Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Clustering Techniques Applied on Covid-19 Scientific Literature

Due to the current emergency situation, caused by COVID-19, the scientific literature on the topic has rapidly grown.At the same time, purposeful and targeted research plans with strong background knowledge is urgently needed.However, the huge number of documents produced by multiple communities generates a fragmented terminology that may cause confusion in information retrieval.To this aim, in a comparative study, we test different techniques to efficiently cluster these publications for improving their level of findability.
Plug-and-Play Priors Enabled SAR Image Inpainting in the Presence of Speckle Noise

Synthetic aperture radar (SAR), being a coherent imaging system, usually produces images that are affected by granular deformities known as speckle. Image restoration from such noisy observation is an ill-posed problem. Model-based optimization is the framework that effectively tackle such inverse problems by building the degradation model and utilizing the prior information. The modular structure of alternating direction method of multipliers (ADMM) converges to solution by iteratively minimizing the cost function, which is the sum of the above two models. Recently, Plug-and-Play (PnP) ADMM is developed, which provides the flexibility to use image denoisers in place of regularizers. Image estimation from partial/incomplete observation is quite challenging and open topic of research in the literature. In this paper, SAR image reconstruction under multiplicative noise is discussed for image inpainting using PnP ADMM. Simulation results show that denoisers can be used to restore the images affected by large fractions of missing pixels.
A Novel On-demand Trust-based Access Control Framework for Resource-constrained IoT System

Access control mechanism in IoT is a challenge in itself owing to the massive scale and heterogeneity of IoT devices that are connected. The task becomes more difficult when there are resource-constrained devices in the network. Employing a resourceful trusted third party as a centralized authority for access control and storage of data is no more an ideal solution due to possible breach of privacy and single point of failure. It also hampers the scalability of the IoT system. The On-demand Trust-based Access Control(OTAC) framework proposes a distributed trust-based blockchain model optimized for access control in resource-constrained environments. OTAC offers a differential level of security and privacy on a demand basis. The hierarchical structure of OTAC framework gives it a decentralized architecture. OTAC uses a customized trust-based consensus, immune to Byzantine attack and hence gives it an edge over other blockchain-based access control schemes.
Deep Photo Scan: Semi-Supervised Learning for dealing with the real-world degradation in Smartphone Photo Scanning

Physical photographs now can be conveniently scanned by smartphones and stored forever as digital images, yet the scanned photos are not restored well. One solution is to train a supervised deep neural network on many digital images and their smartphone-scanned versions. However, it requires a high labor cost, leading to limited training data. Previous works create training pairs by simulating degradation using low-level image processing techniques. Their synthetic images are then formed with perfectly scanned photos in latent space. Even so, the real-world degradation in smartphone photo scanning remains unsolved since it is more complicated due to lens defocus, low-cost cameras, losing details via printing. Besides, locally structural misalignment still occurs in data due to distorted shapes captured in a 3-D world, reducing restoration performance and the reliability of the quantitative evaluation. To address these problems, we propose a semi-supervised Deep Photo Scan (DPScan). First, we present a way of producing real-world degradation and provide the DIV2K-SCAN dataset for smartphone-scanned photo restoration. Also, Local Alignment is proposed to reduce the minor misalignment remaining in data. Second, we simulate many different variants of the real-world degradation using low-level image transformation to gain a generalization in smartphone-scanned image properties, then train a degradation network to generalize all styles of degradation and provide pseudo-scanned photos for unscanned images as if they were scanned by a smartphone. Finally, we propose a Semi-Supervised Learning that allows our restoration network to be trained on both scanned and unscanned images, diversifying training image content. As a result, the proposed DP-Scan quantitatively and qualitatively outperforms its baseline architecture, state-of-the-art academic research, and industrial products in smartphone photo scanning.
A SAR Image Segmentation Method based on Otsu and Level Set

Synthetic Aperture Radar(SAR) [1] is an active earth observation system that can observe the earth in real time and has the ability to penetrate the earth. Therefore, the SAR system has unique advantages in disaster monitoring, environmental monitoring, ocean monitoring, resource exploration, crop yield estimation, surveying and mapping, and military applications. It plays an important role in the field of remote sensing. Research on SAR image segmentation methods for images have always been a hot point in SAR target recognition. Aiming at the problem of SAR image ground target segmentation, after analyzing Otsu and Level set image segmentation methods, this paper proposed a SAR image segmentation method based on Otsu and level set segmentation methods. Compared with traditional SAR image segmentation methods, the experiment result shows our method has a higher segmentation accuracy and operating efficiency for ground targets in SAR images.
2D-FFT Based Modulation Classification Using Deep Convolution Neural Network

