Reviewerly/exHarmony huggingface dataset:
Achieving Privacy-Preserving Multitask Allocation for Mobile Crowdsensing

In the mobile crowdsensing (MCS) with large-scale data collection and sharing environments, since a growing number of applications need to exploit multisource sensing information, it is almost indispensable to develop a generic mechanism supporting efficient and accurate multiple tasks allocation. Meanwhile, achieving the maximum service benefit, the cloud server allocates the multitask based on the user attribute preferences, but it will lead to the privacy leakage of sensing users (SUs). Motivated by the aforementioned challenges, we propose a privacy-preserving multitask allocation (PMTA) scheme for MCS in this article. Specifically, we exploit <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering and matrix multiplication to realize a secure and efficient grouping mechanism, which achieves the selection of high-quality and accurate target users set with privacy preserving. Based on the short group signature algorithm and 0–1 encoding technique, we construct a privacy-preserving matching mechanism to guarantee the anonymous authentication and achieve the matching for task requirements and user reputation levels in a privacy-preserving way. Finally, we give a security analysis, and we evaluate the computational costs and communication overhead, and the experimental result shows the efficiency of our proposed PMTA scheme.
Twitter Sentiment Analysis and Expert Ratings of Initial Coin Offering Fundraising: Evidence from Australia and Singapore Markets

Sentiment analysis of Twitter data is quite valuable for determining the market opinion. Twitter sentiment analysis is more challenging than generic sentiment analysis owing to slang and misspellings. The techniques utilized for evaluating the sentiment of tweets that have the greatest importance for the success of an Initial Coin Offering (ICO) are machine learning approaches. In this study, we examined market sentiment and used Expert Ratings to predict the success of ICOs in the Australian and Singapore markets. Based on 68,281 tweets from 57 ICOs across four industries: business services, cryptocurrency, entertainment, and platform. Several classification methods were investigated, including Support Vector Machines (SVMs), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB). The outcomes indicated that sentiment analysis of tweets and expert ratings may be used to forecast the success of an initial coin offering. The results indicate that the suggested model is capable of accurately assessing the tweets of the ICO Successful with a maximum accuracy of about 94.7 % when implementing the Support Vector Machines (SVMs) classifier.
RGTransformer: Region-Graph Transformer for Image Representation and Few-Shot Classification

The goal of few-shot image classification is to learn a classifier that can be well generalized to the unseen classes with a few available labeled samples. One major challenge for few-shot learning is how to conduct effective image representation for support and query images. Recently, local region-based image representation and metric learning approaches have been demonstrated effectively for few-shot classification problem. However, existing approaches generally conduct representations of image regions individually which thus lack of considering the rich spatial/structural relationships among image regions. In this paper, we propose to bridge the individual regions and exploit the structural contexts among regions via a novel Region-Graph Transformer (RGTransformer). In RGTransformer, each region aggregates the information from its neighboring regions and thus can obtain context-aware feature representations for regions. Using the proposed RGTransformer, we propose an effective metric learning model for few-shot image classification. We evaluate the proposed method on four benchmark datasets and experimental results demonstrate the effectiveness and advantages of the proposed RGTransformer.
Multimodal Sentiment Analysis using Audio and Text for Crime Detection

Thanks to the advancement of communication technologies and the widespread use of social media networks, individuals generate daily a significant amount of data that contains valuable emotional information. During the last few decades, most of the research in sentiment analysis has concentrated on textual sentiment analysis, which has been accomplished through text mining techniques. Audio sentiment analysis, on the other hand, is still in its infancy and started to attract the scientific community. In this paper, we use the XD-Violence dataset to develop a multimodal learning model that predicts crimes by incorporating both audio and text modalities into the same model. As an initial step, we benchmark the dataset on audio using CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) before moving on to text using BERT (Bidirectional Encoder Representations from Transformers). Finally, we combine CNN and BERT to get the best results with an accuracy of 85,63 %, a loss of 30,47%, and an F1-score of 85,16%.
Detection of anomalous vehicle trajectories using federated learning

Nowadays mobile positioning devices, such as global navigation satellite systems (GNSS) but also external sensor technology like cameras allow an efficient online collection of trajectories, which reflect the behavior of moving objects, such as cars. The data can be used for various applications, e.g., traffic planning or updating maps, which need many trajectories to extract and infer the desired information, especially when machine or deep learning approaches are used. Often, the amount and diversity of necessary data exceeds what can be collected by individuals or even single companies. Currently, data owners, e.g., vehicle producers or service operators, are reluctant to share data due to data privacy rules or because of the risk of sharing information with competitors, which could jeopardize the data owner's competitive advantage. A promising approach to exploit data from several data owners, but still not directly accessing the data, is the concept of federated learning, that allows collaborative learning without exchanging raw data, but only model parameters. In this paper, we address the problem of anomaly detection in vehicle trajectories, and investigate the benefits of using federated learning. To this end, we apply several state-of-the-art learning algorithms like one-class support vector machine (OCSVM) and isolation forest, thus solving a one-class classification problem. Based on these learning mechanisms, we successfully proposed and verified a federated architecture for the collaborative identification of anomalous trajectories at several intersections. We demonstrate that the federated approach is beneficial not only to improve the overall anomaly detection accuracy, but also for each individual data owner. The experiments show that federated learning allows to increase the anomaly detection accuracy from in average AUC-ROC scores of 97% by individual intersections up to 99% using cooperation.
Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation

Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre. Previous works rely on conventional aggregation modules (e.g., dilated convolution, convolutional LSTM), which only make use of the local context. In this paper, we propose a novel framework STswinCL that explores the complementary intra- and inter-video relations to boost segmentation performance, by progressively capturing the global context. We firstly develop a hierarchy Transformer to capture intra-video relation that includes richer spatial and temporal cues from neighbor pixels and previous frames. A joint space-time window shift scheme is proposed to efficiently aggregate these two cues into each pixel embedding. Then, we explore inter-video relation via pixel-to-pixel contrastive learning, which well structures the global embedding space. A multi-source contrast training objective is developed to group the pixel embeddings across videos with the ground-truth guidance, which is crucial for learning the global property of the whole data. We extensively validate our approach on two public surgical video benchmarks, including EndoVis18 Challenge and CaDIS dataset. Experimental results demonstrate the promising performance of our method, which consistently exceeds previous state-of-the-art approaches. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YuemingJin/STswinCL</uri> .
Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification

Cancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases: data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy.
(Dis)Appearables: A Concept and Method for Actuated Tangible UIs to Appear and Disappear based on Stages

(Dis)Appearables is an approach for actuated Tangible User Interfaces (TUIs) to appear and disappear. This technique is supported by Stages: physical platforms inspired by theatrical stages. Self-propelled TUI’s autonomously move between front and back stage allowing them to dynamically appear and disappear from users’ attention. This platform opens up a novel interaction design space for expressive displays with dynamic physical affordances.
An experimental study of the impact of virtual reality training on manufacturing operators on industrial robotic tasks

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and "pick and place" -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users' satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment.
MiNuGAN: Dual Segmentation of Mitoses and Nuclei Using Conditional GANs on Multi-center Breast H&amp;E Images

Breast cancer is the second most commonly diagnosed type of cancer among women as of 2021. Grading of histopathological images is used to guide breast cancer treatment decisions and a critical component of this is a mitotic score, which is related to tumor aggressiveness. Manual mitosis counting is an extremely tedious manual task, but automated approaches can be used to overcome inefficiency and subjectivity. In this paper, we propose an automatic mitosis and nuclear segmentation method for a diverse set of H&E breast cancer pathology images. The method is based on a conditional generative adversarial network to segment both mitoses and nuclei at the same time. Architecture optimizations are investigated, including hyper parameters and the addition of a focal loss. The accuracy of the proposed method is investigated using images from multiple centers and scanners, including TUPAC16, ICPR14 and ICPR12 datasets. In TUPAC16, we use 618 carefully annotated images of size 256×256 scanned at 40×. TUPAC16 is used to train the model, and segmentation performance is measured on the test set for both nuclei and mitoses. Results on 200 held-out testing images from the TUPAC16 dataset were mean DSC = 0.784 and 0.721 for nuclear and mitosis, respectively. On 202 ICPR12 images, mitosis segmentation accuracy had a mean DSC = 0.782, indicating the model generalizes well to unseen datasets. For datasets that had mitosis centroid annotations, which included 200 TUPAC16, 202 ICPR12 and 524 ICPR14, a mean F1-score of 0.854 was found indicating high mitosis detection accuracy.
Two-Round n-out-of-n and Multi-Signatures and Trapdoor Commitment from Lattices

Although they have been studied for a long time, distributed signature protocols have garnered renewed interest in recent years in view of novel applications to topics like blockchains. Most recent works have focused on distributed versions of ECDSA or variants of Schnorr signatures; however, and in particular, little attention has been given to constructions based on post-quantum secure assumptions like the hardness of lattice problems. A few lattice-based threshold signature and multi-signature schemes have been proposed in the literature, but they either rely on hash-and-sign lattice signatures (which tend to be comparatively inefficient), use expensive generic transformations, or only come with incomplete security proofs. In this paper, we construct several lattice-based distributed signing protocols with low round complexity following the Fiat–Shamir with Aborts (FSwA) paradigm of Lyubashevsky (Asiacrypt 2009). Our protocols can be seen as distributed variants of the fast Dilithium-G signature scheme and the full security proof can be made assuming the hardness of module SIS and LWE problems. A key step to achieving security (unexplained in some earlier papers) is to prevent the leakage that can occur when parties abort after their first message—which can inevitably happen in the Fiat–Shamir with Aborts setting. We manage to do so using homomorphic commitments. Exploiting the similarities between FSwA and Schnorr-style signatures, our approach makes the most of observations from recent advancements in the discrete log setting, such as Drijvers et al.’s seminal work on two-round multi-signatures (S&P 2019). In particular, we observe that the use of commitment not only resolves the subtle issue with aborts, but also makes it possible to realize secure two-round n-out-of-n distributed signing and multi-signature in the plain public key model, by equipping the commitment with a trapdoor feature. The construction of suitable trapdoor commitment from lattices is a side contribution of this paper.
Supervised Machine Learning for Training a Neural Network as 5:2 Compressor

