Reviewerly/exHarmony huggingface dataset:
NEWS Article Summarization with Pretrained Transformer

Pretrained language models have shown tremendous improvement in many NLP applications including text summarization. Text-to-Text transfer transformer (T5) and Bidirectional Encoder Representations from Transformers (BERT) are most recent pretrained language models applied most widely in NLP research domain. In this paper we have shown how T5 and BERT can be applied for text summarization task and can be use for both abstractive and extractive summary generation tool. Our hypothesis is that T5 performance outperform over BART and transformer developed from the scratch. To test our hypothesis used our dataset containing more than 80K news articles and their summaries. This dataset has been tested using BART, Text-to-Text transformer (T5), model generated using transfer learning over T5, and an encoder-decoder based model developed from scratch. The results show that T5 gives better result than other three models used for testing.
Sampling-Based Motion Planning for Uncertain High-Dimensional Systems via Adaptive Control

This paper considers the problem of safe motion planning for high-dimensional holonomic robots with 2nd-order uncertain dynamics. We integrate sampling-based motion planning techniques with traditional adaptive feedback control and address difficulties encountered in planning for such systems. More specifically, we develop a feedback control scheme that tracks a given reference trajectory within certain bounds, while simultaneously compensating for potential uncertainty in the dynamical parameters of the robot (masses, moments of inertia) and external disturbances. Employing this result, we are able to cast the problem of kinodynamic motion planning to a geometric one, which can be usually solved more efficiently since it does not take into account the robot dynamics (a.k.a. differential constraints). Intuitively, we use a geometric planner to obtain a high-level safe path for the robot, and a low-level adaptive feedback control algorithm to execute it while taking into account the robot dynamics. Experimental results validate the proposed approach.
Attribute-Based Access Control(ABAC) with Decentralized Identifier in the Blockchain-Based Energy Transaction Platform

With the development of ICT technology, it has also been applied to the power grid, but as a target of hackers' attack, a security solution to protect the power grid is being studied. Attribute-based Access Control (ABAC) is one of the most commonly used security solutions by providing granular and flexible access control, but it threatens privacy because it determines access control based on user attribute values. With user privacy emerging as a new topic, Decentralized Identity(DID) is emerging as a new alternative for protecting user privacy. In this paper, we propose a DID-based ABAC to solve the problem of ABAC's privacy exposure and apply it to a power transaction platform.
UCAV Path Planning Based on Improved Chaotic Particle Swarm Optimization

This paper presents an improved particle swarm optimization (PSO) algorithm based on the chaos of Zaslavskii to solve the path planning problem of Uninhabited Combat Air Vehicle (UCAV) in the threat battlefield. The proposed method uses the characteristics of traditional particle swarm optimization algorithm, such as fast convergence speed, ergodicity, randomness of chaotic motion and sensitivity to initial value. The chaos mapping formula of Zaslavskii is used to generate chaotic sequence and improves the inertia weight and random variables of PSO algorithm. The improved PSO algorithm is applied to bear on the problem of path planning of UCAV. It gradually escapes from the local optimal path and find the global optimal path in the complex and changeable battlefield environment. The simulation results show that the improved PSO algorithm can effectively accelerate the convergence speed and improve the optimal trajectory.
Virtual Reality – A Paradigm shift in Education Pedagogy

Quality and Innovation in the current pedagogical system is the core research topics in recent years for upgrading the life of next generation smart kids. Virtual reality plays a major role in bringing new trends in the education system. The current trend in educational technologies mainly focused on electronic learning and mobile based learning approach to enhance the quality of education in terms of informative and interactive learning. Virtual reality has become predominant in most of the applications such as education, healthcare, training, entertainment and so on. This research article gives the overall view of the impact of virtual reality in pedagogical field, its present and future.
Trace metals in deep-sea sediments collected from Kuril Basin (Sea of Okhotsk) and Kuril-Kamchatka Trench area

Heavy metal concentrations in deep-sea sediments collected from Kuril Basin (Sea of Okhotsk) and Kuril-Kamchatka Trench area were analyzed. For the Kuril Basin, a geochemical natural background of these elements was identified based on a dated core. The results showed enrichment by heavy metals due to natural factors in the study area. All heavy metals showed a low ecological risk; thus, the potential ecological risk of the study area was also low.
Low-Resource End-to-end Sanskrit TTS using Tacotron2, WaveGlow and Transfer Learning