Automatic modulation classification (AMC) has a wide range of applications in the military and civilian areas. In the military, it is used for the extraction of information from unknown intercepted signals and the generation of jamming signals. Civil applications include interference management and spectrum underutilization. To overcome the limitations of traditional methods like maximum likelihood (ML) and feature- based (FB), deep learning (DL) networks have been developed and are being evolved. Following this direction, a convolution neural network (CNN) based AMC method is proposed. The two dimensional Fast Fourier Transform (2D-FFT) is used as a classification feature and a less complex and efficient deep CNN model is designed to classify the modulation schemes of different orders of PSK and QAM. The developed method achieves adequate classification performance for considered five modulation schemes in the AWGN channel.
Committee Selection in DAG Distributed Ledgers and Applications

In this paper, we propose several solutions to the committee selection problem among participants of a DAG distributed ledger. Our methods are based on a ledger intrinsic reputation model that serves as a selection criterion. The main difficulty arises from the fact that the DAG ledger is a priori not totally ordered and that the participants need to reach a consensus on participants' reputation. Furthermore, we outline applications of the proposed protocols, including: (i) self-contained decentralized random number beacon; (ii) selection of oracles in smart contracts; (iii) applications in consensus protocols and sharding solutions. We conclude with a discussion on the security and liveness of the proposed protocols by modeling reputation with a Zipf law.
Feature extraction via prototype margin distance maximizing criterion for subspace learning

Locality sensitive discriminant analysis (LSDA) is an effective feature extraction algorithm, which captures the local structure and label information to build margin for classification. However, the margin of LSDA is easily affected by outliers. To overcome the limitations of LSDA, we propose a new margin-based feature extraction algorithm called prototype margin distance maximizing criterion (PMDMC). Specifically, for each class, we first find its either heterogeneous nearest marginal samples to build the between-class marginal patch. While one side does not characterize the margin. Therefore, for between-class marginal patch, we turn back to find its corresponding within-class marginal patch in the original class. With the between-class marginal patch and within-class marginal patch, we build a novel margin. Finally, we use the Fisher-like criterion to find the optimal feature subspace. In this feature subspace, the margins between different classes are enlarged by maximizing the distance between two heterogeneous marginal patches, and simultaneously minimizing the distance between the prototypes (class means) and their corresponding within-class marginal patches. The obtained margins are more discriminative than before. What's more, under this margin design, the outliers which are usually lying in margins can be pulled close into their respective homogeneous domains. From the comparative experiments performed on the ORL, Yale, and AR face databases, we prove the effectiveness of the proposed PMDMC.
Graph Neural Networks in Cheminformatics

Graph neural networks represent nowadays the most effective machine learning technology in the biochemistry domain. Learning on the huge amount of chemical data can take an important part in finding new molecules or new drugs, which is a crucial research work in cheminformatics. This work would be no more time-consuming and labor-intensive with the assistant of machine learning techniques: they are capable of both handling massive datasets and learning the hidden information from the structure of graphs. In terms of applying machine learning of graphs in chemistry, this paper discusses the explorations on the following matters. Firstly, we introduce the up-to-date study of the machine learning approaches being applied in solving cheminformatics research problems. Secondly, we present concise overviews on the original mathematical model and variants of graph neural networks and the utilization in drug discovery evaluating the performance with machine learning. We end our analysis with a critical discussion of potential research based on current literature reviews and suggestions for relevant approaches and challenges.
Scalable optimal deployment in the cloud of component-based applications using optimization modulo theory, mathematical programming and symmetry breaking

Automated deployment of component-based applications in the Cloud consists in the allocation of virtual machines (VMs) offers from various Cloud Providers such that the constraints induced by the interactions between components and by the components hardware/software requirements are satisfied and the performance objectives are optimized (e.g. costs are minimized). It can be formulated as a constraint optimization problem, hence, in principle, the optimization can be carried out automatically. In the case the set of VM offers is large (several hundreds), the computational requirement is huge, making the automatic optimization practically impossible with the current general optimization modulo theory (OMT) and mathematical programming (MP) tools. We overcame the difficulty by methodologically analyzing the particularities of the problem with the aim of identifying search space reduction methods. These are methods exploiting: (i) the symmetries of the general Cloud deployment problem, (ii) the graph representation associated to the structural constraints specific to each particular application, and (iii) their combination. An extensive experimental analysis has been conducted on four classes of real-world problems, using six symmetry breaking strategies and two types of optimization solvers. As a result, the combination of a variable reduction strategy with a column-wise symmetry breaker leads to a scalable deployment solution, when OMT is used to solve the resulting optimization problem.
Classifying Twitter Spammer based on User's Behavior using Decision Tree

