Reviewerly/exHarmony huggingface dataset:
Learning English in Early Childhood Education with Augmented Reality: Design, Production, and Evaluation of the “Wordtastic Kids” App

The pedagogical use of AR for language learning in early childhood education is focusing attention on the didactic potential that these resources present at such early ages. The aim of this study was to develop and assess the “Wordtastic Kids” Application (APP) in order to: (a) design, produce, and evaluate a new contextualized AR application for learning English in early childhood education; and (b) know the academic performance of early childhood education students towards learning English using an AR application. The methodology was based on the design of materials and the subsequent realization of the pre-test/post-test evaluation to determine the academic performance of the students. The results show a general increase in the scores obtained in all the pre-test and post-test categories that were addressed using the Wordtastic Kids Application, which proves to be a tool that improves the learning of English vocabulary in pre-school children. Therefore, it can be concluded that AR can be useful for complementing traditional vocabulary learning in children. However, it is important to note that the successful implementation of AR will depend on several factors, such as adequate teacher preparation, the quality of AR content, and the ability of children to interact with technology effectively.
DLMT: Outsourcing Deep Learning with Privacy Protection Based on Matrix Transformation

In recent years, deep learning has been applied in a wide variety of domains and gains outstanding success. In order to achieve high accuracy, a large amount of training data and high-performance hardware are necessary for deep learning. In real-world applications, many deep learning developers usually rent cloud GPU servers to train or deploy their models. Since training data may contain sensitive information, training models on cloud servers will cause severe privacy leakage problem. To solve this problem, we propose a privacy-preserving deep learning model based on matrix transformation. Specifically, we transform original data by adding or multiplying a random matrix. The obtained data is significantly different from the origin and it is hard to recover original data, so it can protect the privacy in original data. Experimental results demonstrate that the models trained with processed data can achieve high accuracy.
Optimizing The Access Control System for IOTA Tangle: A Game-Theoretic Perspective

With the development of Internet of Things (IoT) applications, there is an explosion in demands for data sharing between IoT nodes, which needs the IoT network to provide a data sharing platform to enhance data privacy and security. IOTA Tangle has been considered as a promising Distributed Ledger Technology to establish such a platform because of its affinity to IoT nodes. Additionally, Tangle proposes an access control system to prevent congestion of the platform by allocating a number of tokens to each node, and limiting data sharing rate of each node based on its hold tokens. However, this poses an inconspicuous but fatal problem that high-rate-demand nodes with few tokens cannot acquire an ideal rate. In this paper, we optimize the access control system for the Tangle-based platform by setting a token transferring mechanism, where a chaebol node can sell tokens to provide IoT nodes with ideal data sharing rate. Since the chaebol node needs more profits and IoT nodes need ideal data sharing rate, we formulate the token pricing and purchasing problem as a two-stage Stackelberg game. We analyze the equilibrium of the game in a uniform pricing scheme and design an access control algorithm TTM based on the equilibrium. Finally, we verify performance of the algorithm by experiments. The results show that the proposed algorithm outperforms the state-of-art in terms of providing high-rate-demand nodes with the ideal rate.
Spa-L Transformer: Sparse-self attention model of Long short-term memory positional encoding based on long text classification

The emergence of Transformer and its derivative models brings new opportunities to tasks of NLP (Natural Language Processing). Transformer is not only a separate model, but also the core of different text task systems. Therefore, Transformer has become an important component of many powerful models. However, Transformer is not without defects. Researchers are still puzzled by the huge amount of computation generated in the process of self-attention. Especially in long text data sets. We propose a new ProbSparse self-attention Transformer model based for text classification. In the following, we will call it SpaL Transformer. We query important attention factors through KL divergence and add Long Short Term Memory(LSTM) to positional encoding, and only focus on the main query. Then, we select the most important relevant attention based on the confidence score to focus the overall attention. At the same time, we propose LSTM positive encoding to obtain relative position information to optimize the model. In long text dataset IMDB, our model improves the accuracy of Transformer. And F1 score improved by 0.064.
BAT: Mining Binary-API Topic for Multi-service Application Development

