Reviewerly/exHarmony huggingface dataset:
Extractive Text Summarization Using Syntactic Sub-graph Models

Summarization systems are needed to handle the vast amounts of text data being collected by machine-controlled systems. These systems help users to get a better understanding of the main concepts of the paper. This type of summary is produced by people who produce extract summaries. It involves gathering significant excerpts from a text and not creating a new summary. The graph-based approaches are very popular in terms of text summarisation. This paper presents an example of generating single-document summaries using Dijkstra algorithm and Rectangles. The second phase is to construct sub-graphs and then bit vector representation. The final section is to retrieve the derived sentences.
Online Influence Forest for Streaming Anomaly Detection

As the digital world grows, data is being collected at high speed on a continuous and real-time scale. Hence, the imposed imbalanced and evolving scenario that introduces learning from streaming data remains a challenge. As the research field is still open to consistent strategies that assess continuous and evolving data properties, this paper proposes an unsupervised, online, and incremental anomaly detection ensemble of influence trees that implement adaptive mechanisms to deal with inactive or saturated leaves. This proposal features the fourth standardized moment, also known as kurtosis, as the splitting criteria and the isolation score, Shannon's information content, and the influence function of an instance as the anomaly score. In addition to improving interpretability, this proposal is also evaluated on publicly available datasets, providing a detailed discussion of the results.
Reducing Trust Assumptions with OSCORE, RISC-V, and Layer 2 One-Time Passwords

In the Internet of things (IoT), traffic often goes via middleboxes, such as brokers or virtual private network (VPN) gateways, thereby increasing the trusted computing base (TCB) of IoT applications considerably. A remedy is offered by the application layer security protocol Object Security for Constrained RESTful Environments (OSCORE). It allows for basic middlebox functions without breaking end-to-end security. With OSCORE, however, traffic is routed to IoT devices largely unfiltered. This opens up avenues for remote denial-of-sleep attacks where a remote attacker injects OSCORE messages so as to cause IoT devices to consume more energy. The state-of-the-art defense is to let a trusted middlebox perform authenticity, freshness, and per-client rate limitation checks before forwarding OSCORE messages to IoT devices, but this solution inflates the TCB and hence negates the idea behind OSCORE. In this paper, we suggest filtering OSCORE messages in a RISC-V-based trusted execution environment (TEE) running on a middlebox that remains widely untrusted. To realize this approach, we also put forward the tiny remote attestation protocol (TRAP), as well as a Layer 2 integration that prevents attackers from bypassing our TEE. Experimental results show our remote denial-of-sleep defense to be lightweight enough for low-end IoT devices and to keep the TCB small.
Gender Classification Using CNN Transfer Learning and Fine-Tuning

The important task of analyzing facial images is performed by a soft biometric application known as gender classification. Convolutional Neural Network (CNN) is currently one of the deep learning techniques used to classify gender to address a variety of issues, including intelligent advertising, tourism, surveillance systems, and other fields. However, CNN requires a lot of power to process information quickly and accurately. Thankfully, transfer learning has been developed to solve this issue. The aim of this study is to evaluate the accuracy of gender classification using transfer learning techniques compared to methods that train data from scratch. The MobileNet and MobileNetv2 models are used in this study because they are among the fastest and the models with the least amount of power consumption. The transfer learning process will then be used to refine these two models by evaluating their performance and processing two different types of FaceARG datasets that have been divided into two different face skin color (Bright and Dark). According to the experimental findings, the accuracy of MobileNetv2 increased to 92% for Bright face skin color datasets and 89% for Dark face skin color datasets. This paper demonstrates that CPU usage is still relevant if the model performs transfer learning before the classification process.
COVID-19 Face Mask Classification Using Deep Learning

The COVID-19 pandemic has triggered a global health disaster because its virus is spread mainly through minute respiratory droplets from coughing, sneezing, or prolonged close contact between individuals. Consequently, World Health Organization (WHO) urged wearing face masks in public places such as schools, train stations, hospitals, etc., as a precaution against COVID-19. However, it takes work to monitor people in these places manually. Therefore, an automated facial mask detection system is essential for such enforcement. Nevertheless, face detection systems confront issues, such as the use of accessories that obscure the face region, for example, face masks. Even existing detection systems that depend on facial features struggle to obtain good accuracy. Recent advancements in object detection, based on deep learning (DL) models, have shown good performance in identifying objects in images. This work proposed a DL-based approach to develop a face mask detector model to categorize masked and unmasked faces in images and real-time streaming video. The model is trained and evaluated on two different datasets, which are synthetic and real masked face datasets. Experiments on these two datasets showed that the performance accuracy rate of this model is 99% and 89%, respectively.
Image steganography approach using spatial and transform domain technique

