Reviewerly/exHarmony huggingface dataset:
Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach

In the clinical diagnosis of cardiovascular diseases, left ventricle (LV) segmentation in cardiac magnetic resonance images (MRI) is an indispensable procedure for doctors. To reduce the time needed for diagnosis, we develop an automatic LV segmentation method by integrating the convolutional neural network (CNN) with the level set approach. Firstly, a CNN based myocardial central-line detection algorithm was proposed to replace the manual initialization process for traditional level set approaches. Secondly, we present a novel central-line guided level set approach (CGLS) for delineating the myocardium region. In particular, we incorporate the myocardial central-line into the level set energy formulation as a constraint term. It plays two important roles in the iterative process: restricting the zero-level contour to stay around the myocardial central-line and preserving the anatomical geometry of myocardium segmentation result. In experiments, our method yields results as below: (1) 1.74 mm and 2.06 mm in terms of epicardium and endocardium perpendicular distance on MICCAI 2009 dataset, (2) 0.955 and 0.853 in terms of LV and myocardium Dice metric at the end-diastole on ACDC MICCAI 2017 dataset. The experimental data demonstrate that our method outperforms some state-of-the-art methods and achieves a good agreement with the manual segmentation results.
uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

Human activity recognition (HAR) is an important branch of human-centered research. Advances in wearable and unobtrusive technologies offer many opportunities for HAR. While much progress has been made in HAR using wearable technology, it still remains a challenging task using unobtrusive (non-wearable) sensors. This paper investigates detection and tracking of multi-occupant HAR in a smart-home environment, using a novel low-resolution Thermal Vision Sensor (TVS). Specifically, the research presents the development and implementation of a two-step framework, consisting of a Computer Vision-based method to detect and track multiple occupants combined with Convolutional Neural Network (CNN)-based HAR. The proposed algorithm uses frame difference over consecutive frames for occupant detection, a set of morphological operations to refine identified objects, and features are extracted before applying a Kalman filter for tracking. Laterally, a 19-layer CNN architecture is used for HAR and afterward the results from both methods are fused using time interval-based sliding window. This approach is evaluated through a series of experiments based on benchmark Thermal Infrared datasets (VOT-TIR2016) and multi-occupant data collected from TVS. Results demonstrate that the proposed framework is capable of detecting and tracking 88.46% of multi-occupants with a classification accuracy of 90.99% for HAR.
Generation of two-photon hybrid-entangled <i>W</i> state with photonic qubit and time-bin via cross-Kerr nonlinearities

Herein, we present a scheme to prepare a hybrid-entangled W state that possesses the polarization states of two photons and the time-bin using cross-Kerr nonlinearities (XKNLs) and linearly optical devices. To maximize the merit of the entangled W state that can conserve entanglement against the loss of one qubit, we propose a method to prepare a two-photon hybrid-entangled W state correlated with degrees of freedom (polarization and the time-bin) via nonlinearly optical gates using XKNLs. In our scheme, the nonlinear gates consist of weak XKNLs, quantum bus beams, and photon-number-resolving measurements to realize the controlled-path gate and merging-path gate between two photons. To insert the time-bin information into photons, the time-bin encoder employs only linearly optical devices (delay lines and polarizing beam splitters). Subsequently, to evaluate the efficiency and experimental feasibility of our scheme for generating a two-photon hybrid-entangled W state, we analyzed the performance of nonlinear gates using XKNLs under the decoherence effect.
Abbreviated View of Deepfake Videos Detection Techniques

In the era of technological advances and a qualitative breakthrough in the artificial intelligence field and deep neural networks, a new age of hyper-realistic digital videos forgery called DeepFake has been born, with that new technology, it is difficult to distinguish between real videos and fake ones which are uploaded daily on various websites across the Internet. Many open-source DeepFake creation methods have risen, leading to a growing number of synthesized media clips over the internet. There are many efficient fast methods and techniques which have been designed to detect and spot such phenomenon. Background Comparison, Temporal Pattern Analysis, Eye blinking, Facial Artifacts, Mesoscopic Analysis, and Pose Estimation are some of those techniques. Some of these approaches designed to detect and identify the video forgery without any prior enlightenment concerning the videos under analysis. The primary scope of this study is to provide an abbreviate review of these methodologies of a method-comparison study that has been presented to assist the researcher's evaluation of such studies.
Phase tracking for sub-shot-noise-limited receivers