End-to-end text-to-speech (TTS) systems have been developed for European languages like English and Spanish with state-of-the-art speech quality, prosody, and naturalness. However, development of end-to-end TTS for Indian languages is lagging behind in terms of quality. The challenges involved in such a task are: 1) scarcity of quality training data; 2) low efficiency during training and inference; 3) slow convergence in the case of large vocabulary size. In our work reported in this paper, we have investigated the use of fine-tuning the English-pretrained Tacotron2 model with limited Sanskrit data to synthesize natural sounding speech in Sanskrit in low resource settings. Our experiments show encouraging results, achieving an overall MOS of 3.38 from 37 evaluators with good Sanskrit spoken knowledge. This is really a very good result, considering the fact that the speech data we have used is of duration 2.5 hours only.
Route planning of intelligent bridge cranes based on an improved artificial potential field method

The artificial potential field method is usually applied to the path planning problem of driverless cars or mobile robots. For example, it has been applied for the obstacle avoidance problem of intelligent cars and the autonomous navigation system of storage robots. However, there have been few studies on its application to intelligent bridge cranes. The artificial potential field method has the advantages of being a simple algorithm with short operation times. However, it is also prone to problems of unreachable targets and local minima. Based on the analysis of the operating characteristics of bridge cranes, a two-dimensional intelligent running environment model of a bridge crane was constructed in MATLAB. According to the basic theory of the artificial potential field method, the double-layer artificial potential field method was deduced, and the path and track fuzzy processing method was proposed. These two methods were implemented in MATLAB simulations. The results showed that the improved artificial potential field method could avoid static obstacles efficiently.
Applications of Blockchain in Healthcare, Industry 4, and Cyber-Physical Systems

A Blockchain is the collection of the blocks consisting of digital assets, and these blocks with digital information connect like a chain stored through a node in a database. This paper gives an overview of Blockchain and its keywords and Blockchain-based applications in different areas. Specifically includes how Blockchain helps in the healthcare industry, prevent healthcare frauds, storing and managing Electronic Health Records (EHR), Blockchain based solution in industry 4.0 applications, Blockchain in the cyber-physical systems, Blockchain-based medical cyber-physical systems and some general forms of Blockchain. Concluding with some of the challenges in the Blockchain technology and proposed solutions.
OptSmart: a space efficient Optimistic concurrent execution of Smart contracts

Popular blockchains such as Ethereum and several others execute complex transactions in the block through user-defined scripts known as smart contracts. Serial execution of smart contract transactions/atomic units (AUs) fails to harness the multiprocessing power offered by the prevalence of multi-core processors. By adding concurrency to the execution of AUs, we can achieve better efficiency and higher throughput. In this paper, we develop a concurrent miner that proposes a block by executing AUs concurrently using optimistic Software Transactional Memory systems (STMs). It efficiently captures independent AUs in the concurrent bin and dependent AUs in the block graph (BG). Later, we propose a concurrent validator that re-executes the same AUs concurrently and deterministically using the concurrent bin followed by the BG given by the miner to verify the block. We rigorously prove the correctness of concurrent execution of AUs. The performance benchmark shows that the average speedup for the optimized concurrent miner is $$5.21 \times$$ , while the maximum is $$14.96 \times$$ over the serial miner. The optimized validator obtains an average speedup of $$8.61 \times$$ to a maximum of $$14.65 \times$$ over the serial validator. The proposed miner outperforms $$1.02 \times$$ to $$1.18\times$$ , while the proposed validator outperforms $$1 \times$$ to $$4.46 \times$$ over state-of-the-art concurrent miners and validators, respectively. Moreover, the proposed efficient BG saves an average of $$2.29 \times$$ more block space when compared with the state-of-the-art.
Incorporating lexicon and character glyph and morphological features into BiLSTM-CRF for Chinese medical NER

Chinese Medical Named Entity Recognition (CMNER) is the basic task of information processing and intelligent medical service in Chinese medical field. In order to make full use of the information of characters and words in the text to improve the effect of CMNER, this paper proposes a recognition model based on character-based BiLSTM-CRF, which integrates lexicon and character features. Firstly, in order to make full use of the information of words and word sequence in the text, the C-ExSoftword method is proposed to integrate the lexicon into the model. According to the similarity of Chinese character glyph and morphologically related forms in the field of Chinese medicine, this paper takes the four-corner coding as the character glyph features of Chinese characters, and extracts the morphological features of each word by using the improved Bidirectional Maximum Matching (BDMM) algorithm. Chinese character glyph features and morphological features are integrated into each character vector. Finally, experiments on real data sets show that the proposed model performs better than the character-based and word-based models.
Cross Hashing: Anonymizing encounters in Decentralised Contact Tracing Protocols

