Reviewerly/exHarmony huggingface dataset:
A Systematic Literature Review: Learning with Visual by The Help of Augmented Reality Helps Students Learn Better

Studying is a part of a student’s life, but not everyone enjoys studying. The long texts and unappealing content make the students lose their interest. To resolve this problem, we suggest changing the conventional method of studying to a new way using Augmented Reality (AR) as a learning tool. Some literatures have used AR technology to help students learn some new concepts. By combining the digital and physical environment, studying activity can be more fun for students. This paper would like to evaluate the effectiveness and the results of using AR in educational purposes by conducting Systematic Literature Review (SLR). The relation between authentic learning and AR is also considered. Based on our study, we can conclude that using AR as a learning tool help student to learn better for certain subjects. This is mainly by visualizing abstract concepts to be 3D object. Therefore, AR can be used to learn concretely the structure of abstract concepts. Furthermore, students think that AR is interesting and gave them the motivation to study more. The students can also easily remember and recall information because the experience is more engaging. In general, AR is more effective as medium of the learning media because the students can see the reproduced real objects in learning and practicing. Further research and development will make AR a more promising learning tool in the future.
A probabilistic clustering model for hate speech classification in twitter

The key challenges for automatic hate-speech classification in Twitter are the lack of generic architecture, imprecision, threshold settings and fragmentation issues. Most studies used binary classifiers for hate speech classification, but these classifiers cannot really capture other emotions that may overlap between positive or negative class. Hence, a probabilistic clustering model for hate speech classification in twitter was developed to tackle problems with hate speech classification. A metadata extractor was used to collect tweets containing hate speech keywords and a crowd-sourced experts was employed to label the collected hate tweets into two categories: hate speech and non-hate speech. Features representation was done with Term Frequency- Inverse Document Frequency (TF-IDF) model and enhanced with topics inferred by a Bayes classifier. A rule-based clustering method was used to automatically classify real-time tweets into the correct topic clusters. Fuzzy logic was then used for hate speech classification using semantic fuzzy rules and a score computation module. From the evaluation results, it was observed that the developed model performed better in hate speech detection with F1-sore of 0.9256 using a 5-fold cross validation. Similarly, the developed model for hate speech classification performed better with F1-score of 91.5 compared to related models. The developed model also indicates a more perfect test having an AUC of 0.9645, when compared to similar methods. The Paired Sample t-Test validated the efficiency of the developed model for hate speech classification.
Why My Code Summarization Model Does Not Work

Code summarization aims at generating a code comment given a block of source code and it is normally performed by training machine learning algorithms on existing code block-comment pairs. Code comments in practice have different intentions. For example, some code comments might explain how the methods work, while others explain why some methods are written. Previous works have shown that a relationship exists between a code block and the category of a comment associated with it. In this article, we aim to investigate to which extent we can exploit this relationship to improve code summarization performance. We first classify comments into six intention categories and manually label 20,000 code-comment pairs. These categories include “what,” “why,” “how-to-use,” “how-it-is-done,” “property,” and “others.” Based on this dataset, we conduct an experiment to investigate the performance of different state-of-the-art code summarization approaches on the categories. We find that the performance of different code summarization approaches varies substantially across the categories. Moreover, the category for which a code summarization model performs the best is different for the different models. In particular, no models perform the best for “why” and “property” comments among the six categories. We design a composite approach to demonstrate that comment category prediction can boost code summarization to reach better results. The approach leverages classified code-category labeled data to train a classifier to infer categories. Then it selects the most suitable models for inferred categories and outputs the composite results. Our composite approach outperforms other approaches that do not consider comment categories and obtains a relative improvement of 8.57% and 16.34% in terms of ROUGE-L and BLEU-4 score, respectively.
DMRFNet: Deep Multimodal Reasoning and Fusion for Visual Question Answering and explanation generation

