Reviewerly/exHarmony huggingface dataset:
A distributed optimisation framework combining natural gradient with Hessian-free for discriminative sequence training

This paper presents a novel natural gradient and Hessian-free (NGHF) optimisation framework for neural network training that can operate efficiently in a distributed manner. It relies on the linear conjugate gradient (CG) algorithm to combine the natural gradient (NG) method with local curvature information from Hessian-free (HF). A solution to a numerical issue in CG allows effective parameter updates to be generated with far fewer CG iterations than usually used (e.g. 5-8 instead of 200). This work also presents a novel preconditioning approach to improve the progress made by individual CG iterations for models with shared parameters. Although applicable to other training losses and model structures, NGHF is investigated in this paper for lattice-based discriminative sequence training for hybrid hidden Markov model acoustic models using a standard recurrent neural network, long short-term memory, and time delay neural network models for output probability calculation. Automatic speech recognition experiments are reported on the multi-genre broadcast data set for a range of different acoustic model types. These experiments show that NGHF achieves larger word error rate reductions than standard stochastic gradient descent or Adam, while requiring orders of magnitude fewer parameter updates.
“Deep Learning based diagnosis of sickle cell anemia in human RBC”

Sickle cell disease is a type of anemia distinguish by irregular erythrocytes that cause blood stream blocking, it is a severe hematological condition that causes people to be treated regularly during their lives and may also result in death. Standard RBC have a spherical form and are compact and resilient, allowing them to travel across narrow capillaries with ease. Irregular RBC's, on the other hand, have a sickle appearance and are rigid and blunt, allowing them to get trapped in thin blood vessels. Patients will experience discomfort as a result of this, and low oxygen and exhaustion will result. In this research a Deep CNN model, to classy sickle cell disease and data augmentation technique such as flipping, zooming, height and width shift done to get much better accuracy, in this research Idb1 erythrocytes microscopic photographs of blood smears obtained from patients infected with sickle cell, and the dataset is divided into test and train for each classis which are circular, elongated and others. For the classification task five pre trained model are used which are VGG16, VVG19, ResNet50, ResNet101 and Inception V3. Proposed models' efficiency is shown by the results of the work, which offers better accuracy of the classification.
Local plastic surgery-based face recognition using convolutional neural networks

Face recognition has established itself as a widely used application in various areas of social networks such as Facebook, Instagram, Twitter, etc. However, an emerging issue that affects the performance of face recognition is facial plastic surgery, which occurs as a result of medical surgical procedure. We have addressed local plastic surgery face recognition with three different types of local facial plastic surgeries that change certain portions of the face permanently: rhinoplasty, blepharoplasty and lip augmentation. Furthermore, we have proposed local plastic surgery -based face recognition using a convolutional neural network (CNN). It automatically extracts high-level features, unlike the hand-crafted features as in state-of-the-art face recognition algorithms. The proposed nine-layer CNN model local plastic surgery -based face recognition has been evaluated on the Plastic Surgery Face Database (PSD) and the American Society of Plastic Surgeons Face Database (ASPS), which consist of presurgical face images and postsurgical face images of variable sizes. We have performed experimentation with these databases and achieved accuracy ranges from 90%–100%. From the result, CNN outperformed in terms of accuracy as compared to state-of-the-art face recognition algorithms, irrespective of image size and with increasing sizes of databases.
Graph Laplacian-based Tumor Segmentation and Denoising in Brain Magnetic Resonance Imaging

Brain tumor segmentation in Magnetic Resonance Imaging (MRI) scans provides vital information to radiologists in the diagnosis and staging of disease. However, these MRI scans are often corrupted with noise during its acquisition. Traditional approaches to this problem employ denoising which leads, in general, to edge smoothing and development of artifacts in MRI slices, thereby affecting tumor segmentation performance. In this paper, we employed graph signal processing (GSP) theory to first segment tumor core in each MRI slice using graph Laplacian followed by edge-aware denoising which is performed in synergy. The paper aims to present a novel technique to tackle the two problems of segmentation and denoising both under the GSP framework. The experimental results demonstrated on simulated and clinical brain MRI datasets, show highly competitive performance both in terms of tumor core segmentation under Dice and Sensitivity measures, and in terms of edge-aware denoising under PSNR and SSIM measures.
Dialogue State Tracking with Incremental Reasoning

Abstract Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method outperforms the state-of-the-art methods in terms of joint belief accuracy for MultiWOZ 2.1, a large-scale human--human dialogue dataset across multiple domains.
Sparse nonnegative convolution is equivalent to dense nonnegative convolution

