Reviewerly/exHarmony huggingface dataset:
Transfer learning based recurrent neural network algorithm for linguistic analysis

Abstract Each language is a system of understanding and skills that allow language users to work together, hypothesize, express thoughts, opinions; wishes need to be articulated. Linguistics is the research of these structures in all respects: the composition, usage, and sociology of language, in particular, are the core of linguistics. Machine learning is the research area that allows machines to learn without being specifically scheduled. In linguistics, the design of writing is understood to be a foundation for many distinct company apps and probably the most useful if incorporated with machine learning methods. Research shows that besides text tagging and algorithm training, there are major problems in the field of big data. This article provides a collaborative effort (transfer learning integrated into recurrent neural network) to analyze the distinct kinds of writing between the language's linear and noncomputational sides and to enhance granularity. In addition to this, this article creates a recurrent neural network model for learning and processing text data automatically. RNN based linguistic process is fast and cost‐effective. It analyses the text data automatically and iteratively. This is the main reason for using RNN for the linguistic process. The outcome demonstrates stronger incorporation of granularity into the language from both sides. Comparative results of machine learning algorithms are used to determine the best way to analyze and interpret the structure of the language.
MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection

Many recent semi-supervised learning (SSL) studies build teacher-student architecture and train the student net-work by the generated supervisory signal from the teacher. Data augmentation strategy plays a significant role in the SSL framework since it is hard to create a weak-strong aug-mented input pair without losing label information. Espe-cially when extending SSL to semi-supervised object de-tection (SSOD), many strong augmentation methodologies related to image geometry and interpolation-regularization are hard to utilize since they possibly hurt the location information of the bounding box in the object detection task. To address this, we introduce a simple yet effective data augmentation method, Mix/UnMix (MUM), which un-mixes feature tiles for the mixed image tiles for the SSOD framework. Our proposed method makes mixed input image tiles and reconstructs them in the feature space. Thus, MUM can enjoy the interpolation-regularization effect from non-interpolated pseudo-labels and successfully generate a meaningful weak-strong pair. Furthermore, MUM can be easily equipped on top of various SSOD methods. Exten-sive experiments on MS-COCO and PASCAL VOC datasets demonstrate the superiority of MUM by consistently im-proving the mAP performance over the baseline in all the tested SSOD benchmark protocols. The code is released at https.//github.com/JongMokKim/mix-unmix.
Secure and Privacy-aware Blockchain-based Remote Patient Monitoring System for Internet of Healthcare Things

Remote Patient Monitoring (RPM) is a form of telehealth or virtual health that strengthens online medical services and allows delivering healthcare remotely. Nowadays, remote patient monitoring systems (RPMS) are widely used by healthcare providers to remotely monitor the vital signs of patients. As the RPM field expands, concerns about efficient and secure medical data transmission are raised. This kind of patient medical data is collected by the mean of Internet of Healthcare Things (IoHT) or sometimes denoted as Internet of Medical Things (IoMT) devices. The collected data needs to be stored and retrieved with highly assured levels of security and privacy as the data comprises private and critical patients’ information. To secure medical data, this paper proposes a blockchain-based architecture to manage access control to medical data and to preserve patient’s data privacy. The blockchain-based system is built on Hyperledger Fabric, a permissioned distributed ledger solution, and the ledgers and transactions are stored in the cloud. The proposed architecture is designed to contribute to the robustness of the RPM systems and to avoid recorded security limitations in commonly used permissioned blockchains methods. Performance evaluation has proved the robustness and superiority of the proposed system in terms of data confidentiality, integrity, availability, traceability, scalability, and data privacy while integrated with the RPM services.
Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path

With the pandemic gripping the entire humanity and with uncertainty hovering like a black cloud over all our future sustainability and growth, it became almost apparent that though the development and advancement are at their peak, we are still not ready for the worst. New and better solutions need to be applied so that we will be capable of fighting these conditions. One such prospect is delivery, where everything has to be changed, and each parcel, which was passed people to people, department to department, has to be made contactless throughout with as little error as possible. Thus, the prospect of drone delivery and its importance came around with optimization of the existing system for making it useful in the prospects of delivery of important items like medicines, vaccines, etc. These modular AI-guided drones are faster, efficient, less expensive, and less power-consuming than the actual delivery.
Hope Speech Detection for Dravidian Languages Using Cross-Lingual Embeddings with Stacked Encoder Architecture

