishing balance and constant functions. Here, a scheme to implement the DJ algorithm in Rydberg atoms using the composite nonadiabatic holonomic quantum computation (NHQC) is presented. Taking advantages of composite loops and holonomic features to resist systematic errors, the scheme of composite NHQC works more robustly compared with the standard dynamic counterparts. By exemplifying the DJ algorithm implementation, the performance of single‐loop NHQC‐, composite NHQC‐, and the dynamic counterpart‐based processes are compared both analytically and numerically, indicating the best robustness of composite scheme to systematic errors. In addition, the combination of composite NHQC and quantum algorithm also provides an alternative pathway for the realization of robust quantum algorithm in the future.
Generalization bottleneck in deep metric learning

Deep metric learning aims to learn a non-linear function that maps raw-data to a discriminative lower-dimensional embedding space, where semantically similar samples have larger similarity than dissimilar ones. Most existing approaches process each raw-data in two steps, by mapping the raw-data to a higher-dimensional feature space via a fixed backbone, followed by mapping the higher-dimensional feature space to a lower-dimensional embedding space via a linear layer. This paradigm, however, inevitably leads to a Generalization Bottleneck (GB) problem. Specifically, GB refers to a limitation that the generalization capacity of lower-dimensional embedding space is inferior to the higher-dimensional feature space in the test stage. To mitigate the capacity gap between feature space and embedding space, we propose to introduce a fully-learnable module, dubbed Relational Knowledge Preserving (RKP), that improves the generalization capacity of lower-dimensional embedding space by transferring the mutual similarity of instances. Our proposed RKP module can be integrated into a general deep metric learning approach. And, experiments conducted on different benchmarks show that it can significantly improve the performance of original model.
Memory augmented convolutional neural network and its application in bioimages

The long short-term memory (LSTM) network underpins many achievements and breakthroughs especially in natural language processing fields. Essentially, it is endowed with certain memory capabilities to boost its performance. Currently, the volume and speed of big data generation are increasing exponentially, and such data require efficient models to acquire memory augmented knowledge. In this paper, we propose a memory augmented convolutional neural network (MACNN) with utilizing self-organizing maps (SOM) as the memory module. First, we depict the potential challenge about just applying solely a convolutional neural network (CNN) so as to highlight the advantage of augmenting SOM memory for better network generalization. Then, we dissert a corresponding network architecture incorporating memory to instantiate the distributed knowledge representation machanism, which tactically combines both SOM and CNN. Each component of the input vector is connected with a neuron in a two-dimensional lattice. Finally, we test the proposed network on various datasets and the experimental results reveal that MACNN can achieve competitive performance, especially for bioimages datasets. Meanwhile, we further illustrate the learned representations to interpret the SOM behavior and to comprehend the achieved results, which indicates that the proposed memory-incorporating model can exhibit the better performance.
A formal analysis of performance-security tradeoffs under frequent task reconfigurations

Reconfiguration can be used to improve system performance, increase fault-tolerance, recover from failures, prevent and recover from cyberattacks. The process of using reconfiguration to increase a system’s resilience to cyberattacks is called Moving Target Defense (MTD). While system reconfiguration has many advantages, it brings potential disadvantages such as performance and availability degradation when a system is undergoing reconfiguration. This paper analyzes the use of MTD to reduce the frequency of cyberattacks and develops a closed-form analytic model that captures the tradeoffs between resilience to cyberattacks and system performance. The state-of-the art in MTD discusses how proposed methods operate and uses experimentation to demonstrate their use. This paper fills a research gap in terms of developing formal models that analyze tradeoffs between performance and resilience to cyberattacks. More specifically, the main contributions of this paper include: (1) A closed-form analytic model for computing the probability that an attacker is successful in completing the reconnaissance phase of the attack and the computation of the task’s execution time when regular reconfigurations are used. (2) An optimization model for determining the optimal task reconfiguration frequency in a way that takes into account given performance-security tradeoffs. (3) A closed-form analytic model for computing the probability that an attacker is successful in completing the reconnaissance phase of the attack and the computation of the task’s execution time when irregular reconfigurations are used. (4) Derivation of the maximum probability that an attacker fails in completing the reconnaissance phase under irregular reconfiguration intervals.
FPGA Based Meteorological Monitoring Station

In this paper, we propose to implement a meteorological monitoring station using embedded systems. This model is possible thanks to different sensors that enable us to measure several environmental parameters, such as i) relative humidity, ii) average ambient temperature, iii) soil humidity, iv) rain occurrence, and v) light intensity. The proposed system is based on a field-programmable gate array device (FPGA). The proposed design aims at ensuring high-resolution data acquisition and at predicting samples with precision and accuracy in real-time. To present the collected data, we develop also a web application with a simple and friendly user interface.
A Siamese Template Matching Method for SAR and Optical Image