Computing the convolution A ⋆ B of two length-n vectors A,B is an ubiquitous computational primitive, with applications in a variety of disciplines. Within theoretical computer science, applications range from string problems to Knapsack-type problems, and from 3SUM to All-Pairs Shortest Paths. These applications often come in the form of nonnegative convolution, where the entries of A,B are nonnegative integers. The classical algorithm to compute A⋆ B uses the Fast Fourier Transform (FFT) and runs in time O(n logn).
Solid state drive forensics analysis—Challenges and recommendations

Abstract Solid state drive (SSD) is rapidly replacing hard disk drive (HDD) in almost all computing devices. With the exponential growth in SSD technologies, it is quite possible that very soon HDD will become obsolete. It is good news for the end‐users, but may not be so pleasant for the digital forensics examiners. It has been a challenge for the cyber‐crime investigator ever since the evolution of SSD technology. The intrinsic characteristics of SSD are not very supportive for forensic examiners. The TRIM function and background garbage process make it difficult to retrieve deleted artifacts from the SSD. The traditional disk write blocker cannot stop the background process. There is a lot of uncertainty involved in SSD data acquisition. Sometimes it is also difficult to prove the integrity of SSD in the court of law which makes the SSD's legal admissibility questionable. The objective of this article is to examine the uncertainty involved in SSD forensics with experimental analysis. This article discusses in detail the different components of SSD and its working principle followed by experimental forensics analysis, critical observations, guidelines, and recommendations.
Videoforensicshq: Detecting High-Quality Manipulated Face Videos

There are concerns that new approaches to the synthesis of high quality face videos may be misused to manipulate videos with malicious intent. The research community therefore developed methods for the detection of modified footage and assembled benchmark datasets for this task. In this paper, we examine how the performance of forgery detectors depends on the presence of artefacts that the human eye can see. We introduce a new benchmark dataset for face video forgery detection, of unprecedented quality. It allows us to demonstrate that existing detection techniques have difficulties detecting fakes that reliably fool the human eye. We thus introduce a new family of detectors that examine combinations of spatial and temporal features and outperform existing approaches both in terms of detection accuracy and generalization.
Self-Driving Algorithm and Location Estimation Method for Small Environmental Monitoring Robot in Underground Mines

In underground mine environments where various hazards exist, such as tunnel collapse, toxic gases, the application of autonomous robots can improve the stability of exploration and efficiently perform repetitive exploratory operations. In this study, we developed a small autonomous driving robot for unmanned environmental monitoring in underground mines. The developed autonomous driving robot controls the steering according to the distance to the tunnel wall measured using the light detection and ranging sensor mounted on the robot to estimate its location by simultaneously considering the measured values of the inertial measurement unit and encoder sensors. In addition, the robot autonomously drives through the underground mine and performs environmental monitoring using the temperature/humidity, gas, and particle sensors mounted on the robot. As a result of testing the performance of the developed robot at an amethyst mine in Korea, the robot was found to be able to autonomously drive through tunnel sections with ∼28 m length, ∼2.5 m height, and∼3 m width successfully. The average error of location estimation was approximately 0.16 m. Using environmental monitoring sensors, temperature of 15–17◦C, humidity of 42%–43%, oxygen concentration of 15.6%–15.7%, and particle concentration of 0.008–0.38 mg/m3 were measured in the experimental area, and no harmful gases were detected. In addition, an environmental monitoring map could be created using the measured values of the robot’s location coordinates and environmental factors recorded during autonomous driving.
Causal and Interpretable Rules for Time Series Analysis

The number of complex infrastructures in an industrial setting is growing and is not immune to unexplained recurring events such as breakdowns or failure that can have an economic and environmental impact. To understand these phenomena, sensors have been placed on the different infrastructures to track, monitor, and control the dynamics of the systems. The causal study of these data allows predictive and prescriptive maintenance to be carried out. It helps to understand the appearance of a problem and find counterfactual outcomes to better operate and defuse the event. In this paper, we introduce a novel approach combining the case-crossover design which is used to investigate acute triggers of diseases in epidemiology, and the Apriori algorithm which is a data mining technique allowing to find relevant rules in a dataset. The resulting time series causal algorithm extracts interesting rules in our application case which is a non-linear time series dataset. In addition, a predictive rule-based algorithm demonstrates the potential of the proposed method.
CariMe: Unpaired Caricature Generation With Multiple Exaggerations

Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from generic image-to-image translation, drawing caricatures automatically is a more challenging task due to the existence of various spatial deformations. Previous caricature generation methods are obsessed with predicting definite image warping from a given photo while ignoring the intrinsic representation and distribution of geometric exaggerations in caricatures. This limits their ability on diverse exaggeration generation. In this paper, we generalize the caricature generation problem from instance-level warping prediction to distribution-level deformation modeling. Based on this assumption, we present the first exploration for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unpaired CARIcature generation with Multiple Exaggerations (CariMe)</i> . Technically, we propose a Multi-exaggeration Warper network to learn the distribution-level mapping from photos to facial exaggerations. This makes it possible to generate diverse and reasonable exaggerations from randomly sampled warp codes given one input photo. To better represent the facial exaggeration and produce fine-grained warping, a deformation-field-based warping method is also proposed, which captures more detailed exaggerations than previous point-based warping methods. Experiments and two perceptual studies prove the superiority of our method comparing with other state-of-the-art methods, showing the improvement of our work on caricature generation. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/edward3862/CariMe-pytorch</uri> .
Local indistinguishability and incompleteness of entangled orthogonal bases: Method to generate two-element locally indistinguishable ensembles

We relate the phenomenon of local indistinguishability of orthogonal states with the properties of unextendibility and uncompletability of entangled bases for bipartite and multipartite quantum systems. We prove that all two-qubit unextendible entangled bases are of size three and they cannot be perfectly distinguished by separable measurements. We identify a method of constructing two-element orthogonal ensembles, based on the concept of unextendible entangled bases, that can potentially lead to information sharing applications. Two-element ensembles form the fundamental unit of ensembles, and yet does not offer locally indistinguishable ensembles for pure state elements. Going over to mixed states does open this possibility, but can be difficult to identify. The method provided using unextendible entangled bases can be used for their systematic generation. In multipartite systems, we find a class of unextendible entangled bases for which the unextendibility property remains conserved across all bipartitions. We also identify nonlocal operations, local implementation of which require entangled resource states from a higher-dimensional quantum system.
How to select and weight context dimensions conditions for context-aware recommendation?

Contextual information plays a key role in Context-Aware Recommender Systems (CARS). The rating prediction in CARS focuses on improving recommendation accuracy attempting to form a personalized information recommendation for users. Three key problems that affect the performances of recommender systems: (i) context condition’s selection; (ii) context condition’s weighting; and (iii) users’ context conditions matching. Context-aware approaches have the assumption that all context conditions have the same weight. These approaches ignore that users have different preferences in different contexts. To address these three problems, we introduce a novel approach for Selecting, Weighting Context Conditions (SWCC) and measuring semantic similarity between users’ situations. Evaluation experiments show that the proposed approach is outperforming the pioneering context-aware recommendation approaches of the literature.
A Queueing Model for Threshold-Based Scaling of UPF Instances in 5G Core

The fifth-generation networks User Plane Function (UPF) provides necessary procedures to connect end-devices and data networks. A Protocol Data Unit (PDU) session between specific user equipment (UE) and the UPF is established before data transmission in 5G networks. The UPF software can be packed into either a virtual machine (VM) or a container image and instantiated in service providers' cloud infrastructure. Each UPF instance may handle several concurrent PDU sessions from various users. Operators should control the number of UPF instances to save resource consumption in the provision of Quality of Service (QoS). This paper presents a queueing model for a scenario where a threshold-based algorithm controls the number of UPF instances depending on users' traffic. We derive a method to compute the steady-state probabilities and performance measures efficiently. We investigate the performance of the UPF scaling algorithm in various scenarios with the assumption of specific hardware. Numerical results show that scaling UPF instances can lead to the good utilization of the system resource.
Introduction to Blockchain

Blockchain technology, which is nothing but a distributed ledger technology, has gained a lot of attention over the years because of its delegation, immutability, security, and transparency; on the other hand, the Internet of Things (IoT) is an environment consisting of interrelated computing devices having unique identifiers and can transmit data over a network which does not require human–human or human–computer interaction. IoT, because of its efficient resource utilization, helps other technologies reach their accurate and complete capability. A combination of IoT and blockchain can do wonders in various fields. It may add to accelerated data change, lower costs, improved security, and a more efficient supply chain. This chapter explains the basic working of blockchain. It includes the top five protocols and applications of blockchain in various fields. This chapter also discusses the use of blockchain in healthcare, consensus algorithms in blockchain, platforms of blockchain, IoT and Blockchain application development, and, lastly, how blockchain can be used in IoT-enabled healthcare and make it more efficient and promising.
Graph-based path decision modeling for hypersonic vehicles with no-fly zone constraints