Twitter is one of microblogging service that widely used by people. Its popularity invites spammers to disturb other users with a large number of spam tweets. Spammers send untrusted news, unwanted tweets to another twitter accounts to introduce a product and service, a job with high salary, promote a new website, spread advertise to generate sales that could harm other users. This paper collects a hundred accounts from non-spammer and spammer. After that, manually classified as a non-spammer and spammer. User's behavior characteristics, which could give many clues to classify spammer. This paper applies profile users as features for the machine learning to classify users as a non-spammer or spammer. This paper applies seven attributes such as the statuses count, followers count, friends count, the age of account, average tweets per day, average limits between tweets, verified user or not. Using a Decision Tree method, we could classify non-spammer and spammer. The accuracy of the classification of non-spammer and spammer is 88,235%.
Intrinsic Motivation for Deep Deterministic Policy Gradient in Multi-Agent Environments

Deep reinforcement learning (DRL) has been increasingly applied to multi-agent domains in recent years. However, the agent explores the environment randomly, resulting in low exploration efficiency and learning performance, which limits the development of multi-agent reinforcement learning. In this paper, we introduce a new intrinsic motivation mechanism, named Group Intrinsic Curiosity Module(GICM), into Multi-Agent Deep Deterministic Policy Gradient (MAdDpG). By using GICM, the policy encourages agents to pursue novel and surprising states for searching the current scenarios more comprehensively. We evaluate our approach on three different kinds of setting tasks in simulation platform provided by OpenAI. Empirical results show that our proposed approach achieves the better performance in policy convergence speed and higher success rate in completing the tasks than vanilla reinforcement learning algorithms.
An Energy-Efficient GAN Accelerator with On-chip Training for Domain Specific Optimization

Generative Adversarial Networks (GAN) consists of multiple deep neural networks cooperating and competing each other. Due to their complex architectures, training GANs requires huge computations and external memory accesses. However, retraining GANs with user-specific data is critical on mobile devices because the pretrained model outputs distorted images under user-specific conditions. This paper proposes an FPGA-based GAN training accelerator to enable energy-efficient domain specific optimization of GAN with user's local data. A SELRET(Selective-Layer Retraining) is proposed to reduce the computation by 69% through selecting layers that are effective in enhancing the quality of the output. Moreover, ROLIN (Reordering Layers for Instance Normalization) is proposed to reduce the external memory accesses of intermediate data. The design is implemented on Intel's Cyclone V, showing 51 GFLOPS peak performance with 10.04 GFLOPS/W energy-efficiency for face modification of 256×256 image.
Mindless Response or Mindful Interpretation: Examining the Effect of Message Influence on Phishing Susceptibility

Influence-based deceptive messages constantly play a critical role in email phishing attacks. However, the literature lacks adequate understanding about how phishing messages with attractive and coercive influence result in the receivers’ adverse consequences. We therefore take the perspective of mindless response and mindful interpretation to address this issue by examining comprehensive relationships among message influence, cognitive processing, and phishing susceptibility. To accomplish this, a survey approach was adopted after a simulated phishing attack was conducted in campuses. Our empirical evidence shows that both message influence and cognitive processing can lead to people being phished, and a combination of different influences can also trigger cognitive processing. This research makes contributions to the literature of information security, persuading influence, and cognitive psychology.
Effects of right or left face stimulation on self and other perception in enfacement illusion

Enfacement illusion is a visuo-tactile illusion elicited by being touched on one's own face while observing another face being touched at the same time, resulting in a change in self-face recognition. Left-face stimulation is usually preferred in enfacement studies. We investigated whether left or right face stimulation has any effect on the vividness of the illusion and if any relationship exists between emphatic abilities, personality traits and vividness of the illusion. The enfacement procedure included two stimuli: a cotton swab touching to face and a syringe approaching to face. We assessed subjective feeling of the illusion using a questionnaire and morphed face evaluations between self and other. Additionally, galvanic skin response (GSR) was measured. The results indicated that the syringe elicited higher GSR than that of touching and left face stimulation was much more sensitive in discriminating synchronous and asynchronous conditions. We found a significant relationship between self-to-other morph evaluations and GSR when the left side of the face was stimulated synchronously. However, a significant relationship was found between other-to-self evaluations and GSR when the right side of the face was stimulated asynchronously. We also noted significant correlations between vividness of the illusion and emphatic concern, extraversion and psychoticism traits.
Potentials of Machine Learning for Data analysis in IoT: A Detailed Survey