With the growing popularity of API-driven multiservice application (mashup) development, the burgeoning web APIs have left developers drowning in the sea of web API selections. Matching developers with the most appropriate APIs is the key to improving user satisfaction and promoting more popular web applications. As a result, more and more researchers pay attention to web API recommender systems based on collaborative filtering. However, employing collaborative filtering to recommend APIs is challenging due to the severe sparsity of mashup and API interactions. To address this problem, we propose a probabilistic generative model, called the Binary-API Topic model (BAT), to parameterize mashups and APIs. Technically, BAT is equipped with a mechanism to extract binary-APIs and predict unknown pairwise interactions. To improve generality and capture more relevance from a limited number of interactions, we learn binary-API topics by directly modeling the generation of API co-occurrence patterns across the repository (all mashup collections from programmableweb.com). The main advantage of BAT is that it preserves API co-occurrence patterns in model learning and exploits the rich global relevance. Finally, through extensive experiments, we demonstrate that BAT can achieve the highest performance on the sparse real-world data set.
AKGIN: An API Knowledge Graph and Intent Network based Mashup-Oriented API Recommendation Method

With the continuous development of the Web API ecosystem, mashup-oriented API recommendation gets a lot of attention. Collaborative filtering, deep learning and their combination based methods are recently proposed for API recommendation. However, the recommendation results often perform poorly when data is sparse. Therefore, we propose an API knowledge graph (AKG) and intent network based mashup-oriented API recommendation method, whose main target is to recruit the semantic representation ability to improve the precision of API recommendation. Firstly, Latent Dirichlet Allocation (LDA) is used to extract API topics, which are important entities in the AKG. Then, the mashup intention is modeled based on the mashup-API interactions. The high-order relationship paths are recursively aggregated into the representation vector of Mashup and API. Finally, the inner product of their representations present the probability of the API recommendation for the mashup. The experimental results on real-world datasets show that our proposed method outperforms several baseline methods in mashup-oriented API recommendations.
TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction

Dialogue-based Relation Extraction (DRE) aims to predict the relation type of argument pairs that are mentioned in dialogue. The latest trigger-enhanced methods propose trigger prediction tasks to promote DRE. However, these methods are not able to fully leverage the trigger information and even bring noise to relation extraction. To solve these problems, we propose TLAG, which fully leverages the trigger and label-aware knowledge to guide the relation extraction. First, we design an adaptive trigger fusion module to fully leverage the trigger information. Then, we introduce label-aware knowledge to further promote our model’s performance. Experimental results on the DialogRE dataset show that our TLAG outperforms the baseline models, and detailed analyses demonstrate the effectiveness of our approach.
Community Detection with Graph Convolutional Auto-Encoder and Deep Clustering

Communities usually exhibit similar opinions, similar functions, or similar purposes, and recent years have witnessed the the resurgence of community detection in various fields. The node attribute network has gradually become the mainstream of the community network, however, most of existing community detection methods are limited by the high-dimensional node attribute and network topology, leading to the suboptimal performance. Inspired by recent deep learning-based community detection methods, we focus on building an unsupervised deep learning architecture to handle high-dimensional data in complex networks for community detection. To this end, we propose a novel Community Detection method based Deep Clustering and Graph Convolution auto-encoder Network (CD-DCGCN). Our CD-DCGCN designs an end-to-end framework consisting of dual auto-operations, one is a graph convolution auto-encoder, the other is community auto-detection. In addition, we realize the cooperative work of the dual auto-operations by constructing a joint optimized function. Our experimental results on nine attribute network benchmark datasets show that the proposed CD-DCGCN can obtain promising performance compared with several popular baseline methods.
The malicious resource consumption detection in permissioned blockchain based on traffic analysis

Blockchain is widely applied in many applications. However, it faces many security risks. Unlike external attacks, the evildoing of internal members need more attentions in permissioned blockchain. We introduce the malicious resource consumption attack which seriously degrade the performance of permissioned blockchain system. To detect malicious resource consumption attacks, we propose MRC-Dec which applies a LSTM-AE model to analyze traffic between blockchain nodes, to determine whether the attacks happen. First, we construct the traffic dataset for attack detection by implementing two types of malicious resource consumption, namely useless endorsement and storage pollution on Fabric. Next in order not to be affected by the blockchain scale which means the number of nodes in blockchain, the traffic features are extracted between the node sets instead of between nodes. After feature extraction, a sequence of traffic features in temporal order is obtained, which is input to the LSTM-AE model. The LSTM-AE model learns the characteristic of normal traffic, and a threshold is set to determine normal and attack. The evaluation results show that MRC-Dec can find the malicious resource consumption attacks with high performance and can well adopt to the blockchain scale.
BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.
Using Rough Sets to Improve the High-dimensional Data Anomaly Detection Method Based on Extended Isolation Forest