Cryptography is often used to keep information confidential by making it illegible. However, incomprehensible information may raise suspicions of the adversary and may lead him to sabotage this style of communication. Therefore, steganography has a place in the arena of information security. Steganography refers to the technique of hiding information in digital media to hide the existence of information. Data caching is the art of hiding data for colorful purposes similar as; to maintain private data, secure non-public data and so on. Securely exchange the data over the internet network is veritably important issue. So, to transfer the data securely to the destination, there are numerous approaches like cryptography and steganography. In this design we propose a LSB &amp; DCT grounded steganographic system for hiding the data by applying Least Significant Bit (LSB) algorithm for bedding the data into the images which is enforced through the HTML, CSS, JavaScript.
Toward a Solution for an Energy Knowledge Graph

Data integration demands the development of data management techniques to efficiently overcome interoperability issues and provide a harmonized view of both data and their meaning (i.e., metadata). This paper addresses the challenges of energy data management and integration and proposes a process of creating a knowledge graph, motivated by the needs of the stakeholders from Serbia and related to the integration of a large number of different renewable energy sources (RES) with the proprietary SCADA system of the Institute Mihajlo Pupin. The Energy Knowledge Graph (KG) has been built by reusing the energy-based semantic data model and the SDM-RDFizer, an open-source tool and interpreter of the W3C Recommendations Standard R2RML and its RDF Mapping Language (RML) extension. The data connectors implemented by the SDM-RDFizer plan the execution of the mapping rules and loading of the dataset to an RDF triple store to speed up the process of knowledge base creation. The Energy KG has been deployed on a Smart Grid Architecture Model (SGAM)—compliant platform hosted at the Institute Mihajlo Pupin.
Extension of Fuzzy Principal Component Analysis to Type-2 Fuzzy Principal Component Analysis

This chapter compares type 1 and type 2 fuzzy principal component analysis which are based on type 1 and type 2 fuzzy C-means algorithms, respectively. The two clustering methods are the combination of k-means clustering algorithm and type 1 and type 2 fuzzy logic, respectively.
An Ethnolinguistic Research Agenda for Intelligent Autonomous Systems

Depicting intelligence by autonomous systems by extracting patterns from heterogeneous data streams poses significant computational and analytical challenges in real-time. We need technologies that can provide multilingual services while respecting each culture and its values and ethics. In many cases, language is not the only reason for misunderstandings and unsatisfactory services, but sometimes cultural properties also play an equivalent role. Current technologies can prominently understand and work with multilingual information; what is required is to embed the cultural constructs and properties during automation. Ethnolinguistics is precisely talking about that, i.e., the relationship between language and culture. The semantic representations of language and cultural differences can prove expressive enough in making judgements when decisions cross geographic boundaries. The vision is to utilize a hybrid of both the syntactic (deep learning) and the symbolic (knowledge graphs) approaches to modeling behaviour. This paper tries to lay forward the current status and future research directions for the vision of intertwining ethnolinguistics and knowledge bases for the intelligent autonomous systems (IAS).
Money Transfer on Transaction Signature-Based Ledger

Money transfer is indispensable in our daily lives. For providing the services, financial institutions bear great costs for verifying customers’ identity and behaviors, so called KYC (Know Your Customer) costs. This paper proposes a comprehensive transaction scheme to reduce the KYC costs, by sharing customers’ information among multiple financial service providers. Once a trusted service provider identifies a user using an image of his/her physical ID such as passports and residence certificates, the provider issues a digital certificate of the user’s public key. When the user opens another account at different providers, the providers can identify the user with the certificate without proceeding the KYC step. The security analysis shows that the proposed transaction scheme is resistant even to harsh attacks in a network represented by Man-in-the-Middle (MITM) attacks, as long as the user’s physical ID is tied to his/her public key appropriately. The performance analysis shows that the proposed scheme is applicable in terms of computation time and storage space.
TADRP: Toward Thermal-Aware Data Replica Placement in Data-Intensive Data Centers

