Reviewerly/exHarmony huggingface dataset:
NTP-Miner: Nonoverlapping Three-Way Sequential Pattern Mining

Nonoverlapping sequential pattern mining is an important type of sequential pattern mining (SPM) with gap constraints, which not only can reveal interesting patterns to users but also can effectively reduce the search space using the Apriori (anti-monotonicity) property. However, the existing algorithms do not focus on attributes of interest to users, meaning that existing methods may discover many frequent patterns that are redundant. To solve this problem, this article proposes a task called nonoverlapping three-way sequential pattern (NTP) mining, where attributes are categorized according to three levels of interest: strong, medium, and weak interest. NTP mining can effectively avoid mining redundant patterns since the NTPs are composed of strong and medium interest items. Moreover, NTPs can avoid serious deviations (the occurrence is significantly different from its pattern) since gap constraints cannot match with strong interest patterns. To mine NTPs, an effective algorithm is put forward, called NTP-Miner, which applies two main steps: support (frequency occurrence) calculation and candidate pattern generation. To calculate the support of an NTP, depth-first and backtracking strategies are adopted, which do not require creating a whole Nettree structure, meaning that many redundant nodes and parent–child relationships do not need to be created. Hence, time and space efficiency is improved. To generate candidate patterns while reducing their number, NTP-Miner employs a pattern join strategy and only mines patterns of strong and medium interest. Experimental results on stock market and protein datasets show that NTP-Miner not only is more efficient than other competitive approaches but can also help users find more valuable patterns. More importantly, NTP mining has achieved better performance than other competitive methods in clustering tasks. Algorithms and data are available at: https://github.com/wuc567/Pattern-Mining/tree/master/NTP-Miner .
A new hybrid prediction model of cumulative COVID-19 confirmed data

Establishing an accurate and efficient prediction model is of great significance for governments and other social organizations to formulate prevention and control policies and curb the explosive spread of the pandemic. To improve prediction accuracy of cumulative COVID-19 confirmed data, a new hybrid prediction model based on gradient-based optimizer variational mode decomposition (GVMD), extreme learning machine (ELM), and autoregressive integrated moving average (ARIMA), named GVMD-ELM-ARIMA, is proposed. To solve the problem of selecting the k value and the penalty factor α in variational mode decomposition (VMD), this paper proposes gradient-based optimizer variational mode decomposition (GVMD), which realizes the self-adaptive determination of k value and α value. Firstly, GVMD decomposes the cumulative COVID-19 confirmed data into some intrinsic mode functions (IMFs) and a residual component (IMFr). Secondly, IMFs are predicted by ELM. Then, IMFr is predicted by ARIMA. Finally, the final prediction results are obtained by reconstructing the prediction result of IMFs and IMFr. The cumulative COVID-19 confirmed data of the United States, India and Russia is used to verify its effectiveness. Taking the United States as an example, compared with the average MAPE, RMSE and MAE of the single model, the average MAPE of the hybrid model is reduced by 47.27%, the average RMSE is reduced by 44.50%, and the average MAE is reduced by 55.34%. Compared with GVMD-ELM-ELM, GVMD-ELM-ARIMA proposed in this paper reduces the MAPE by 60%, the RMSE by 56.85%, and the MAE by 61.61%. The experimental results show that GVMD-ELM-ARIMA has best prediction accuracy, and it provides a new method for predicting the cumulative COVID-19 confirmed data.
An Overview of Blockchain Online Social Media from the Technical Point of View

Social media is becoming one of the dominant ways to communicate. Before social media, people were extremely limited in their means to interact with others, and they were limited largely to the people that they knew in person. However, this impact on people in real life has damaged privacy. Alternative solutions have been proposed in order to overcome current social media issues. In this direction, blockchain is one of the most promising, and several blockchain-based social media have been proposed. In this paper, we analyze blockchain online social media from the technical point of view in order to understand the current trend of social DApps and to describe which characteristics are important in a blockchain-based social media scenario. We analyze real data by exploiting one of the most well-known DApps sites, and we compare current technologies in order to highlight which ones can be better applied to a real social scenario, such as Facebook.
Detecting out-of-distribution samples via variational auto-encoder with reliable uncertainty estimation