During the COVID-19 (SARS-CoV-2) epidemic, Contact Tracing emerged as an essential tool for managing the epidemic.App-based solutions have emerged for Contact Tracing, including a protocol designed by Apple and Google (influenced by an open-source protocol known as DP3T).This protocol contains two well-documented de-anonymisation attacks.Firstly that when someone is marked as having tested positive and their keys are made public, they can be tracked over a large geographic area for 24 hours at a time.Secondly, whilst the app requires a minimum exposure duration to register a contact, there is no cryptographic guarantee for this property.This means an adversary can scan Bluetooth networks and retrospectively find who is infected.We propose a novel "cross hashing" approach to cryptographically guarantee minimum exposure durations.We further mitigate the 24-hour data exposure of infected individuals and reduce computational time for identifying if a user has been exposed using k-Anonymous buckets of hashes and Private Set Intersection.We empirically demonstrate that this modified protocol can offer like-for-like efficacy to the existing protocol.
Experience-Based Product Inspection Planning for Industry 4.0

In this paper we describe how our Smart Virtual Product Development (SVPD) system can be used to enhance product inspection planning. The SVPD system is comprised of three main modules, these being the design knowledge management (DKM) module, the manufacturing capability and process planning (MCAPP) module, and the product inspection planning (PIP) module. Experiential knowledge relating to formal decisional events is collected, stored and used by the system in the form of set of experiences (SOEs). Here we discuss the working mechanism of the PIP module and show how experiential knowledge relating to the inspection of products that have features and functions in common can be used to enhance product inspection planning during early stages of product development. Our discussion commences with an introduction to fundamental concepts and a general system overview. We then describe the development of our SVPD system’s PIP module, and a case study we undertook for validation purposes. Results of the case study show that our system is capable of supporting product inspection planning in smart manufacturing, and thus has a vital role to play in Industry 4.0.
Annotating and detecting topics in social media forum and modelling the annotation to derive directions-a case study

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.
A Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

The secure and efficient processing of private information in the cloud computing paradigm is still an open issue. New security threats arise with the increasing volume of data into cloud storage, where cloud providers require high levels of trust, and data breaches are significant problems. Encrypting the data with conventional schemes is considered the best option to avoid security problems. However, a decryption process is necessary when the data must be processed, but it falls into the initial problem of data vulnerability. The user cannot operate on the data directly and must download it to perform the computations locally. In this context, Fully Homomorphic Encryption (FHE) is considered the holy grail of cryptography in order to solve cybersecurity problems, it allows a non-trustworthy third-party resource to blindly process encrypted information without disclosing confidential data. FHE is a valuable capability in a world of distributed computation and heterogeneous networking. In this survey, we present a comprehensive review of theoretical concepts, state-of-the-art, limitations, potential applications, and development tools in the domain of FHE. Moreover, we show the intersection of FHE and machine learning from a theoretical and a practical point of view and identify potential research directions to enrich Machine Learning as a Service, a new paradigm of cloud computing. Specifically, this paper aims to be a guide to researchers and practitioners interested in learning, applying, and extending knowledge in FHE over machine learning.
Analysis and Design of Policy and Standard Operating Procedure (SOP) for Information Technology in the Communication and Information Services Department

SOP as quality standards to perform the process of the instruction and executing tasks and activities are important for an organization. The communication and information services in West Java Province has developed a service known as Management of West Java province domain emails. SOP for service provided is to support the procedures needed for the continuity of service provided needed for the continuity of service. This research uses an observation and interview as a method to collect data. SOP will divide into 5 categories by the answer of a data interview that has been collected. This paper tries to explain what are necessary and which type of work needs to be done for writing SOPs for service provided in the communication and information services of West Java Province.
Security Framework for Internet of Things (IoT)