Non-conventional receivers for phase-coherent states based on non-Gaussian measurements such as photon counting surpass the sensitivity limits of shot-noise-limited coherent receivers, the quantum noise limit (QNL). These non-Gaussian receivers can have a significant impact in future coherent communication technologies. However, random phase changes in realistic communication channels, such as optical fibers, present serious challenges for extracting the information encoded in coherent states. While there are methods for correcting random phase noise with conventional heterodyne detection, phase-tracking for non-Gaussian receivers surpassing the QNL is still an open problem. Here we demonstrate phase tracking for non-Gaussian receivers to correct for time-varying phase noise while allowing for decoding beyond the QNL. The phase-tracking method performs real-time parameter estimation and correction of phase drifts using the data from the non-Gaussian discrimination measurement, without relying on phase reference pilot fields. This method enables non-Gaussian receivers to achieve higher sensitivities and rates of information transfer than ideal coherent receivers in realistic channels with time-varying phase noise. This demonstration makes sub-QNL receivers a more robust, feasible, and practical quantum technology for classical and quantum communications.
Cross-modal feature extraction and integration based RGBD saliency detection

In RGBD saliency detection research field, RGB and depth cues are generally given the same status by RGBD saliency models. However, they ignore that both modalities are significantly different in inherent attribution so that effective features cannot be drawn from depth maps. In order to address this issue, this paper proposes a novel RGBD saliency model including two key components: the contrast-guided depth feature extraction (CDFE) module and the cross-modal feature integration (CFI) module. Specifically, considering the specific properties of depth information, we first design a targeted CDFE module, which learns multi-level deep depth features by strengthening the depth contrast between foreground and background, to provide multi-level deep depth features. Then, to sufficiently and reasonably integrate multi-level cross-modal features, namely the multi-level deep RGB and depth features, we equip the saliency inference branch with the CFI module, which contains two successive steps, i.e. information enrichment and feature enhancement. Extensive experiments are conducted on five challenging RGBD datasets, and the experimental results clearly demonstrate the effectiveness and superiority of the proposed model against the state-of-the-art RGBD saliency models.
Interactive Task Learning from GUI-Grounded Natural Language Instructions and Demonstrations

We show SUGILITE, an intelligent task automation agent that can learn new tasks and relevant associated concepts interactively from the user's natural language instructions and demonstrations, using the graphical user interfaces (GUIs) of third-party mobile apps.This system provides several interesting features:(1) it allows users to teach new task procedures and concepts through verbal instructions together with demonstration of the steps of a script using GUIs; (2) it supports users in clarifying their intents for demonstrated actions using GUI-grounded verbal instructions; (3) it infers parameters of tasks and their possible values in utterances using the hierarchical structures of the underlying app GUIs; and (4) it generalizes taught concepts to different contexts and task domains.We describe the architecture of the SUGILITE system, explain the design and implementation of its key features, and show a prototype in the form of a conversational assistant on Android.
Robot path planning based on laser range finder and novel objective functions in grey wolf optimizer

Mobile robots are the robots that can move through the environment and be used in many applications, including the industrial environment, planet exploration, warehousing, and daily household chores. They can be controlled by an operator, set to do some specific jobs, or work autonomously. Robot path planning is the task of an autonomous robot to move safely from one position to another. In this paper, three new objective functions are introduced in the structure of improved grey wolf optimizer (IGWO) and improved particle swarm optimization (IPSO) for the robot path planning problems. As another part of our proposed method, a reduction of laser range finder (LRF) data is performed, and the avoidance collision approach is also introduced. Robots determine the next position by using LRF data and IGWO (IPSO) algorithms in a local approach. The initial and the goal positions are predefined for each robot. Moreover, the location of static obstacles and other robots are unknown for each robot. Finally, the experimental results of the robot path planning using IGWO are compared to different algorithms. The results indicate that the proposed method performs better in determining an optimal, short, safe, and smooth path. Also, it has less power and time consumption than other methods. All the algorithms are implemented in the V-REP robot simulator.
Fréchet sufficient dimension reduction for random objects

Summary We consider Fréchet sufficient dimension reduction with responses being complex random objects in a metric space and high-dimensional Euclidean predictors. We propose a novel approach, called the weighted inverse regression ensemble method, for linear Fréchet sufficient dimension reduction. The method is further generalized as a new operator defined on reproducing kernel Hilbert spaces for nonlinear Fréchet sufficient dimension reduction. We provide theoretical guarantees for the new method via asymptotic analysis. Intensive simulation studies verify the performance of our proposals, and we apply our methods to analyse handwritten digit data and real-world affective face data to demonstrate its use in real applications.
The PANGAEA mineralogical database