The task of hope speech detection has gained traction in the natural language processing field owing to the need for an increase in positive reinforcement online during the COVID-19 pandemic. Hope speech detection focuses on identifying texts among social media comments that could invoke positive emotions in people. Students and working adults alike posit that they experience a lot of work-induced stress further proving that there exists a need for external inspiration which in this current scenario, is mostly found online. In this paper, we propose a multilingual model, with main emphasis on Dravidian languages, to automatically detect hope speech. We have employed a stacked encoder architecture which makes use of language agnostic cross-lingual word embeddings as the dataset consists of code-mixed YouTube comments. Additionally, we have carried out an empirical analysis and tested our architecture against various traditional, transformer, and transfer learning methods. Furthermore a k-fold paired t test was conducted which corroborates that our model outperforms the other approaches. Our methodology achieved an F1-score of 0.61 and 0.85 for Tamil and Malayalam, respectively. Our methodology is quite competitive to the state-of-the-art methods. The code for our work can be found in our GitHub repository (https://github.com/arunimasundar/Hope-Speech-LT-EDI).
Voxel-level Siamese Representation Learning for Abdominal Multi-Organ Segmentation

Recent works in medical image segmentation have actively explored various deep learning architectures or objective functions to encode high-level features from volumetric data owing to limited image annotations. However, most existing approaches tend to ignore cross-volume global context and define context relations in the decision space. In this work, we propose a novel voxel-level Siamese representation learning method for abdominal multi-organ segmentation to improve representation space.The proposed method enforces voxel-wise feature relations in the representation space for leveraging limited datasets more comprehensively to achieve better performance. Inspired by recent progress in contrastive learning, we suppressed voxel-wise relations from the same class to be projected to the same point without using negative samples. Moreover, we introduce a multi-resolution context aggregation method that aggregates features from multiple hidden layers, which encodes both the global and local contexts for segmentation.Our experiments on the multi-organ dataset outperformed the existing approaches by 2% in Dice score coefficient. The qualitative visualizations of the representation spaces demonstrate that the improvements were gained primarily by a disentangled feature space.Our new representation learning method successfully encoded high-level features in the representation space by using a limited dataset, which showed superior accuracy in the medical image segmentation task compared to other contrastive loss-based methods. Moreover, our method can be easily applied to other networks without using additional parameters in the inference.
Instance-wise Occlusion and Depth Orders in Natural Scenes

In this paper, we introduce a new dataset, named InstaOrder, that can be used to understand the geometrical relationships of instances in an image. The dataset consists of 2.9M annotations of geometric orderings for class-labeled instances in 101K natural scenes. The scenes were annotated by 3,659 crowd-workers regarding (1) occlusion order that identifies occluder/occludee and (2) depth order that describes ordinal relations that consider relative distance from the camera. The dataset provides joint annotation of two kinds of orderings for the same instances, and we discover that the occlusion order and depth order are complementary. We also introduce a geometric order prediction network called InstaOrderNet, which is superior to state-of-the-art approaches. Moreover, we propose a dense depth prediction network called InstaDepthNet that uses auxiliary geometric order loss to boost the accuracy of the state-of-the-art depth prediction approach, MiDaS [54].
A study of cyber attacks: In the healthcare sector

With the advent of technologies entering into every aspect of humankind, we as human beings need to, both, protect and secure ourselves, from the privileges and imprecations of the technologies. Securing the vast interdisciplinary and interconnected networks from different sectors like, government, military, finance, academics and recently recognized as the most essential and significant sector, who almost touched every human being, the Healthcare sector, has become the most strenuous task. Thus cybersecurity, which constitutes various technologies, processes and practices, designed to protect networks, devices, programs, and data from attack, damages and unauthorized access, attributes more towards surveillance. Healthcare sectors are extensively sensible and realistic to cyber-attacks, wherein disorganization in operations or even divulgence of patient's personal information can have profound consequences. In today's electronic world, as security had become the most vital area for organizations to function uninterruptedly, thus Cyber security had become the most significant and vibrant issue to be explored and researched. Cybersecurity is referred to as computer-based technology, intentionally and accidentally disordering and falsifying the fundamental software, hardware or networked connections. Due to the substantial amalgamation of technology into the healthcare system, Cybersecurity events had become an augment challenge for the healthcare sector. The cybersecurity menace had now approached an existential hazard to the future of the health sector. To upkeep the benefits of health care technology, we must also effectually protect patient information and the essential procedures of our health care entities. Cyber-attacks incidents are growing intimidation to the health care industry in general and hospitals in particular. Cyber-attacks had caused prominent damage to the health sector, breaching into patients personal and professional information, causing millions of dollars loss, in terms of revenues and fines. In this paper, we present the challenges of cyber security in healthcare and cyber-attacks classification based on their targeted security goal. Also, we provide a review of the literature on cybersecurity issues surrounding healthcare and discuss possible solutions to mitigate data breaches and cyber-attacks.
A homogenization-planning-tracking method to solve cooperative autonomous motion control for heterogeneous carrier dispatch systems