Variational autoencoders (VAEs) are influential generative models with rich representation capabilities from the deep neural network architecture and Bayesian method. However, VAE models have a weakness that assign a higher likelihood to out-of-distribution (OOD) inputs than in-distribution (ID) inputs. To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs. In this study, we propose an improved noise contrastive prior (INCP) to be able to integrate into the encoder of VAEs, called INCPVAE. INCP is scalable, trainable and compatible with VAEs, and it also adopts the merits from the INCP for uncertainty estimation. Experiments on various datasets demonstrate that compared to the standard VAEs, our model is superior in uncertainty estimation for the OOD data and is robust in anomaly detection tasks. The INCPVAE model obtains reliable uncertainty estimation for OOD inputs and solves the OOD problem in VAE models.
Siren

With the popularity of machine learning on many applications, data privacy has become a severe issue when machine learning is applied in the real world. Federated learning (FL), an emerging paradigm in machine learning, aims to train a centralized model while distributing training data among a large number of clients in order to avoid data privacy leaking, which has attracted great attention recently. However, the distributed training scheme in FL is susceptible to different kinds of attacks. Existing defense systems mainly utilize model weight analysis to identify malicious clients with many limitations. For example, some defense systems must know the exact number of malicious clients beforehand, which can be easily bypassed by well-designed attack methods and become impractical for real-world scenarios.
Under the Hood of the Ethereum Gossip Protocol

Blockchain protocols’ primary security goal is consensus: one version of the global ledger that everyone in the network agrees on. Their proofs of security depend on assumptions on how well their peer-to-peer (P2P) overlay networks operate. Yet, surprisingly, little is understood about what factors influence the P2P network properties. In this work, we extensively study the Ethereum P2P network’s connectivity and its block propagation mechanism. We gather data on the Ethereum network by running the official Ethereum client, geth, modified to run as a “super peer” with many neighbors. We run this client in North America for over seven months, as well as shorter runs with multiple vantages around the world. Our results expose an incredible amount of churn, and a surprisingly small number of peers who are actually useful (that is, who propagate new blocks). We also find that a node’s location has a significant impact on when it hears about blocks, and that the precise behavior of this has changed over time (e.g., nodes in the US have become less likely to hear about new blocks first). Finally, we find prune blocks propagate faster than uncles.
Curvature Generation in Curved Spaces for Few-Shot Learning

Few-shot learning describes the challenging problem of recognizing samples from unseen classes given very few labeled examples. In many cases, few-shot learning is cast as learning an embedding space that assigns test samples to their corresponding class prototypes. Previous methods assume that data of all few-shot learning tasks comply with a fixed geometrical structure, mostly a Euclidean structure. Questioning this assumption that is clearly difficult to hold in real-world scenarios and incurs distortions to data, we propose to learn a task-aware curved embedding space by making use of the hyperbolic geometry. As a result, task-specific embedding spaces where suitable curvatures are generated to match the characteristics of data are constructed, leading to more generic embedding spaces. We then leverage on intra-class and inter-class context information in the embedding space to generate class prototypes for discriminative classification. We conduct a comprehensive set of experiments on inductive and transductive few-shot learning, demonstrating the benefits of our proposed method over existing embedding methods.
Perceptions of University Students towards Digital Transformation during the Pandemic

The aim of this study is to determine the perceptions of university students toward teaching–learning processes during the COVID-19 pandemic. This research gathers inquiries made worldwide on the perceptions of students in higher education during a state of alarm. The proposed objectives were (a) to analyse the perception of students toward teaching–learning processes in university; (b) to determine the assessment given by students about the changes that took place in university teaching as a result of COVID-19; and (c) to explore resources (hardware and software), professional collaboration, digital pedagogy and student empowerment (motivation) regarding digital education and recent changes in university teaching due to the pandemic. This study used a non-experimental, descriptive design based on opinion polls or surveys. The results show a positive correlation between digital pedagogy, student motivation and digital environments. As a conclusion, we encourage the scientific community to continue delving into the motivation, collaboration and reflective exchange of experiences, self-learning and promotion of initiatives that foster the development of competencies in future teachers. It is also important to continue the research on integrated designs in training processes in university, tutoring and continuous evaluation, as they are key for digital transformation in universities.
EdgeShare: A blockchain-based edge data-sharing framework for Industrial Internet of Things