With the mushrooming growth of data volumes, data replica placement plays a key role in promoting the energy efficiency and Quality-of-Service (QoS) of data-intensive data centers. The existing data placement strategies mainly focus on storage performance improvement or QoS enhancement in data centers, but ignore the indispensable factor -heat recirculation. To bridge this gap, we propose a thermal-aware data replica placement strategy called TADRP, aiming to improve cooling efficiency and minimize the total power consumption of data-intensive data centers. TADRP leverages an ant colony optimization (ACO) algorithm coupled with Laplacian probability distribution to find a quasi-optimal disk sequence (or Disk Sequence for short), which consists of disks selected from different rack servers to place data replicas. TADRP categorizes disks of Disk Sequence into active and inactive ones, by placing hot and cold replicas on active and inactive disks, respectively. We quantitatively evaluate TADRP in terms of cooling costs, total power consumption, number of power-state transitions, and execution time. We compare TADRP with four alternative solutions, namely, Random, Hadoop, SRS, and CDP-NSGAII. Experimental results show that TADRP can reduce the cooling costs and the total power of the existing solutions by 14.7% -61.7% and 19.2 %-55.1%, respectively, without undesirable I/O performance drops.
Development of Intelligent Garbage Classification System Based on WeChat Applets

The garbage classification policy has introduced new management regulations in many cities. Violators will be punished with fines, and the classification management is stricter than before. People are often at a loss when standing next to “four color trash cans”, so it is necessary to lead to random delivery and improve the accuracy of garbage classification in the era of “smart cities” as to help people easily solve the problem of garbage classification. The system is implemented based on WeChat applet. The front-end of the system uses the development framework of applet WXML + WXSS + JS + JSON, the background uses cloud development for management, and uses fuzzy query and call API technology to achieve online search, photo identification and other functions. Take photos on the spot, and the accuracy rate of picture recognition reaches 80%, which enables convenient and quick intelligent query through direct mobile phone inquiry. In addition, users can also increase the knowledge of citizens by viewing garbage dictionaries, knowledge questions and answers, etc. In addition, the applet can be customized and developed according to the classification management of each place. The development of the system helps to improve the citizens’ awareness of garbage classification and the correct rate of garbage placement, and provides a reference value for the development of garbage classification management in local communities.
Two-Layer Architecture for Signature-Based Attacks Detection over Encrypted Network Traffic

The rapid development of network function virtualization (NFV) technology on a large scale and the explosive growth of network traffic in enterprises has made it necessary to move to the paradigm of middlebox services (MB) in the cloud. Intrusion detection system (IDS) is one of these middlebox services that needs to be deployed in the cloud. However, with the growth of network attacks, redirecting enterprise traffic to external middleboxes inevitably raises new concerns related to packet content security and unauthorized access to the ruleset used for detection. To address these concerns, many research efforts targeted the design and development of IDS that operate over encrypted traffic (secure IDS) by looking for ways to make matching possible over encrypted data (aka secure/encrypted pattern matching) without any leakage while maintaining the same level of efficiency. However, most of the existing designs are communication inefficient and too slow to be deployed to support 5G network traffic that requires high throughput. Furthermore, the majority of real network traffic is legitimate and needs to be filtered quickly. Therefore, in order to improve the inspection delay, we propose in this paper a fast and efficient secure IDS that performs detection over encrypted network traffic based on the Searchable Encryption (SE) class of methods using a two-layer architecture in which the first layer is used to quickly filter out the majority of legitimate traffic and the second layer is used to further inspect only unfiltered malicious traffic. We implemented our solution and a recent powerful secure IDS and showed how our approach provides better results and outperforms it.
MECaaS: Mobile Edge Computing as a Services Based on Blockchain

As a new generation of computing paradigm for Internet of things (IoT), edge computing has been proved to be promising in solving the problems of insufficient centralized computing power, bandwidth congestion and transmission delay by distributing traditional cloud resources down to edges. While so, massive of computing resources have not been utilized due to the physical heterogeneity and allocation issues. Addressing on the two major challenges, in this paper, we propose a blockchain-based edge computing solution (MECaaS), which integrates smart contract, component interface, distributed allocation algorithm and other elements as a service. Comparing with the existing blockchain-edge computing solutions, we focus more on technical implementation than just the theoretical framework, by distributing this service to inspire devices’ self-potential in interaction, perception and decision, in further forms a distributed intelligent system. The service is deployed in a heterogeneous IoT scenario, and proved to be efficient for edge computing. Also, experimental results show that distributed resource allocation strategies perform better than centralized ways to some extent.
Proposal of fully augmented complex-valued neural networks