Taxiing aircraft and towed aircraft with drawbar are two typical dispatch modes on the flight deck of aircraft carriers. In this paper, a novel hierarchical solution strategy, named as the Homogenization-Planning-Tracking (HPT) method, to solve cooperative autonomous motion control for heterogeneous carrier dispatch systems is developed. In the homogenization layer, any towed aircraft system involved in the sortie task is abstracted into a virtual taxiing aircraft. This layer transforms the heterogeneous systems into a homogeneous configuration. Then in the planning layer, a centralized optimal control problem is formulated for the homogeneous system. Compared with conducting the path planning directly with the original heterogeneous system, the homogenization layer contributes to reduce the dimension and nonlinearity of the formulated optimal control problem in the planning layer and consequently improves the robustness and efficiency of the solution process. Finally, in the tracking layer, a receding horizon controller is developed to track the reference trajectory obtained in the planning layer. To improve the tracking performance, multi-objective optimization techniques are implemented offline in advance to determine optimal weight parameters used in the tracking layer. Simulations demonstrate that smooth and collision-free cooperative trajectory can be generated efficiently in the planning phase. And robust trajectory tracking can be realized in the presence of external disturbances in the tracking phase. The developed HPT method provides a promising solution to the autonomous deck dispatch for unmanned carrier aircraft in the future.
Privacy-Preserving Biometric Matching Using Homomorphic Encryption

Biometric matching involves storing and processing sensitive user information. Maintaining the privacy of this data is thus a major challenge, and homomorphic encryption offers a possible solution. We propose a privacy-preserving biometrics-based authentication protocol based on fully homomorphic en-cryption, where the biometric sample for a user is gathered by a local device but matched against a biometric template by a remote server operating solely on encrypted data. The design ensures that 1) the user's sensitive biometric data remains private, and 2) the user and client device are securely authenticated to the server. A proof-of-concept implementation building on the TFHE library is also presented, which includes the underlying basic operations needed to execute the biometric matching. Performance results from the implementation show how complex it is to make FHE practical in this context, but it appears that, with implementation optimisations and improvements, the protocol could be used for real-world applications.
An onboarding model for integrating newcomers into agile project teams

A stable team is deemed optimal for agile software development project success; however, all teams change membership over time. Newcomers joining an agile project team must rapidly assimilate into the organisational and project environment. They must do this while learning how to contribute effectively and without seriously interrupting project progress. This paper addresses how newcomers integrate into an established agile project team and how agile practices assist with onboarding. A single, qualitative case study approach was used, investigating a co-located agile project team in a large IT department who regularly onboard inexperienced newcomers. Analysis was abductive, consisting of inductive coding and theming using categories from an existing onboarding theory. We describe the team's onboarding practices and adjustments and present an agile onboarding model that encompasses onboarding activities, individual adjustments, and workplace adjustments. A mixture of general and specific agile onboarding practices contribute to successful onboarding in an agile team. We provide practical guidelines to improve onboarding practice in agile teams. Our major new contribution is an extended model of onboarding for agile teams.
Advantages of first-derivative reflectance spectroscopy in the VNIR-SWIR for the quantification of olivine and hematite

The focus of this paper is to study the application of the first order derivative method for the estimation of minerals rates in different mineral mixtures. The primary goal with this is to find robust spectral features of specific minerals that are not severely influenced by the spectral features of the other minerals in a mixture. Results were used to select appropriate spectral features to be applied for quantifying the minerals in upcoming studies. Mixtures of different terrestrial minerals equivalent to those dominating the Martian surface with a grain size <0.25 ​mm were prepared and analyzed in the laboratory by reflectance spectroscopy in the VNIR-SWIR range. The first derivatives were computed and correlated with the mineral concentration at specific wavelengths using the Unscrambler X software. The results indicated the first derivatives near 2300 ​nm, that is a characteristic absorption feature of olivine rich in magnesium and iron silicate, correlate strongly to the olivine content for all the mixtures containing olivine, binary and ternary, with regression coefficients ranging between 0.93 and 0.98. Additionally, the main advantage of this work is that first derivative spectra of mixtures with different olivine ratio highlights in the overlapping regions of the spectra the wavelengths where the first derivative values correlate strongly to the amount of olivine in the mixtures. The region near 1050–1300 ​nm was identified as a promising one for hematite-olivine mixtures and 785–900 ​nm for magnetite olivine, with a regression coefficient mean of 0.97 and 0.98, respectively. The study of hematite-plagioclase mixtures demonstrates that wavelengths near 785–858 ​nm and 940–989 ​nm lying within the overlapping regions of hematite and plagioclase exhibit robust correlation to the hematite content with a regression coefficient mean of 0.98 for both areas.
Quantum data hiding with continuous-variable systems