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods.
MIA-FedDL: A Membership Inference Attack against Federated Distillation Learning

Federated learning, which gathers the model parameters of numerous IoT devices without accessing user data, is used to train high-quality models and enhance the quantity and quality of available data. Federated distillation learning strategies are suggested as a means of addressing the difficulties associated with federated learning in Non-IID environment. However, recent research has demonstrated that federated learning falls short of providing complete privacy protection and is still open to malevolent attackers’ inference attacks. In this paper, we investigate a malicious attacker’s membership inference attack in a federated distillation learning context. We initially concentrate on federated distillation learning in the Non-IID environment, and then we propose a membership inference attack technique started by a malicious client that can successfully infer the privacy of other clients without getting more information. Comprehensive experimental results show the effectiveness of our MIA-FedDL and quantify user privacy leakage in federated distillation learning.
Building Intelligent Chatbots: Tools, Technologies, and Approaches

This paper aims to provide a comprehensive overview of the approaches and technologies used in building chatbots, including declarative and open-domain chatbots, and to serve as a useful resource for researchers and practitioners working in this field. We review pipeline methods, which involve the use of various techniques to process, understand, and generate natural language utterances, and end-to-end methods, which involve training a single neural network to handle all aspects of the conversation. We also examine neural generative models, which use machine learning techniques to generate responses based on input data, and retrieval-based methods, which use pre-defined responses and match them to user input. Furthermore, we review the various technologies used in building chatbots, including natural language processing (NLP) libraries, frameworks, non-cloud-based platforms, and cloud-based platforms. We provide a comparison of the strengths and limitations of these approaches and technologies and discuss the potential future directions for research in this field.
A Study of Genetic Algorithm Parameterization via a Benchmark of Test Functions

This paper presents a study to properly parameterize the Genetic Algorithm (GA) in a way to increase its performance. Parameterization is a crucial phase before any use for problem solving. An analysis of the influence of the main parameters of GA on the quality of solutions found has been shown. The parameters taken into consideration are the population size, the type of selection, the type of crossover and the mutation rate. A set of mathematical functions (unimodal and multimodal) whose global minimum is known in advance is used for this purpose.
Arabic Speech Commands Recognition with LSTM &amp; GRU Models Using CUDA Toolkit Implementation

Speech commands recognition, especially in the Arabic language, is a fertile field for research, due to the severe shortage of datasets in the Arabic language, unlike the English language, available on the most famous set of data for spoken commands, which is the Google Speech Commands dataset. This has sped up research and given rise to numerous fresh deep-learning methods involving keyword discovery. This paper introduces the classification of two classes of Arabic speech commands taken from the Arabic Speech Commands Dataset (v1.0) using LSTM & GRU models. Furthermore, the model will be trained on a GPU using NVIDIA's CUDA to reduce training time. Several experiments were run during training to investigate how various factors affect the system's performance and ensure that our model's parameters are the best. The outcomes reveal that the proposed method had good training, validation, and testing accuracy, and because of the innovation (CUDA Toolkit), the training was completed quite quickly, and the model accurately identified the commands.
Blockchain-Enabled Two-Echelon Supply Chains for perishable products using Just in Time inventory Management: A case study of the dairy industry