Visual Question Answering (VQA), which aims to answer questions in natural language according to the content of image, has attracted extensive attention from artificial intelligence community. Multimodal reasoning and fusion is a central component in recent VQA models. However, most existing VQA models are still insufficient to reason and fuse clues from multiple modalities. Furthermore, they are lack of interpretability since they disregard the explanations. We argue that reasoning and fusing multiple relations implied in multimodalities contributes to more accurate answers and explanations. In this paper, we design an effective multimodal reasoning and fusion model to achieve fine-grained multimodal reasoning and fusion. Specifically, we propose Multi-Graph Reasoning and Fusion (MGRF) layer, which adopts pre-trained semantic relation embeddings, to reason complex spatial and semantic relations between visual objects and fuse these two kinds of relations adaptively. The MGRF layers can be further stacked in depth to form Deep Multimodal Reasoning and Fusion Network (DMRFNet) to sufficiently reason and fuse multimodal relations. Furthermore, an explanation generation module is designed to justify the predicted answer. This justification reveals the motive of the model’s decision and enhances the model’s interpretability. Quantitative and qualitative experimental results on VQA 2.0, and VQA-E datasets show DMRFNet’s effectiveness.
Face morphing detection in the presence of printing/scanning and heterogeneous image sources

Face morphing represents nowadays a big security threat in the context of electronic identity documents as well as an interesting challenge for researchers in the field of face recognition. Despite of the good performance obtained by state-of-the-art approaches on digital images, no satisfactory solutions have been identified so far to deal with cross-database testing and printed-scanned images (typically used in many countries for document issuing). In this work, novel approaches are proposed to train Deep Neural Networks for morphing detection: in particular generation of simulated printed-scanned images together with other data augmentation strategies and pre-training on large face recognition datasets, allowed to reach state-of-the-art accuracy on challenging datasets from heterogeneous image sources.
Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.
Blockchain and Deep Reinforcement Learning Empowered Spatial Crowdsourcing in Software-Defined Internet of Vehicles

Owing to its benefits such as flexibility, scalability, and interoperability, Software-Defined Networking (SDN) has been incorporated into Internet of Vehicles (IoV) to cope with the increasing demands of vehicular applications. The integration of SDN and IoV, namely SDN-IoV, can enrich many new applications for intelligent transportation such as traffic monitoring, smart navigation, and self-driving. The spatial crowdsourcing technology has been adopted as an effective data collection and processing method that is the premise of various SDN-IoV applications. However, as huge amounts of data are generated in spatial crowdsourcing services, the data privacy and security has become a key challenge for SDN-IoV. To overcome abovementioned challenge, a Deep Reinforcement Learning (DRL) and Blockchain empowered Spatial Crowdsourcing System (DB-SCS) is proposed. In DB-SCS, we design an improved multi-blockchain structure and a blockchain-based hierarchical task management method, which divide the spatial tasks into different categories according to the privacy requirements and the areas of the task and then decompose different categories of tasks and task receivers into sub-blockchains. While guaranteeing the data privacy, DB-SCS can also enhance the spatial crowdsourcing performance by using the proposed DRL-based management strategy to dynamically select the consensus algorithm, block size, and block generation rule. Extensive simulation experiments demonstrate that the DB-SCS can obtain high throughput, low overhead, and data privacy under various SDN-IoV scenarios.
On Fuhr's guideline for IR evaluation

In the December 2017 issue of SIGIR Forum, Fuhr presented ten "Thou Shalt Not"s (i.e., warnings against bad practices) for IR experimenters. While his article provides a lot of good materials for discussion, the objective of the present article is to argue that not all of his recommendations should be considered as absolute truths: researchers should be aware that there are other views; conference programme chairs and journal editors should be very careful when providing a guideline for evaluation practices.
Wireless sensor networks based Agriculture 4.0: challenges and apportions

Today, the penetration of digital technologies and wireless sensor networks (WSN) in every field is leading to a new direction of research. The use of wireless sensor networks in agriculture is also leading to many scientific advances and shortcomings. Experience from around the world shows that remote monitoring, control, monitoring and sales of agricultural products using IoT, sensors and means of communication are carried out using wireless sensors in the quality of work. The study of IoT and WSN, their advantages and disadvantages, allows us to consider the current penetration of agriculture, as well as its potential value in the future.
An examination of the cryptocurrency pump-and-dump ecosystem