Template matching between synthetic aperture radar (SAR) and optical image is a meaningful and important topic for remote sensing tasks. In this letter, a deep learning framework based on Siamese structure and U-Net is proposed. First, a sparse 3-D transform-domain collaborative filtering (BM3D) for SAR image denoising is applied. Then, deep features of SAR and optical images are extracted from two branches of the proposed Siamese U-Net network. Finally, a cross correlation layer is used to generate the heatmap of image pairs based on the deep features extracted, and the peak maximal location of the heatmap is seen as the best matching location result. During training, a balanced loss between ground truth and prediction is used to get trained model weights in order to make a balance between positive samples and negative samples. The experiments on the same test dataset demonstrate that the proposed method improves the matching accuracy and precision to some degree in comparison with existing template matching methods for SAR and optical images.
Research on Teaching Evaluation of Courses Based on Computer System Ability Training

The courses based on computer system ability training has its particularity of training objective. To achieve objective feedback of teaching, a multi-subject and multi-dimensional hierarchical teaching evaluation system is proposed based on the analysis of computer system ability training objectives. Then, the teaching content, implementation, learning experience and teaching effect were evaluated by supervisors, colleague teachers and students. Finally, the multi-dimensional evaluation results show that the evaluation system proposed is effective and provides a reference for teaching evaluation.
Human Kidney Tissue Image Segmentation by U-Net Models

Segmentation approaches based on deep neural networks are researched for the microscopical images of the human kidney tissues. Several existing methods, used for medical imaging analysis and based on neural networks, were examined. Among several U-Net architectures, which are widely used for image segmentation, some their variations demonstrated the quite high performance despite the 4 times lower model size. As a result, the reasonable precision was obtained by a rudimentary network architectures and limited train time augmentations. It will open the promising perspectives for their deployment of the Edge Computing devices with the limited computing resources.
Multi-Modal Dialogue Policy Learning for Dynamic and Co-operative Goal Setting

Developing an adequate and human-like virtual agent has been one of the primary applications of artificial intelligence. In the last few years, task-oriented dialogue systems have gained huge popularity because of their upsurging relevance and positive outcomes. In real-world, users may not always have a predefined and rigid task goal beforehand; they upgrade/downgrade/change their goal component dynamically depending upon their utility value and agent's serving capability. However, existing virtual agents fail to incorporate this dynamic behavior, leading to either unsuccessful task completion or an ungratified user experience. The paper presents an end to end multimodal dialogue system for dynamic and co-operative goal setting, which incorporates i) a multi-modal semantic state representation in policy learning to deal with multi-modal inputs, ii) a goal manager module in a traditional dialogue manager for handling dynamic and goal unavailability scenarios effectively, iii) an accumulative reward (task/persona/sentiment) for task success, personalized persuasion and user-adaptive behavior, respectively. The obtained experimental results and the comparisons with baselines firmly establish the need and efficacy of the proposed system.
Learning Consistency- and Discrepancy-Context for 2D Organ Segmentation

Recently, CNN-based methods lead tremendous progress in segmenting abdominal organs (e.g., kidney, liver, and pancreas) and anomaly tumors in CT scans. Although 3D CNN-based methods can significantly improve accuracy by using 3D volume as input, they need more computational cost and may not satisfy the efficiency requirement for many practical applications. In this study, we mainly aim at improving the 2D segmentation by leveraging the consistency- and- discrepancy- context information from adjacent slices. Specifically, the consistency context mainly considers that the prediction variance of two adjacent slices needs to follow the variance in the ground truth. The discrepancy-context assumes the label difference of adjacent slices usually occurs in the edge area of organs. To fully utilize the above context information, we further devise a two-stage 2.5D segmentation framework based on the U-Net that takes three adjacent slices as input. In the first stage, we encourage the predictions of the three slices following the consistency context. In the second stage, we refine the segmentation result by adopting the prediction discrepancy area of adjacent slices as an extra input. Experimental results on several challenging datasets demonstrate the effectiveness of our proposed methods. Moreover, the adjacent-slice context information considered in this study can be effortlessly incorporated into other segmentation frameworks without extra testing overhead.
MD-CSDNetwork: Multi-Domain Cross Stitched Network for Deepfake Detection

The rapid progress in the ease of creating and spreading ultra-realistic media over social platforms calls for an urgent need to develop a generalizable deepfake detection technique. It has been observed that current deepfake generation methods leave discriminative artifacts in the frequency spectrum of fake images and videos. Inspired by this observation, in this paper, we present a novel approach, termed as MD-CSDNetwork, for combining the features in the spatial and frequency domains to extract a shared discriminative representation for classifying deepfakes. MD-CSDNetwork is a novel cross-stitched network with two parallel branches carrying spatial and frequency information, respectively. We hypothesize that these multi-domain input data streams can be considered as related supervisory signals and can ensure better performance and generalization. Further, the concept of cross-stitch connections is utilized where they are inserted between the two branches to learn an optimal combination of domain-specific and shared representations from other domains automatically. Extensive experiments are conducted on the popular benchmark datasets. We report improvements over all the manipulation types in the FaceForensics++ dataset and comparable results with state-of-the-art methods for cross-database evaluation on the Celeb-DF dataset and the Deepfake Detection Dataset.
RecSys 2021 Tutorial on Conversational Recommendation: Formulation, Methods, and Evaluation