For decades, the perishable goods supply chains have lacked the necessary advancements to become as profitable as other industries. Due to the industry's time-constrained nature and the products' inherent characteristics, most operations need to be streamlined to avoid product expiration and recalls. Tracking perishable products in supply chains and ensuring their conformity to the standards and norms has become a priority. Customers are informed now more than ever and expect a wide range of selection and high-quality products. To meet these demands, perishable supply chains must operate as efficiently as possible, using novel methods and techniques. Blockchain technology has been used for various purposes, but few have considered perishable supply chains. The use of Blockchain in this industry will help reduce food waste and increase transparency in the supply chain. This paper proposes a proof of concept that uses smart contracts and Blockchain technology to manage perishable products flow between suppliers, retailers, and customers to allow the transfer of ownership and the tracking of the shelf-life span of dairy products. The truffle framework is used to build, test and deploy the smart contracts using solidity, while Ganache is used to simulate a test network based on the Ethereum Blockchain.
On the Fence: Anomaly Detection in IoT Networks

The Internet of Things (IoT) is increasingly impacting every aspect of life, with deployment in various societal applications. This paper explores anomaly detection via novelty and outlier detection approaches for IoT networks. To this end, three unsupervised learning algorithms, namely Isolation Forest (IF), Local Outlier Factor (LOF), and One-Class Support Vector Machine (OSVM), are evaluated on three publicly available IoT datasets. The results demonstrate that when the proposed solution leverages LOF to embrace the novelty approach by considering only pure benign data for training, it achieves high performance with Fl-scores within the range of 84% to 94%.
LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based Adaptive Live Streaming

Video content in Live HTTP Adaptive Streaming (HAS) is typically encoded using a pre-defined, fixed set of bitrate-resolution pairs (termed Bitrate Ladder), allowing play-back devices to adapt to changing network conditions using an adaptive bitrate (ABR) algorithm. However, using a fixed one-size-fits-all solution when faced with various content complexities, heterogeneous network conditions, viewer device resolutions and locations, does not result in an overall maximal viewer quality of experience (QoE). Here, we consider these factors and design LALISA, an efficient framework for dynamic bitrate ladder optimization in live HAS. LALISA dynamically changes a live video session’s bitrate ladder, allowing improvements in viewer QoE and savings in encoding, storage, and bandwidth costs. LALISA is independent of ABR algorithms and codecs, and is deployed along the path between viewers and the origin server. In particular, it leverages the latest developments in video analytics to collect statistics from video players, content delivery networks and video encoders, to perform bitrate ladder tuning. We evaluate the performance of LALISA against existing solutions in various video streaming scenarios using a trace-driven testbed. Evaluation results demonstrate significant improvements in encoding computation (24.4%) and bandwidth (18.2%) costs with an acceptable QoE.
PRINCIPIA: Opportunistic CPU and CPU-shares Allocation for Containerized Virtualization in Mobile Edge Computing

Leveraging virtualization technology, Mobile Edge Computing (MEC) deploys multiple services with different execution time requirements running as isolated processes. For instance, both real-time (RT) and non-RT applications may be (are) running on the same infrastructure using containerized virtualization. Nevertheless, sharing resources (e.g., CPU) with collocated workloads could impact the RT performance of RT applications. This paper presents PRINCIPIA, a dynamic CPU and CPU-shares allocation mechanism that opportunistically enables non-RT applications to run on underutilized CPUs while providing RT guarantees to RT applications. By monitoring MEC’s system metrics like processor’s CPU utilization and container’s CPU usage, PRINCIPIA dynamically allocates both CPU and CPU-shares to containers running non-RT applications aiming at opportunistically exploiting underutilized CPUs by containers running RT applications. We evaluate PRINCIPIA on a small-scale MEC server which uses containerized virtualization along with Linux RT Kernel to deploy both RT and non-RT applications. Our findings show that PRINCIPIA mitigates the impact on the RT performance of RT applications providing bounded processing latency in comparison with the default host Kernel scheduler.
Detection of IoT Devices and Network Anomalies based on Anonymized Network Traffic

Nowadays, a crucial aspect of IoT networks management is traffic monitoring, where Machine Learning (ML) arose as a tool to perform several tasks, such as IoT device identification and network anomalies detection. However, the access to information about network traffic can affect users’ privacy, regarding devices and users identification and, consequently, violating existing privacy regulations. Thus, a solution to perform anomaly detection without compromising privacy is necessary. Within this context, this paper presents an ML solution to perform network anomaly detection based on anonymized network traffic, ensuring privacy when identifying the device and performing features selection. Experiments using a real IoT network traffic dataset indicate that it is possible to detect network anomalies with 99% accuracy while preserving privacy.