Reviewerly/exHarmony huggingface dataset:
Learning Inter-Frame Information for Space-Time Video Super-Resolution

With the widespread popularity of playback devices, video has become the main way of human information acquisition and dissemination. However, due to the limitations of network transmission, production cost, and other factors, some devices can only obtain Low-resolution video. The loss of detailed features of low-resolution videos is serious and cannot be adapted to current HD devices. In this paper, based on the research of existing super-resolution algorithms, we focus on improving the reconstruction quality of video frame sequences. Aiming at the problems of low utilization of inter-frame information and low reconstruction quality of existing video super-resolution algorithms. In this paper, a video super-resolution reconstruction method based on interframe information utilization is proposed. Firstly, a novel U spanning feature extractor is designed to extract the feature expression of video frame sequences fully. Secondly, a deformable inter-frame alignment module based on residual learning is constructed to make the inter-frame alignment more accurate and thus promote the mutual utilization of inter-frame information. Finally, an upsampling and downsanpling residual block is proposed to extract features that better match the upsampling reconstruction requirements. The experimental results show that this method has better reconstruction quality and is advanced and applicable compared to mainstream video oversampling methods.
A Batching Method For Complex Emitters Based On Frequency Agility Pattern Mining

Waveform agility is the key characteristic of existing airborne AESA radars. The carrier frequency and other waveform parameters of AESA radar signals can change dynamically in milliseconds. This brings a difficult problem for the batching of the AESA radar signals. To solve this problem, a batching method for complex AESA emitters based on frequency agility pattern mining is proposed in this paper. The frequency agility patterns are mined across multiple deinterleaving time intervals, and applied in the emitter batching to merge EDWs (Emitter Description Words) with different frequency parameters. Finally, experiments with intercepted real data demonstrate that our proposed method is very effective in the batching of complex AESA radar signals.
InterCriteria Analysis of the Global Competitiveness Report for the Financial System EU Countries

In recent years, innovative techniques for data analysis and decision-making, resulting from the processing of a large database, have been increasingly used. This trend is part of the management process, for example in the field of finance, where through the application of elements of artificial intelligence, data is processed, processes are analyzed, risk is assessed and intelligent solutions are proposed.In this paper, data from The Global competitiveness report of the World Economic Forum is processed in order to analyze the financial systems of EU Countries. The emphasis is on the data from Pillar 9 - Financial System. It consists of the indicators - Debt and Stability. These in turn include several sub-indicators.The analysis was carried out by applying the Intercriteria Analysis.
The Use Of Chat-Bot Capabilities As A Type Of Modeling In Intelligent Learning

The use of chatbots these days is reflected in almost all spheres of activity: from e-commerce to industry and education. In this study we have highlighted some important features of using chatbots in the educational process, studied the practice of their use in higher education institutions, which allowed us to identify the main advantages and disadvantages of their use, as well as to formulate general recommendations aimed at improving the effectiveness of their use in the educational process. Among other things, the paper considers the prospects and theoretical aspects of the relevance and effectiveness of using chatbots in the educational process, acting as a tool to improve the interaction between teachers and students in terms of organizational and methodological issues. This article considers artificial intelligence and the use, advantages and disadvantages of using chatbots in the virtual learning of future teachers of computer science. An analysis and classification of chatbots is made and proposed. The results of the experiment consisting in the application of the developed in the course of the study chat-bot in the educational process are presented. Teachers and students in the specialty "Informatics" took part in the experiment. The experiment proved the effectiveness of the chat-bot application in the classroom. The results confirm our hypothesis that for effective organization of intelligent learning teachers need appropriate virtual assistants in the form of chatbots. The results of this study allow us to improve the experience of using chatbots in the educational environment with the help of intelligent modeling.
A Multivariate Time Series Anomaly Detection Model Based on Graph Attention Mechanism in Energy Consumption of Intelligent Buildings