Machine Learning has achieved substantial development in numerous applications like image processing, pattern recognition, approximate computing etc. This paper interlinks supervised machine learning algorithm and VLSI architectures to train a neural network as exact and approximate 5:2 compressors. Probabilistic pruning type of approximation technique has been employed on the exact 5:2 compressor. This approximation technique on compressors reduces the power consumption with variation in the outputs without affecting the error limit. The simulation of 5:2 compressors and training of neural network using machine learning algorithm has been done using Spectre simulator of Cadence Design Systems at 45nm CMOS technology node and Keras library with TensorFlow background respectively.
Partial Recovery of Erdðs-Rényi Graph Alignment via k-Core Alignment

We determine information theoretic conditions under which it is possible to partially recover the alignment used to generate a pair of sparse, correlated Erdos-Renyi graphs. To prove our achievability result, we introduce the k-core alignment estimator. This estimator searches for an alignment in which the intersection of the correlated graphs using this alignment has a minimum degree of k. We prove a matching converse bound. As the number of vertices grows, recovery of the alignment for a fraction of the vertices tending to one is possible when the average degree of the intersection of the graph pair tends to infinity. It was previously known that exact alignment is possible when this average degree grows faster than the logarithm of the number of vertices.
Birds of a feather flock together: matched personality effects of product recommendation chatbots and users

Purpose AI-driven product recommendation chatbots have markedly reduced operating costs and increased sales for marketers. However, previous literature has paid little attention to the effects of the personality of e-commerce chatbots. This study aimed to examine the ways that the interplay between the chatbot's and the user's personality can increase favorable product attitudes and future intentions to use the chatbot. Based on prior literature, we specifically focused on the degree of extroversion of both chatbot and user. Design/methodology/approach A total of 291 individuals participated in this study. Two different versions of chatbot were created for this study (i.e. extroversion: high vs. low). Participants self-reported their degree of extroversion. The PROCESS macro Model 1 and Model 7 with the Johnson–Neyman technique were employed to test the hypotheses. Findings The results showed that the high extroversion chatbot elicited greater user satisfactions and perceptions of chatbot friendliness among users with a high level of extroversion. On the contrary, the low extroversion chatbot resulted in greater user satisfactions and perceived chatbot friendliness among users with a low level of extroversion. This study further found that user satisfactions and perceived chatbot friendliness mediated the effects of the chatbot on greater intentions to use the chatbot and more favorable product attitudes. Originality/value By showing the effects of matching the personality of the chatbot and user, this study revealed that similarity-attraction effects also apply to human–chatbot interaction in e-commerce. Future studies would benefit by investigating the similarity-attraction effects in different characteristics, such as appearance, opinion and preference. This study also provides useful information for e-commerce marketers and chatbot UX/UI designers.
Multi-head attention-based two-stream EfficientNet for action recognition

Abstract Recent years have witnessed the popularity of using two-stream convolutional neural networks for action recognition. However, existing two-stream convolutional neural network-based action recognition approaches are incapable of distinguishing some roughly similar actions in videos such as sneezing and yawning. To solve this problem, we propose a Multi-head Attention-based Two-stream EfficientNet (MAT-EffNet) for action recognition, which can take advantage of the efficient feature extraction of EfficientNet. The proposed network consists of two streams (i.e., a spatial stream and a temporal stream), which first extract the spatial and temporal features from consecutive frames by using EfficientNet. Then, a multi-head attention mechanism is utilized on the two streams to capture the key action information from the extracted features. The final prediction is obtained via a late average fusion, which averages the softmax score of spatial and temporal streams. The proposed MAT-EffNet can focus on the key action information at different frames and compute the attention multiple times, in parallel, to distinguish similar actions. We test the proposed network on the UCF101, HMDB51 and Kinetics-400 datasets. Experimental results show that the MAT-EffNet outperforms other state-of-the-art approaches for action recognition.
Psychological Factors for the Formation of Collective Ecological Consciousness

This article presents the results of a theoretical analysis of the concept of collective ecological consciousness from the standpoint of its characteristics. It also displays fundamental differences between ecological and individual consciousness. Furthermore, the paper depicts the correlation between environmental culture, which arises in the process of socialization, and the power of environmental intents aimed at protecting the environment. During the integration into society, an individual learns a universal system of beliefs, values, customs, traditions, norms, and rules that are followed by dominant public. They also evolve relationships with the world and nature surrounding them. In particular, in a society with a high level of development of collective ecological consciousness, young people from an early age employ effective strategies for the conservation and restoration of natural resources. Thus, the authors draw attention to the crisis of morality and spirituality, which is the main reason for developing a selfish type of collective ecological consciousness. The authors give examples of environmental education concepts and training to lay the theoretical foundation for developing effective programs to improve environmental culture in the younger generation.