Recommender systems have demonstrated great success in information seeking. However, traditional recommender systems work in a static way, estimating user preferences on items from past interaction history. This prevents recommender systems from capturing dynamic and fine-grained preferences of users. Conversational recommender systems bring a revolution to existing recommender systems. They are able to communicate with users through natural language, which enables them to explicitly elicit user preferences by asking whether a user likes an attribute or item or not. Based on information shared through users' responses, a recommender system can produce more accurate and personalized recommendations.
Automatic features generation and selection from external sources: A DBpedia use case

Feature engineering is one of the major challenges of machine learning. While multiple automation solutions have been proposed in recent years, the vast majority focuses on extracting features from the analyzed dataset itself and not from other (external) sources. In this study we present FGSES, a general framework for automatic feature engineering and its application to DBpedia. Our framework automatically matches the entities in the analyzed dataset to those of the external data source, and then proceeds to generate a large and diverse set of candidate features, both from structured and unstructured content. To efficiently process the large number of generated features, FGSES uses a meta learning-based ranking approach. Our evaluation, conducted on 18 tabular datasets with diverse characteristics, shows that FGSES achieves an average error reduction of 16.5%, significantly outperforming the evaluated baselines.
Multimedia Filtering Analysis of Massive Information Combined with Data Mining Algorithms

With the advent of the big data era, information presentation has exploded. For example, rich methods such as audio and video have integrated more information, but with it, a lot of bad information has been brought. In view of this situation, this paper relies on data mining algorithms, builds a multimedia filtering system model for massive information, and integrates content recognition, packet filtering, and other technologies to match the two to ensure the integrity and real time of filtering. Practice results prove that the method is effective.
Hierarchical Multilabel Ship Classification in Remote Sensing Images Using Label Relation Graphs

Hierarchical multilabel classification (HMC) assigns multiple labels to each instance with the labels organized under hierarchical relations. In ship classification in remote sensing images, depending on the expert knowledge and image quality, the same type of ships in different remote sensing images may be annotated with different class labels from coarse to fine levels such as merchant ship (MS) or container ship (CTS). In this article, we propose a novel deep network with two output channels and their associated loss functions to learn an HMC classifier using samples labeled at different levels in the hierarchy. In the proposed network, a hierarchy and exclusion (HEX) graph is introduced to model the label hierarchy, which satisfies hierarchical constraints by encoding semantic relations between any two labels. The output nodes of the first channel are organized according to the HEX graph, and its corresponding probabilistic classification loss is built to reflect the hierarchical structure of the HEX graph. On the other hand, the output nodes of the second channel only represent the finest grained (last level in the hierarchy) classes, and its multiclass cross-entropy loss is designed to enhance the discriminative power of the HMC classifier on the last level labels, which is also compatible with constraints in the HEX graph. The combination of these two losses from two output channels can effectively transfer the hierarchical information of ship taxonomy during network training. Experimental results on two commonly used ship datasets demonstrate that the proposed method outperforms the state-of-the-art HMC approaches, and is especially advantageous when trained with fewer fine-grained samples.
Blockchain Technology for IoT based Educational Framework and Credentials

The Internet of Things (IoT) and blockchain are two of the most impactful and inventive technologies of the previous decade. Due to its unique qualities, such as security, data integrity, decentralization and reliability, blockchain is attracting practitioners and researchers. IoT, on the other hand, refers to a network of connected things that use wearable devices and software to communicate and exchange data with other devices through the internet. These technologies are growing fast in many computer science fields; therefore, it is essential to improve the current state of knowledge of blockchain practices into IoT enabled education sector to utilize the thoroughgoing features to benefit the students, teachers, employers, organizations and lifelong learners.This research article investigates the benefits of the blockchain, to keep the information of stakeholders cryptically secure on the internet. Especially in the field of education where IoT enables an efficient interaction system among developers, facilitators, students, teachers, recruiters and employers while preserving valuable data on the internet. Several challenges of adopting blockchain for IoT-enabled education systems are addressed so that the practitioners can acquire innovative technologies in the field of education more quickly in near future. The most significant accomplishment of this research is the establishment of a framework for the educational system to communicate through IoT devices and utilize blockchain technology for producing certificates and securing other credentials as well.
A Systematic Literature Review on Transfer Learning for 3D-CNNs

The dependence of convolutional neural networks on large-scale datasets for training is no secret. This is even more problematic when using 3D-CNNs since sufficient 3D datasets for training are scarce and expensive. One possible solution is transfer learning. In this comparison, the state-of-the-art techniques for 3D-CNNs are analyzed and compared. Therefore, a literature search in the databases IEEEXplore DL, ScienceDirect, SpringerLink, and ACM is conducted. The results are compared using the criteria field of application, datasets, 3D-CNN architecture, transfer learning technique, hyperparameters, and final performance. This comparison provides a basis for future work to promote understanding and usage of transfer learning for 3D-CNNs.