Industrial Internet of Things (IIoT) is facing significant scalability and security challenges in sharing massive device data across heterogeneous network domains. Traditional cloud-based sharing solutions for IIoT applications that are getting closer to devices are uneconomical and inefficient because of their centralized architecture. We propose a blockchain-based data-sharing framework called EdgeShare for edge data-sharing services among heterogeneous network domains by introducing a two-layer overlay network topology and an edge computing paradigm that can dramatically alleviate system pressure and reduce communication delays to improve data-sharing efficiency. Under such framework, a novel smart contract is designed to construct access control strategies with dissimilar granularities to achieve different levels of data access control in an undeniable and automated manner. All IIoT data-sharing activities are recorded in a blockchain for secured transaction logging and auditing. We implement an EdgeShare prototype and conduct extensive experiments and analyses to evaluate the feasibility of our proposals. In contrast with centralized sharing methods, results demonstrate that the proposed framework can effectively improve the reliability, efficiency, and security of massive data sharing while introducing reasonable and acceptable overheads.
A transformer generative adversarial network for multi‐track music generation

This study proposes a new generation network based on transformers and guided by the music theory to produce high-quality music work. In this study, the decoding block of the transformer is used to learn the internal information of single-track music, and cross-track transformers are used to learn the information amongst the tracks of different musical instruments. A reward network based on the music theory is proposed, which optimizes the global and local loss objective functions while training and discriminating the network so that the reward network can provide a reliable adjustment method for the generation of the network. The method of combining the reward network and cross entropy loss is used to guide the training of the generator and produce high-quality music work. Compared with other multi-track music generation models, the experimental results verify the validity of the model.
Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration

As Federated Learning (FL) has been widely used for collaborative training, a considerable computational straggler issue emerged: when FL deploys identical neural network models to heterogeneous devices, the ones with weak computational capacities, referred to as stragglers, may significantly delay the synchronous parameter aggregation. Although discarding stragglers from the collaboration can relieve this issue to a certain extent, stragglers may keep unique and critical information learned from the non-identical dataset, and directly discarding will harm the overall collaboration performance. Therefore, in this paper, we propose Helios – a heterogeneity-aware FL framework to tackle the straggler issue. Helios identifies individual devices’ heterogeneous training capability, and therefore the expected neural network model training volumes regarding the collaborative training pace. For straggling devices, a “softtraining” method is proposed to dynamically compress the original identical training model into the expected volume through a rotated neuron training approach. With extensive algorithm analysis and optimization schemes, stragglers can be accelerated while retaining the convergence for local training as well as federated collaboration. Experiments show that Helios can provide up to $2.5\times$ training acceleration and maximum 4.64% convergence accuracy improvement in various collaboration settings.
Bi-Directional Co-Attention Network for Image Captioning

Image Captioning, which automatically describes an image with natural language, is regarded as a fundamental challenge in computer vision. In recent years, significant advance has been made in image captioning through improving attention mechanism. However, most existing methods construct attention mechanisms based on singular visual features, such as patch features or object features, which limits the accuracy of generated captions. In this article, we propose a Bidirectional Co-Attention Network (BCAN) that combines multiple visual features to provide information from different aspects. Different features are associated with predicting different words, and there are a priori relations between these multiple visual features. Based on this, we further propose a bottom-up and top-down bi-directional co-attention mechanism to extract discriminative attention information. Furthermore, most existing methods do not exploit an effective multimodal integration strategy, generally using addition or concatenation to combine features. To solve this problem, we adopt the Multivariate Residual Module (MRM) to integrate multimodal attention features. Meanwhile, we further propose a Vertical MRM to integrate features of the same category, and a Horizontal MRM to combine features of the different categories, which can balance the contribution of the bottom-up co-attention and the top-down co-attention. In contrast to the existing methods, the BCAN is able to obtain complementary information from multiple visual features via the bi-directional co-attention strategy, and integrate multimodal information via the improved multivariate residual strategy. We conduct a series of experiments on two benchmark datasets (MSCOCO and Flickr30k), and the results indicate that the proposed BCAN achieves the superior performance.
Adversarial Domain Adaptation With Prototype-Based Normalized Output Conditioner