This paper proposes a high-level path decision modeling approach of hypersonic vehicles for no-fly zones avoidance with complex distribution. The current algorithms of trajectory planning and guidance can achieve reliable convergence practically dependent on the initial trajectory. However, in the complex no-fly zones scenario under consideration, it is difficult to guarantee a priori a good initial guess, which requires the vehicle to have high-level decision-making ability to ensure trajectory's global performance. To deal with this, this paper establishes the path decision model and integrates physical level information. The path decision problem is formulated based on graph modeling by formulating new policies and rules. To the best of our knowledge, this paper is the first to study the path decision problem and model for hypersonic vehicles. The path decision approach is proposed by incorporating low-level waypoint-follow guidance simulation information in the path evaluation. This operation takes hypersonic dynamics into account so that it avoids the evaluation inconsistency between high-level decision and low-level guidance. Numerical simulation results show that this paper effectively realizes the path decision of the hypersonic vehicle for no-fly zones avoidance and improves the global performance of the trajectory.
Performance Modelling and Quantitative Analysis of Vehicular Edge Computing With Bursty Task Arrivals

The quantitative performance analysis plays a critical role in assessing the capability of Vehicular Edge Computing (VEC) systems to meet the requirements of vehicular applications.However, developing accurate analytical models for VEC systems is extremely challenging due to the unique features of intelligent vehicular applications.Specifically, recent work revealed that the tasks generated by intelligent vehicular applications exhibit a high degree of burstiness, rendering the existing models that were designed based on the assumption of the non-bursty Poisson process unsuitable for VEC systems.To fill this gap, we developed an original analytical model to investigate the performance of VEC systems with bursty task arrivals.To facilitate vehicle cooperation, a new priority-based resource allocation scheme is exploited to schedule the tasks of vehicular applications, which are modelled by a Markov Modulated Poisson Process (MMPP).Next, a multi-state Markov chain is established to investigate the impact of load sharing strategy on the performance of VEC systems.Then, the end-to-end transmission latency is derived based on the proposed model.Comprehensive experiments are conducted to validate the accuracy of this analytical model under various system configurations.Furthermore, the developed model is used as a cost-effective tool to investigate the performance bottleneck of VEC systems.
Proactive Detection of Phishing Kit Traffic

Current anti-phishing studies mainly focus on either detecting phishing pages or on identifying phishing emails sent to victims. In this paper, we propose instead to detect live attacks through the messages sent by the phishing site back to the attacker. Most phishing attacks exfiltrate the information gathered from the victim by sending an email to a “drop”, throwaway email address. We call these messages exfiltrating emails. Detecting and blocking exfiltrating emails is a new tool to protect networks in which a number of largely unmonitored websites are hosted (universities, web hosting companies etc.) and where phishing sites may be created, either directly or by compromising existing legitimate sites. Moreover, unlike most traditional antiphishing techniques which require a delay between the attack and its detection, this method is able to block the attack as soon as it starts collecting data. It is also useful for email providers who can detect the presence of drop mailbox in their service and prevent access to it. Gmail deployed a simple rule-based detection system and detected over 12 million exfiltrating emails sent to more than 19,000 drop Gmail addresses in one year [].In this work, we look at this problem from a new perspective: we use a Recurrent Neural Network to learn the structure of exfiltrating emails instead of their content. We compare our implementation, called DeepPK, against word-based and pattern-based methods, and tested their robustness against evasion techniques. Although all three models are shown to be very effective at detecting unmodified messages, DeepPK is the overall more resistant and remains quite effective even when the messages are altered to avoid detection. With DeepPK, we also introduce a new message encoding technique which facilitates scaling of the classifier and makes detection evasion harder.
HEALTH VULNERABILITY VERSUS ECONOMIC RESILIENCE TO THE COVID-19 PANDEMIC

The purpose of this study is to understand how countries have leveraged on their economic resilience to fight the COVID-19 pandemic. The focus is on a global sample of 150 countries. The study develops a health vulnerability index (HVI) and leverages on an existing economic resilience index (ERI) to provide four main scenarios from which to understand the problem statement, namely ‘low HVI-low ERI,’ ‘high HVI-low ERI,’ ‘high HVI-high ERI,’ and ‘low HVI-high ERI’ quadrants. Countries that have robustly fought the pandemic are those in the ‘low HVI-high ERI’ quadrant and, to a lesser extent, countries in the ‘low HVI-low ERI’ quadrant. Most European countries, namely one African country (Rwanda), four Asian countries (e.g., Japan, China, South Korea, and Thailand), and six American countries (e.g., United States, Canada, Uruguay, Panama, Argentina, and Costa Rica) are apparent in the ideal quadrant.