Future human missions to the surface of the Moon and Mars will involve scientific exploration requiring new support tools to enable rapid and high quality science decision-making. Here, we describe the PANGAEA (Planetary ANalogue Geological and Astrobiological Exercise for Astronauts) Mineralogical Database developed by ESA (European Space Agency): a catalog of petrographic and spectroscopic information on all currently known minerals identified on the Moon, Mars, and associated with meteorites. The catalog also includes minerals found in the analog field sites used for ESA's geology and astrobiology training course PANGAEA, to broaden the database coverage. The Mineralogical Database is composed of the Summary Catalog of Planetary Analog Minerals and of the Spectral Archive and is freely available in the public repository of ESA PANGAEA. The Summary Catalog provides essential descriptive information for each mineral, including name (based on the International Mineralogical Association recommendation), chemical formula, mineral group, surface abundance on planetary bodies, geological significance in the context of planetary exploration, number of collected VNIR and Raman spectra, likelihood of detection using different spectral methods, and bibliographic references evidencing their detection in extraterrestrial or terrestrial analog environments. The Spectral Archive provides a standard library for planetary in-situ human and robotic exploration covering Visual-Near-Infrared reflective (VNIR) and Raman spectroscopy (Raman). To populate this library, we collected VNIR and Raman spectra for mineral entries in the Summary Catalog from open-access archives and analyzed them to select the ones with the best spectral features. We also supplemented this collection with our own bespoke measurements. Additionally, we compiled the chemical compositions for all the minerals based on their empirical formula, to allow identification using the measured abundances provided by LIBS and XRF analytical instruments. When integrated into an operational support system like ESA's Electronic Fieldbook (EFB) system, the Mineralogical Database can be used as a real-time and autonomous decision support tool for sampling operations on the Moon, Mars and during astronaut geological field training. It provides both robust spectral libraries to support mineral identification from instrument outputs, and relevant contextualized information on detected minerals.
Improving multi-class Boosting-based object detection

In recent years we have witnessed significant progress in the performance of object detection in images. This advance stems from the use of rich discriminative features produced by deep models and the adoption of new training techniques. Although these techniques have been extensively used in the mainstream deep learning-based models, it is still an open issue to analyze their impact in alternative, and computationally more efficient, ensemble-based approaches. In this paper we evaluate the impact of the adoption of data augmentation, bounding box refinement and multi-scale processing in the context of multi-class Boosting-based object detection. In our experiments we show that use of these training advancements significantly improves the object detection performance.
Power Analysis Attack on a Lightweight Block Cipher GIFT

GIFT is a new lightweight block cipher with smaller area and higher efficiency, which is very suitable for the Internet of Things (IoT) devices with constrained resources. The power analysis attack is an efficient method to extract the key from the cryptographic equipment. However, it is not easy to reveal the key by means of power analysis attack, when the cipher is implemented by hardware. In this article, we present the method of power analysis attack against GIFT. Firstly, we implemented GIFT on FPGA using the SAKURA-G board. Then, we explored the impact of point of interest (POI) on power analysis attack. We proposed the method of power analysis attack against the diffusion layer of GIFT. The experimental results show that the result of power analysis attack is affected by POI, and the key can be recovered when POI is registered. We can reveal the key using correlation power analysis, when targeting the diffusion layer of GIFT.
Siloseam

Silicone is a transformative design material found within a variety of emerging HCI practices including shape-changing interfaces, soft robotics, and wearables. However, workflows for designing and fabricating silicone forms require a time-intensive mold-cast-cure pipeline that limits the experiential knowledge that can be gained from working directly with silicone. In this work, we conduct a material-centric exploration of silicone and develop designerly workflows for creating inflatable silicone bladders. We present Siloseam, a creative framework that streamlines a bladder design and fabrication process, collects tacit knowledge involved in recovering from errors, and introduces new workflows that reuse existing molds. A set of exemplar artifacts demonstrates an expanded repertoire of silicone forms that leverage various configurations of airtight seams to create playful, haptic interactions. We discuss the remaining challenges in integrating silicone with a broader range of materials and opportunities for developing designerly workflows for other mold-and-cast processes.
Performance Analysis of Various Server Hosting Techniques