The recent introduction of thousands of cryptocurrencies in an unregulated environment has created many opportunities for unscrupulous traders to profit from price manipulation. We quantify the scope of one widespread tactic, the “pump and dump”, in which actors coordinate to bid up the price of coins before selling at a profit. We joined all relevant channels on two popular group-messaging platforms, Telegram and Discord, and identified thousands of different pumps targeting hundreds of coins. We find that pumps are modestly successful in driving short-term price rises, but that this effect has diminished over time. We also find that the most successful pumps are those that are most transparent about their intentions. Combined with evidence of concentration among a small number of channels, we conclude that regulators have an opportunity to effectively crack down on this illicit activity that threatens broader adoption of blockchain technologies.
Collaborative Edge Computing With FPGA-Based CNN Accelerators for Energy-Efficient and Time-Aware Face Tracking System

Convolutional neural networks (CNNs) have become the critical technology to realize face detection and face recognition in the face tracking (FT) system. However, traditional CNNs usually have nontrivial computational time and high energy consumption, making them inappropriate to be deployed in the large-scale time-sensitive FT system. To address this challenge, we design an artificial intelligence and Internet of Things (AIoT) empowered edge-cloud collaborative computing (ECCC) system based on the energy-efficient field-programmable gate array (FPGA)-based CNN accelerators for the purpose of realizing a low-latency and low-power FT. First, we present the AIoT-empowered ECCC system architecture, which consists of an intelligent computing subsystem, an Internet-of-Things (IoT) subsystem, an edge-cloud collaborative subsystem, and an application subsystem. In what follows, we investigate the enabling technologies for these subsystems. Thereafter, we develop an FPGA-based hardware accelerator dedicated to the compact MobileNet CNN by using the hardware design techniques, such as systolic array, matrix tiling, fixed-point precision, and parallelism. Furthermore, we integrate the FPGA accelerators with CPUs and GPUs to build a context-aware CPU/GPU/FPGA heterogeneous computing system. Finally, we implement a delay-aware energy-efficient scheduling algorithm dedicated to this heterogeneous system. With the above hardware and software codesign mechanism, the energy cost and execution time of CNNs can be decreased significantly. The real-world experiments on the CPU/GPU/FPGA-based ECCC system proved the effectiveness of the proposed schemes in reducing the latency and improving the power efficiency of the FT system.
On construction and (non)existence of c-(almost) perfect nonlinear functions

Functions with low differential uniformity have relevant applications in cryptography. Recently, functions with low c-differential uniformity attracted lots of attention. In particular, so-called APcN and PcN functions (generalization of APN and PN functions) have been investigated. Here, we provide a characterization of such functions via quadratic polynomials as well as non-existence results.
IR-Rec: An interpretive rules-guided recommendation over knowledge graph

Most existing recommendation methods focus on the improvement of recommender accuracy while ignoring the influence of recommended explanation. Recommender explainability is an efficient way to help consumers make much more suitable decisions and enhance their acceptance and trustfulness in recommender systems (RSs). Incorporating a knowledge graph (KG) into RS is a promising way to improve recommender results while enhancing the strength of explanation. In this paper, an interactive rules-guided recommender (IR-Rec) framework based on KG is proposed. First, an existing KG is enriched by introducing it to facts about e-commerce. Then, a number of paths are extracted from the enhanced KG for user-item interactions that are able to ascertain the underlying reason for recommendations according to the semantic strength of the KG. These paths are summarized into some common behavior rules that have the ability to explain the underlying motivations of users. According to the characteristics of users, items, and rules, different neural networks are designed, such as a graph convolutional network, to learn more accurate embeddings. Furthermore, recommendations that meet users’ personalized interests are made by combining the public behavior rules and their individual features. Extensive experiments are carried out on four Amazon datasets for top-K recommendation. All the results show that the proposed method performs better than other respective baselines and demonstrate the effectiveness of rule extraction for making recommendations.
Secure and Lightweight Fine-Grained Searchable Data Sharing for IoT-Oriented and Cloud-Assisted Smart Healthcare System