Domain adversarial training has become a prevailing and effective paradigm for unsupervised domain adaptation (UDA). To successfully align the multi-modal data structures across domains, the following works exploit discriminative information in the adversarial training process, e.g., using multiple class-wise discriminators and involving conditional information in the input or output of the domain discriminator. However, these methods either require non-trivial model designs or are inefficient for UDA tasks. In this work, we attempt to address this dilemma by devising simple and compact conditional domain adversarial training methods. We first revisit the simple concatenation conditioning strategy where features are concatenated with output predictions as the input of the discriminator. We find the concatenation strategy suffers from the weak conditioning strength. We further demonstrate that enlarging the norm of concatenated predictions can effectively energize the conditional domain alignment. Thus we improve concatenation conditioning by normalizing the output predictions to have the same norm of features, and term the derived method as Normalized OutpUt coNditioner (NOUN). However, conditioning on raw output predictions for domain alignment, NOUN suffers from inaccurate predictions of the target domain. To this end, we propose to condition the cross-domain feature alignment in the prototype space rather than in the output space. Combining the novel prototype-based conditioning with NOUN, we term the enhanced method as PROtotype-based Normalized OutpUt coNditioner (PRONOUN). Experiments on both object recognition and semantic segmentation show that NOUN can effectively align the multi-modal structures across domains and even outperform state-of-the-art domain adversarial training methods. Together with prototype-based conditioning, PRONOUN further improves the adaptation performance over NOUN on multiple object recognition benchmarks for UDA. Code is available at https://github.com/tim-learn/NOUN.
Cybersecurity Awareness Enhancement: A Study of the Effects of Age and Gender of Thai Employees Associated with Phishing Attacks

Cybersecurity is crucial at present because cyber threats (e.g., phishing) have become a very common occurrence in everyday life. A literature review showed that there are no studies based on cybersecurity awareness which involved a large number of Thai users. Thus, this research focused on the cybersecurity awareness of approximately 20,000 nationwide employees in a large financial institution in Thailand. The study consisted of three phases, a first phishing attack, knowledge transfer through a mixed-approach and a second phishing attack with different content. After data validation and analysis of the results, it was found that the level of cybersecurity awareness of employees improved significantly. The number of employees who opened the phishing email decreased by 71.5%. Therefore, this approach could be applied to cybersecurity enhancement in other organizations and other sectors/industries. Also, it was found that gender played a significant role in cybersecurity awareness within the Thai cybersecurity ecosystem since Thai female employees were found to have a higher level of cybersecurity awareness than male employees. Furthermore, it was found that the different generations of Thai employees (Generations Y and X and Baby Boomers) did not affect cybersecurity awareness.
Ephemeral Astroturfing Attacks: The Case of Fake Twitter Trends

We uncover a previously unknown, ongoing as-troturfing attack on the popularity mechanisms of social media platforms: ephemeral astroturfing attacks. In this attack, a chosen keyword or topic is artificially promoted by coordinated and inauthentic activity to appear popular, and, crucially, this activity is removed as part of the attack. We observe such attacks on Twitter trends and find that these attacks are not only successful but also pervasive. We detected over 19,000 unique fake trends promoted by over 108,000 accounts, including not only fake but also compromised accounts, many of which remained active and continued participating in the attacks. Trends astroturfed by these attacks account for at least 20% of the top 10 global trends. Ephemeral astroturfing threatens the integrity of popularity mechanisms on social media platforms and by extension the integrity of the platforms.
Towards Domain-Independent and Real-Time Gesture Recognition Using Mmwave Signal

Human gesture recognition using millimeter-wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains, and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive signal variations corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations. Furthermore, a spatial-temporal gesture segmentation algorithm is employed for real-time recognition. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18%, and 98.76% for new users, environments, and locations, respectively. We also evaluate DI-Gesture in challenging scenarios like real-time recogntion and sensing at extreme angles, all of which demonstrates the superior robustness and effectiveness of our system.
A survey on near-human conversational agents

Conversational AI intends for machine-human interactions to appear and feel more natural and inclined to communicate in a near-human context. Chatbots, also known as conversational agents, are typically divided into two use-cases: task-oriented bots and social friend-bots. Task-oriented bots are often used to do activities such as answering questions or solving basic queries. Furthermore, social-friend-bots are designed to communicate like humans, where the user can speak freely and the bot answers organically while maintaining the conversation’s ambience. This paper analyses recent works in the conversational AI domain examining the exclusive methodologies, existing frameworks or tools, evaluation metrics, and available datasets for building robust conversational agents. Finally, a mind-map encompassing all the stated elements and qualities of chatbots is created.