The problem of anomaly detection in the field of intelligent buildings is very important, such as using energy consumption data to detect anomalies in intelligent buildings’ energy facilities. Currently, existing univariate time series anomaly detection methods based on energy consumption data are frequently used in the field of intelligent buildings for anomaly detection. These methods not only ignore other factors that may cause anomalies in building energy consumption but also do not explicitly learn the correlation between multiple time series feature variables, which results in one-sided anomaly detection results. So, we propose a multivariate time series anomaly detection method. First, the method introduces a graph convolutional neural network to build an anomaly detection framework based on graph convolutional neural networks by taking advantage of its ability to extract feature correlations among multiple variables. Second, we offer a graph attention method to enhance the detection framework by increasing attention weights to significant time series features to better choose time series features that are more critical for anomaly identification. Exploring the relationships between time series and feature dimensions can help with building energy consumption anomaly detection. We used a large-scale standard dataset for building energy anomaly detection and compared the effectiveness of this paper’s method with the traditional method for detecting energy anomalies in intelligent buildings. Experimental results show that the model improves detection accuracy while maintaining a low overhead.
Building NLP Tools to Process Sinhala Text Data Written using English Letters

Sri Lanka is a country where the Sinhala and English languages take a prominent place, but most people are not so fluent in the two languages when using digital platforms like social media. Although most of the Sri Lankans use Singlish there is no proper application to translate the relevant language into the main two languages, Sinhala or English.The purpose of this research is to bring more attention of the researchers towards the sub varieties of Sinhala language, especially Singlish by bridging the gap between NLP tools available for processing textual data of main languages and their varieties. And each component focuses on translating Singlish text data into English, translating Singlish into Sinhala, sentiment analysis for Singlish text data and mapping emojis for Singlish tokens.There are language translation tools to translate many languages like Russian, Italian, Japanese into the English Language but there are no proper tools to translate the mainly used sublanguage in Sri Lanka; that is Singlish. Therefore, the proposed solution for the above problem is developing a Singlish to English Language Translation Model and removing slang words to allow NLP technologies created for English to be utilized to handle Singlish textual data.This NLP model shall have the capacity of translating Singlish words that are used on social media into English and if there are any slang words found during the translation, those words will be identified and removed. Translating the Singlish textual data into English would help the users to use other tools built to plagiarize, tools that are used to summarize and tools that are used to paraphrase English text.
Decentralized Blockchain Network

Recommender systems strongly affect a company’s profitability and regularly play a significant role in the success of companies’ sales policies. Companies are now collecting vast amounts of user information to make sure they can use recommender systems and related algorithms for recommending products and services according to customer needs, preferences, and likenesses. Unfortunately, privacy concerns prevent users from sharing data generously with interested companies, even if the quality of the recommendations would be improved. One way to overcome privacy-related issues is using a secured solution, such as blockchain technologies for privacy-based applications. Integrating blockchain technology and the Internet of Things creates modern decentralized systems. The integration gives scalability and security to recommender systems. This research calls for innovative and advanced research on Blockchain and recommendation systems. We constructed a blockchain-based recommender system using “the Movielens” dataset. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recommendation system with acceptable performance.
A Hybrid Attack Graph Analysis Method based on Model Checking

Attack graph is an essential tool for the security assessment of industrial Internet systems. Unfortunately, the traditional attack graph mainly focuses on discrete information and cannot handle continuous information such as temperature and humidity. These continuous data represent the states of the industrial Internet and are also indicators of attacks, which should be included in the attack graph. To this end, this paper presents a hybrid attack graph model, which can simultaneously describe the discrete and continuous information of the system. Based on the hybrid attack graph model, a security analysis method is presented. Firstly, the transformation rule from hybrid attack graph to timed automata is established. Secondly, the security attribute of the system is described by sequential logic of continuous interval, and then the model checker TACK is used to complete the analysis of the system. Finally, an example is analyzed and verified by a smart home system to illustrate the method’s effectiveness. The method in this paper provides a new idea for attack graph construction and analysis for industrial Internet systems.
Unsupervised Semantic Segmentation with Feature Enhancement for Few-shot Image Classification

Image classification is a typical task in big data applications. As a few-shot learning (FSL) task, the few-shot image classification attempts to learn a new visual concept from limited labelled images. The existing few-shot image classification methods usually fail to effectively eliminate the interference of image background information, thus affecting the accuracy of image classification. To overcome the shortcomings of existing methods and eliminate the interference of background information, this paper proposes a novel method named Unsupervised Semantic Segmentation with Feature Enhancement (USSFE) for few-shot image classification. USSFE uses the dense contrastive learning to train a robust unsupervised semantic segmentation model for foreground segmentation, and enhances feature representations of the foreground feature maps by extracting the shape and texture features of the foreground images. Our experimental results on two benchmark datasets, miniImageNet and tieredImageNet, show that the proposed USSFE method achieves better performance than the state-of-the-art methods.
WMLink: Wearable Smart Devices and Mobile Phones Linking through Passive Sniffing BLE and WiFi Signals