Machine Learning (ML) and Internet of Things (IoT) has become a pioneering and radical assistant in a wide range of applications and fields of data management. Currently, looking towards the rapid expansion of various trends and communication technology, it is surveyed that almost all the Sensor enabled devices are now connected with each other through the Internet. The widespread use of IoT devices and sensors produces huge amounts of raw data which also increases published data that increases the need of efficient data analysis and data processing mechanisms. The article contributes the detailed survey of different data processing and analyzing techniques of machine learning which can be applied to data generated by IoT devices to offer various services. In addition, the challenges and potential of Machine Learning (ML) for data analysis in IoT are also explored. The machine learning methods to overcome various challenges of IoT are also accessed.
Gesture Recognition using Dynamic Time Warping

This paper presents an approach to the recognition of human gestures in the context of the “Seamless” project. In the project, data collected by IoT sensors can be navigated through gesture recognition algorithms. The aim of recognizing human gestures in Seamless is to give commands through a wearable control device. The paper outlines the Seamless project and then concentrates on our approach to understand a set of gestures using Machine Learning. The employed model and the results are illustrated.
Evaluating verification awareness as a method for assessing adaptation risk

Self-integration requires a system to be self-aware and self-protecting of its functionality and communication processes to mitigate interference in accomplishing its goals. Incorporating self-protection into a framework for reasoning about compliance with critical requirements is a major challenge when the system’s operational environment may have uncertainties resulting in runtime changes. The reasoning should be over a range of impacts and tradeoffs in order for the system to immediately address an issue, even if only partially or imperfectly. Assuming that critical requirements can be formally specified and embedded as part of system self-awareness, runtime verification often involves extensive on-board resources and state explosion, with minimal explanation of results. Model-checking partially mitigates runtime verification issues by abstracting the system operations and architecture. However, validating the consistency of a model given a runtime change is generally performed external to the system and translated back to the operational environment, which can be inefficient. This paper focuses on codifying and embedding verification awareness into a system. Verification awareness is a type of self-awareness related to reasoning about compliance with critical properties at runtime when a system adaptation is needed. The premise is that an adaptation that interferes with a design-time proof process for requirement compliance increases the risk that the original proof process cannot be reused. The greater the risk to limiting proof process reuse, the higher the probability that the requirement would be violated by the adaptation. The application of Rice’s 1953 theorem to this domain indicates that determining whether a given adaptation inherently inhibits proof reuse is undecidable, suggesting the heuristic, comparative approach based on proof metadata that is part of our approach. To demonstrate our deployment of verification awareness, we predefine four adaptations that are all available to three distinct wearable simulations (hearables, stress, and insulin delivery). We capture metadata from applying automated theorem proving to wearable requirements and assess the risk among the four adaptations for limiting the proof process reuse for each of their requirements. The results show that the adaptations affect proof process reuse differently on each wearable. We evaluate our reasoning framework by embedding checkpoints for requirement compliance within the wearable code and log the execution trace of each adaptation. The logs confirm that the adaptation selected by each wearable with the lowest risk of inhibiting proof process reuse for its requirements also causes the least number of requirement failures in execution.
Automated Student Attendance Monitoring System Using Face Recognition

In a conventional attendance monitoring system, the concerned teacher takes attendance manually in a classroom. In general, it is a time-consuming and very difficult task to take attendance of a huge number of students in a short period of time and involves proxy attendance. To overcome these issues, we proposed a face recognition-based student attendance monitoring system in a classroom environment. The proposed method uses the Histogram of Oriented Gradients (HOG) as features extractor, Convolutional Neural Network (CNN) as face encoding and Support Vector Machine (SVM) as a classifier. The proposed system recognizes the face in real-time using a webcam and generates attendance report automatically without any human intervention. Our face recognition method accomplished 99.5% accuracy on Labeled Faces in the Wild (LFW) database and 97.83% accuracy in real-time inside the classroom for the case of attendance monitoring. Finally, we tested our system to validate its effectiveness.