Suppose we want to benchmark a quantum device held by a remote party, e.g., by testing its ability to carry out challenging quantum measurements outside of a free set of measurements $\mathcal{M}$. A very simple way to do so is to set up a binary-state discrimination task that cannot be solved efficiently by means of free measurements. If we can find pairs of orthogonal states that become arbitrarily indistinguishable under measurements in $\mathcal{M}$, in the sense that the error probability in discrimination approaches that of a random guess, we say that there are data hiding against $\mathcal{M}$. Here we investigate data hiding in the context of continuous-variable quantum systems. First, we look at the case where $\mathcal{M}$ denotes the set of measurements implementable with local operations and classical communication. While previous studies have placed upper bounds on the maximum efficiency of data hiding in terms of the local dimension and are thus not applicable to continuous-variable systems, we establish more general bounds that rely solely on the local mean photon number of the states employed. Along the way, we perform a rigorous quantitative analysis of the error introduced by the nonideal Braunstein-Kimble quantum teleportation protocol, determining how much squeezing and local detection efficiency are needed in order to teleport an arbitrary multimode local state of known mean energy with a prescribed accuracy. Finally, following a seminal proposal by Sabapathy and Winter, we look at data hiding against Gaussian operations assisted by the feedforward of measurement outcomes, providing an example of a relatively simple scheme that works with a single mode only.
A novel approach to generate augmented reality assembly assistance automatically from CAD models

Many studies have already shown where the advantages of Augmented Reality (AR) lie and how great the potential of it is. Nevertheless, the technology still lacks its industrial suitability, and until now, the generation of AR assembly assistances is highly demanding and it requires a lot of manual effort. This paper presents an approach to derive AR assembly assistances from 3D assembly models in a fully automated way. In our approach, the assembly information is derived from a CAD model, processed in an exchange format on a server and then transferred to a Microsoft HoloLens 2 with two different user interfaces.
Learning generalized strong branching for set covering, set packing, and 0–1 knapsack problems

Branching on a set of variables, rather than on a single variable, can give tighter bounds at the child nodes and can result in smaller search trees. However, selecting a good set of variables to branch on is even more challenging than selecting a good single variable to branch on. Generalized strong branching extends the strong branching concepts developed for choosing a single variable to choosing a set of variables. As the computational requirements of a full implementation of strong branching are prohibitive, we use extreme gradient boosting to train a model to predict the ranking of (sets of) candidate variables. An extensive computational study using instances from three well-known classes of optimization problems demonstrates that branching on sets of variables outperforms branching on a single variable, that a learned model can be used effectively to select among (sets of) candidate variables, and that the learned strong branching strategies outperform the default branching strategy of state-of-the-art commercial solver CPLEX in terms of both the number of nodes explored in the search tree and the time it takes to explore the search tree.
Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification

Although many efforts have been made into attack and defense on the 2D image domain in recent years, few methods explore the vulnerability of 3D models. Existing 3D attackers generally perform point-wise perturbation over point clouds, resulting in deformed structures or outliers, which is easily perceivable by humans. Moreover, their adversarial examples are generated under the white-box setting, which frequently suffers from low success rates when transferred to attack remote black-box models. In this article, we study 3D point cloud attacks from two new and challenging perspectives by proposing a novel Imperceptible Transfer Attack (ITA): 1) Imperceptibility: we constrain the perturbation direction of each point along its normal vector of the neighborhood surface, leading to generated examples with similar geometric properties and thus enhancing the imperceptibility. 2) Transferability: we develop an adversarial transformation model to generate the most harmful distortions and enforce the adversarial examples to resist it, improving their transferability to unknown black-box models. Further, we propose to train more robust black-box 3D models to defend against such ITA attacks by learning more discriminative point cloud representations. Extensive evaluations demonstrate that our ITA attack is more imperceptible and transferable than state-of-the-arts and validate the superiority of our defense strategy.