In recent years, wearable smart devices have entered people’s lives. These devices can help users to record their daily activities, health data and so on. These devices periodically send out packets to keep connected with mobile phones, which can be observed by any external observer. By analyzing these packets, some information about the device owner will be revealed. Although the observer can sniff the wireless packets, the actual person using the device is unknown. In this paper, we link wearable smart devices with users’ mobile phones which are recognized as users’ electronic identity. Linking these two types of devices opens up new opportunities for applications such as authenticating users by multi-devices or constructing user device graph for advertisements. We deploy sniffing nodes to sniff BLE and WiFi signals, and put forward a multi-stage filtering method to reduce signals’ noises, then link two types of devices by an algorithm based on dynamic time warping. At last, our experiments show that 7 wearable devices can be linked with a correct rate more than 80%. We also find that when one device is observed more than 11 times, its linking accuracy can be close to 100%.
Space-efficient logging for Supply Chain Traceability based on blockchain

Supply chain traceability has become a prime requirement for industries to ensure product quality. Existing solutions often rely on trusted third parties to track the provenance and journey of products in a centralized manner. Blockchain provides a decentralized solution that can achieve immutability and auditability for digital records of supply chain transactions without the need of third parties. However, existing blockchain-based methods do not carefully consider the tracing process, which often needs to retrieve transaction logs related to the target product from a large number of irrelevant logs, resulting in unsatisfactory space overhead and slow query time. Moreover, current smart contracts often ignore the time spent on Ethereum calls, which consume a lot of time when fetching logs. In this paper, we propose a space-saving blockchain-based mechanism to efficiently explore the transaction logs for supply chain traceability. The mechanism introduces a new filter to identify relevant logs fast while using little space, and we propose a new token that extends the data structure in existing smart contracts, reducing the number of Ethereum calls. The experimental results show that our scheme achieves 49% improvement on space consumption and 400ms speed-up in time consumption compared with the state-of-art methods.
Reservoir: Enhance the Burst-flow Tolerance in Datacenter Networks

Micro-burst flow and Incast seriously damage the performance of the datacenter network. However, end-to-end congestion control protocols are difficult to control congestion within the first RTT. People expect to use the buffer capacity of other switches to store a large number of burst packets. However, when the load increases, the switch resources are insufficient to support the storage of excessive packets. In order to solve the above problems, we propose Reservoir, a scheme that uses the butter capacity of adjacent hosts to buffer packets. We conduct extensive experiments to evaluate the performance of Reservoir. The experimental results show that Reservoir reduces the average Incast query completion time by 8%, 5% compared to ECMP, DIBS under light load. Under heavy load, we find that Reservoir reduces the average flow completion time by 25%, 15% and increases throughput nearly 10% compared to ECMP, DIBS.
Efficient and Effective Expert Finding based on Community Search: A Demonstration

In recent years, with the vigorous development of social networks, various kinds of data in the network have shown an explosive growth trend. Among them, a large amount of data in the field of academics includes rich and diverse entity information such as high-quality academic papers, experts, venues, and topics that have intricate and complex relationships, constituting important heterogeneous academic networks, e.g., DBLP. Many expert finding systems have been investigated on the academic network. But most of them are using textual keyword matching techniques to support the systems. Different from the above systems, we designed and implemented an expert finding system to effectively and efficiently return desired experts not only based on textual keyword matching, but also on the experts’ relationship achieved by community search. It contains three layers: data processing layer, core algorithm layer, and application layer. The data processing layer is responsible for data collection and processing to construct heterogeneous academic networks. The core algorithm layer includes the academic network community search algorithm and Top-n expert finding through multiple recalls based on the Threshold algorithm. The application layer receives data from the core algorithm layer to present to users at the front end. On this basis, our core algorithms can also be migrated to other applications, e.g., recommendation, biological data analysis, reviewer assignment, and public safety protection.
Deep Learning based Hand-Drawn Molecular Structure Recognition and 3D Visualisation using Augmented Reality