Cloud Computing is growing exponentially and provides various server hosting services like Virtual Machines, Docker contain- ers and Serverless Architectures. The traditional Virtual Machines (VMs) have long-running power with computation power that appears on request and vanishes after use, right away. Containers are created on top of an operating system using docker; which also automates the deployment of applications on them. Serverless applications are event-driven cloud-based systems where de- velopment of application depends completely on combining the client side’s logic, 3rd parties’ services and cloud-hosted remote procedure calls. The Serverless Container integrates docker containers with serverless applications. Amazon Web Services(AWS) offers ECS with Fargate which hosts docker containers as serverless applications. Serverless Container-aware ARchitecture(SCAR) framework[1] provides the same functionality of hosting docker containers over AWS Lambda. Our work focuses on the perfor- mance analysis of various compute services mentioned above. Based on the results we have also offered recommendations on picking from various server hosting techniques.
Morphological Segmentation to Improve Crosslingual Word Embeddings for Low Resource Languages

Crosslingual word embeddings developed from multiple parallel corpora help in understanding the relationships between languages and improving the prediction quality of machine translation. However, in low resource languages with complex and agglutinative morphologies, inducing good-quality crosslingual embeddings becomes challenging due to the problem of complex morphological forms and rare words. This is true even for languages that share common linguistic structure. In our work, we have shown that performing a simple morphological segmentation upon the corpora prior to the generation of crosslingual word embeddings for both roots and suffixes greatly improves the prediction quality and captures semantic similarities more effectively. To exhibit this, we have chosen two related languages: Telugu and Kannada of the Dravidian language family. We have also tested our method upon a widely spoken North Indian language, Hindi, belonging to the Indo-European language family, and have observed encouraging results.
A Cross-Modal Learning Approach for Recognizing Human Actions

Since a large number of videos have sprung up, the human-centric visual understanding tasks especially human action recognition (HAR) is under great demand. The key issue of the HAR is to capture spatial-temporal collaborative representations by mining abstract and discriminative high-level features. In this article, we propose a cross-modal learning framework that mainly includes an alignment net and a fusion net to improve the performance of the HAR. First, extracted different modal information is mapped into a common subspace to align, which compensates for the spatial-temporal discrepancies. Then, the aligned features are further fused to generate complementary, correlated, and consistent representations. Finally, the learnt features are input to the classifier for recognition. The experimental results have shown that our proposed approach can outperform several state-of-the-art baseline approaches.
A Comparative Analysis of Blockchain Platforms – Bitcoin and Ethereum

The 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century is all about modernization and accepting new technologies. The Blockchain Technology is a prominent and reliable technology that is getting into almost every industry today. After it was first initiated in 2008, it has tremendously gained traction. Within a short span of 10 years, it is commendable that it has grown massively and has been easily accepted worldwide. Though the first application of this technology is cryptocurrency, blockchain technology has transcended to support more applications as well. Developers have been smart enough to study the underlying protocol and customize it according to their own requirements. Today, blockchain offers multiple functionalities like decentralization, security, transparency and democracy.Two such giants which are most popular in the blockchain world are - Bitcoin and Ethereum. The elemental difference between these two platforms of the blockchain technology is the purpose for which they have been created. In this paper, the underlying differences between these two blockchain platforms will be overviewed. Both these applications are cryptocurrencies, but the focus in this paper would be on the comparison of the the underlying concepts and their protocols. The objective of both platforms, the differences in their architectures and the consensus or agreement mechanism used between the participants would be summarized along with the scalability factors and limitations. This paper will provide a brief overview of both these platforms and a clear idea about their use in the industry today.
Segmentation of workpiece surfaces with tool marks based on high definition metrology

Tool marks on face-milled surfaces contain huge information about the manufacturing processes. Direct surface segmentation based on tool marks is in favor of surface error sources diagnoses. However, traditional surface segmentation methods are prone to over-segmentation when partition surfaces with tool marks. This paper proposes an improved segmentation approach to solve this problem. Based on surface topography measured by high definition metrology (HDM), the surface segmentation methodology mainly involves four steps: automatic subsurface selection, local thresh-holding, broken tool marks repairing and water segmentation (abbreviated as “STRW” methodology). A novel concept called “periodic degree” is proposed and used as the criteria of subsurface selection. A binary image of tool marks is created by an adaptive local threshold. Broken tool marks are identified by a distance threshold and repaired by a convex-hull based tool marks repairing algorithm. Finally, water segmentation is applied to divide the surface into different regions and each region belongs to a unique tool tooth trajectory. Three real cases from powertrain plant demonstrate the procedures of the methodology and verify the effectiveness of the proposed methodology.