It is a new trend in healthcare informatization construction to build the smart healthcare system by using the Internet of Things (IoT) and cloud. This IoT-oriented and cloud-assisted healthcare system enables the doctor to monitor the patient’s health state to respond to the paroxysmal diseases in real time. Considering the sensitivity of the patient’s privacy, it is necessary to encrypt the cloud-stored health data to prevent the semi-trusted cloud and unauthorized users from accessing them. However, the encrypted health data stored in the cloud brings inconvenience to the retrieval for the data user. In addition, the expensive computational consumption also raises the challenge to the resource-constrained devices in the patient and doctor sides. To support efficient ciphertext retrieval and cope with the performance challenge, in this article we propose a lightweight attribute-based searchable encryption (LABSE) scheme, which realizes fine-grained access control and keyword search, while reducing the computational overhead for the resource-constrained devices. We rigorously prove the semantic security of the proposed LABSE scheme, and analyze other security properties to response the security requirements under the healthcare scenario. Subsequently, we construct a concrete deployment model for LABSE under the healthcare system. We also compare LABSE with the state-of-the-art related schemes in terms of functionality and complexity. Finally, we demonstrate the practicality and performance advantages by the experiment.
A Novel IoV Block-Streaming Service Awareness and Trusted Verification Scheme in 6G

6 G and mobile Internet-of-Vehicles (IoV) technology require a secure, open, and transparent system. Blockchain has the characteristics of decentralization, non-tampering, and traceability, which can improve the robustness, data privacy, and security transparency of the overall system. Therefore, blockchain will be the most promising technology to ensure the security and privacy of 6 G and vehicle networks. In this article, we propose a novel IoV block-streaming service awareness and trusted verification in 6 G. The edge node uploads the microservices and calling diagram of microservices to the blockchain network. The blockchain network serves as an intermediate verification platform for the edge node and the IoV equipment to record the evidence of interaction between the two ends. Moreover, combined with identity-based blind signature technology, we have designed a security scheme in which IoV devices anonymously request services from edge nodes, which can realize the mutual authentication of IoV devices and edge servers while keeping the user's real identity information confidential. In addition, an edge caching mechanism based on user requests and service awareness is designed to pre-compile services at edge nodes, improve the cache hit rate of service requests from vehicle users on the edge server, and achieve efficient processing of resource requests from users.
Technology for Protecting Video Information Resources in the Info-Communication Space

Practical aspects of realization of technology of cryptocompression representation of images are presented. The technology is based on a combination of image compression methods and cryptography. Its use provides increased efficiency in the formation of an encrypted code sequence of images with the elimination of redundancy of information without loss of quality during its storage and transmission in info-communication systems for crisis purposes.
Linguistic Steganalysis With Graph Neural Networks

Recent linguistic steganalysis methods model texts as sequences and use deep learning models to extract discriminative features for detecting the presence of secret information in texts. However, natural language has a complex syntactic structure and sequences have limited representation ability for text modeling. Moreover, previous methods tend to extract features from local continuous word sequences, which cannot effectively model global characteristics. In this paper, we present a linguistic steganalysis method with graph neural network. In the proposed method, texts are translated as directed graphs with the associated information, where nodes denote words and edges show associations between the words. By training a graph convolutional network for feature extraction, each node of a graph can collect contextual information to update self-expression, accordingly effectively solving the problem of poor representation of polysemous words. Meanwhile, we adopt a globally-shared matrix to record correlation strengths between words so that each text can effectively utilize the global information to obtain the better self-representation. Experimental results have shown that the proposed work achieves the state-of-the-art performance comparing with the previous works.
Using augmented reality technologies for STEM education organization

Abstract Modernization of the education system and the emergence of innovative learning technologies can improve the educational process. The use of augmented reality technology improves the learning of individual students, their motivation, as well as helps in organizing teamwork, group cooperation. As the topic of augmented reality in education is quite new and little studied for STEM education, the study reviews publications on this topic, describes the concept of augmented reality, the analysis of augmented reality technologies is carried out, which are adapted to the teaching of natural and mathematical disciplines. The role of STEM approach with augmented reality in the educational process is determined. An example of the use of augmented reality as part of a robotics project is given.