Due to both false-positive structure identification and flaws in the predicted structures, chemical structure identification from documents remains a complex challenge. Current techniques rely on customized rules and subroutines that, although usually effective, recognition rates are insufficient and systematic improvement is difficult at certain times. Especially when it comes to the identification of hand-drawn Lewis Structures of molecules, most of these existing methodologies fail. Therefore, through this research, we present a system to identify a camera-captured, hand-drawn Lewis structure of a molecule using Machine Learning and Deep Learning concepts. Due to less availability of hand-drawn Lewis structures, we had to make our own dataset and therefore the project was limited to 15 different hydro carbons. Moreover, we provide the users with a mobile application that can visualize the identified molecule in a 3-D space using Augmented Reality. Our machine learning model details are available on the Github (https://github.com/MZJGroup/MoleAR)
Attentional Meta-path Contrastive Graph Convolutional Networks for Knowledge Concept Recommendation

Knowledge concept recommendation provides more professional service to learners with different backgrounds on Massive open online courses(MOOCs) platforms, which was introduced to track and keep students’ interests in a fine-grained view. While some studies construct a heterogeneous information network(HIN) with types of entities and their relationships in MOOCs, which capture the representation of learners and concepts from the HIN to alleviate the influence of the data sparsity, two main limitations in meta-path graph-based knowledge concept recommendation methods hinder the further development: i) Higher-order features of the similarity between learners (concepts) in different meta-paths have not been fully explored. ii) Deviation was found on the distribution of learners’ interest.To address the two issues, we propose a novel Knowledge concept recommendation approach called Attentional Meta-path Contrastive Graph Convolutional Networks for Knowledge Concept Recommendation(AMCGRec). Specifically, on each predefined meta-path on the HIN, we introduce the learners (or concepts) and their structural neighbors to construct self-supervision signals, which can capture learners’ interest from higher-order features of the similarity between learners (concepts). Then, we use an attention mechanism to automatically adjust the importance of different meta-paths. Finally, we leverage InfoNCE loss as the training objective to alleviate the bias of learners’ interest based on interaction data. Extensive experiments on a real-world MOOC dataset show that our proposed AMCGRec outperforms the state-of-the-art other knowledge concept recommendation methods. Our implementation code is available at: https://github.com/omoPucldAMCGRec.
A Personalized Scenic Spot Recommendation Algorithm Based on Improved TrustSVD Model

Recommendation system is one of the effective ways to solve the problem of information overload. Compared with other recommendation systems, there are data sparsity and cold start problems for tourism recommendation systems, so it should consider more factors. In recent years, researchers have improved and applied various recommendation methods, have proposed many tourism recommendation methods and developed corresponding tourism recommendation systems. In this paper, after analyzing the different problems faced by the tourism recommendation system compared with other information recommendation systems, an improved S-TrustSVD personalized scenic spot recommendation method based on category similarity is proposed that is aiming at the problem for the existing scenic spot recommendation algorithm does not consider users’ personal scoring habits, the viewing effect of scenic spots, as well as the sparsity and cold start of users’ scenic spot check-in data, to analyze and improve the tourism recommendation method integrating users’ social relationship matrix decomposition model TrustSVD. The performance of proposed method with other recommendation methods are verified and compared on the tourism public data set of yelp website. The experimental results show that the proposed method not only improves the recommendation accuracy, but also alleviates the problems of data sparsity and cold start.
Mitigating the effects of temporal distortion in a copy-detection based playback attack detector

Temporal distortion, which is occasionally inflicted by the Voice-over-IP (VoIP) channel, often changes the durations of the silence intervals between speech segments. This poses a challenge for a copy-detection based playback attack detector that detects attacks by identifying overly similar recordings using a cross-correlation based similarity measure. In this paper, a new similarity measure, inspired by the Needleman-Wunsch algorithm, is proposed as a solution. Experimental results based on a publicly available database not only confirm the presence of temporal distortion in some of the recordings, but also demonstrate the capability of the new similarity measure in countering the adverse effect of temporal distortion. As a result, the Equal Error Rate (EER) is reduced from 2.26% to 1.94%.
A Model of Data Processing in Cyber-Physical Social Space Agriculture

The information and social spaces integrate heterogeneous technologies and different approaches for collecting, storing and processing information related to the plant genetic resources and smart crop production of Bulgaria. The paper discusses aspects of data processes in the Cyber-Physical Social Space agriculture. A modeling approach using the Generalized Nets was chosen. A model of the main elements of the space and their interaction was created, with focal areas of data storage and processing.