In this paper, we propose a complex-valued neural network based on the widely linear estimation taking advantage of its geometric structure, and show that it can learn geometric transformations that the conventional neural networks cannot. First we formulate a fully augmented complex-valued neuron model based on the widely linear estimation. It is a generalized complex-valued neuron model that includes a usual complex-valued neuron and a degenerated case called a degenerated fully augmented complex-valued neuron. A fully augmented complex-valued neural network consists of such fully augmented complex-valued neurons. Secondly, we derive the back-propagation learning algorithms for the multi-layered fully augmented complex-valued neural networks. Finally, we find out via experiments that the multi-layered fully augmented complex-valued neural network has the different ability to learn 2D affine transformation from that of the usual complex-valued neural network.
Variant Wasserstein Generative Adversarial Network Applied on Low Dose CT Image Denoising

Computed Tomography (CT) images have been extensively employed in disease diagnosis and treatment, causing a huge concern over the dose of radiation to which patients are exposed. Increasing the radiation dose to get a better image may lead to the development of genetic disorders and cancer in the patients; on the other hand, decreasing it by using a Low-Dose CT (LDCT) image may cause more noise and increased artifacts, which can compromise the diagnosis. So, image reconstruction from LDCT image data is necessary to improve radiologists’ judgment and confidence. This study proposed three novel models for denoising LDCT images based on Wasserstein Generative Adversarial Network (WGAN). They were incorporated with different loss functions, including Visual Geometry Group (VGG), Structural Similarity Loss (SSIM), and Structurally Sensitive Loss (SSL), to reduce noise and preserve important information on LDCT images and investigate the effect of different types of loss functions. Furthermore, experiments have been conducted on the Graphical Processing Unit (GPU) and Central Processing Unit (CPU) to compare the performance of the proposed models. The results demonstrated that images from the proposed WGAN-SSIM, WGAN-VGG-SSIM, and WGAN-VGG-SSL were denoised better than those from state-of-the-art models (WGAN, WGAN-VGG, and SMGAN) and converged to a stable equilibrium compared with WGAN and WGAN-VGG. The proposed models are effective in reducing noise, suppressing artifacts, and maintaining informative structure and texture details, especially WGAN-VGG-SSL which achieved a high peak-signal-to-noise ratio (PNSR) on both GPU (26.1336) and CPU (25.8270). The average accuracy of WGAN-VGG-SSL outperformed that of the state-of-the-art methods by 1 percent. Experiments prove that the WGAN-VGG-SSL is more stable than the other models on both GPU and CPU.
Development of Industry-University-Research Institute Collaborative Innovation Information Platform Based on Spring MVC Framework

Spring MVC framework is actually the Spring Web MVC module in the Spring Framework to provide greater security and scalability to the system users. Spring MVC framework is designed around DispatcherServlet, similar to the function of Struts ActionServle, the whole process of user requests to processing, effectively solving the complexities of application development. This paper analyses the principle of Spring MVC framework, constructs a three-dimensional model of Industry-University-Research Collaborative Innovation, designs the system functions through the use case diagram of Industry-University- Research Collaborative Innovation Information Platform, and realizes the Industry-University-Research Collaborative Innovation Information Platform based on Spring MVC framework. The research results provide guarantee for universities, research institutions and enterprises to carry out industry-university-research activities.
Privacy and authenticity of drone communication using chacha20 algorithm

The widespread use of unmanned aerial vehicles (also known as drones) has raised security and safety concerns. To counteract the security risks posed by illegal drone flights, an authentication, and security system based on the chaotic maps and stream cipher lightweight chacha20 algorithm is proposed. In addition to security reasons, the proposed system is specifically designed to decrease the consumption of drone resources, minimize the computational time, deal with one ground control station (GCS). The process of the suggested system firstly generates a flight session key using One DimensionChebyshev chaotic map and registers the flight session key and its flight plan into a centralized database. Then ciphering payload data using lightweight chacha20 to increase security of MAVLink communication protocol and send it to GCS. Finally, a GCS checks authentication of the current flight session based on a flight session key and its flight plan as the message authentication code key for every flight session. The experiment results using the National Institute of Standards and Technology (NIST) test prove the proposed key generation method can produce unique and random fly session key. Results of the proposed system are done on two different types of data (colored image and text) with different sizes. The best values of encrypted image: Mean Square Error MSE=9036.119576; Peak signal-to-noise ratio (PSNR)=8.570983915; Normalized Cross-Correlation (NCC)= 0.530654201,and Universal Quality Image (UQI)= 0.508969269. In terms of speed, the chacha20 algorithm is fast in ciphering execution time (worst time=1.4 msec).