The Internet of things technology provides people with new experiences through the interaction between devices, people and networks. Examples include smart grid, smart health, smart home, smart workplace, e-commerce, smart industrial management, and e-governance. More and more devices are connected every day, resulting in greater security threats and problems. An extensive IoT security model is needed in order to aid resource-based IoT devices and end security. In this paper, we focus on the IoT devices applications and networks, in addition to the attack vectors and security requirements for IoT systems, as well as the organizational approach towards IoT security. We also propose a security architecture to provide security enabled IoT services, and provide a baseline for security deployment.
Economical Mobile Robot Design Prototype and Simulation for Industry 4.0 Applications

Autonomous mobile robots received a rising research interest, with the appearance of complex cyber-physical system applications, like Industry 4.0. Related scenarios require not only some degree of autonomous capabilities of the control software but also tight interaction between humans as well. In this paper, a prototype autonomous mobile robot of such functionalities is presented with additionally aiming cost-effective construction. The robot is currently capable of performing basic tasks, like traversing to a goal configuration, mapping the environment, and detecting and avoiding obstacles. To allow the versatile extension with additional cyber-physical software, the control software interfaces the Robot Operating System (ROS), a popular framework in robotic research. During the development of software and hardware, simulation has been heavily utilized. This allowed an iterative development and the use of verification in the early development phases.
A Fusion Algorithm for Saccade Eye Movement Enhancement With EOG and Lumped-Element Models

Electrooculography (EOG) can be used to measure eye movements while the eyelids are open or closed and to assist in the diagnosis of certain eye diseases. However, challenges in biosignal acquisition and processing lead to limited accuracy, limited resolution (both temporal and spatial), as well as difficulties in reducing noise and detecting artifacts. Methods such as finite impulse response, wavelet transforms, and averaging filters have been used to denoise and enhance EOG measurements. However, these filters are not specifically designed to detect saccades, and so key features (e.g., saccade amplitude) can be over-filtered and distorted as a consequence of the filtering process. Here we present a model-based fusion technique to enhance saccade features within noisy and raw EOG signals. Specifically, we focus on Westheimer (WH) and linear reciprocal (LR) eye models with a Kalman filter. EOG signals were measured using OpenBCI's Cyton Board (at 250 Hz), and these measurements were compared with a state-of-the-art EyeLink 1000 (EL; 250 Hz) eye tracker. On average, the LR model-based KF produced a 47% improvement of measurement accuracy over the bandpass filters. Thus, we conclude that our LR model-based KF outperforms standard bandpass filtering techniques in reducing noise, eliminating artifacts, and restoring missing features of saccade signatures present within EOG signals.
Hiding in Plain Sight: A Measurement and Analysis of Kids’ Exposure to Malicious URLs on YouTube

The Internet has become an essential part of children’s and adolescents’ daily life. Social media platforms are used as educational and entertainment resources on daily bases by young users, leading enormous efforts to ensure their safety when interacting with various social media platforms. In this paper, we investigate the exposure of those users to inappropriate and malicious content in comments posted on YouTube videos targeting this demographic. We collected a large-scale dataset of approximately four million records, and studied the presence of malicious and inappropriate URLs embedded in the comments posted on these videos. Our results show a worrisome number of malicious and inappropriate URLs embedded in comments available for children and young users. In particular, we observe an alarming number of inappropriate and malicious URLs, with a high chance of kids exposure, since the average number of views on videos containing such URLs is 48 million. When using such platforms, children are not only exposed to the material available in the platform, but also to the content of the URLs embedded within the comments. This highlights the importance of monitoring the URLs provided within the comments, limiting the children’s exposure to inappropriate content.
Remote Sensing Image Super-Resolution via Enhanced Back-Projection Networks

Convolutional neural network (CNN)-based image super-resolution (SR) is one of the most active field of research in the remote sensing community. As a state-of-the-art super-resolving method, however, the dense deep back-projection network (DDBPN) ignores the mutual differences among the channel-wise features and discards the initial feature when performing reconstruction. In this paper, we develop an enhanced back-projection network (EBPN) with performance exceeding the DDBPN and other state-of-the-art methods. The performance improvement gains from introducing attention mechanism to capture the feature differences among channels and reconstructing images by using the element-wise sum of the upscaled initial feature and deep features learned at different depths. A retraining strategy is also employed to further boost the SR ability of EBPN for remote sensing images. Experimental results on a remote sensing dataset and four benchmark datasets demonstrate the superiority of EBPN.