Reviewerly/exHarmony huggingface dataset:
Unscented Kalman Filter for INS/GNSS Data Fusion with Time Delay

This paper presents an estimation scheme for an unmanned aerial vehicle (UAV) operating around a Navy research vessel in ocean environments. Estimating the accurate position of a UAV relative to a ship is critical for airborne measurements of ship air wake and autonomous close-proximity flights. However, the position measurements from a low-cost real-time kinematics (RTK) GPS system or vision-based localization often suffer from time-delays caused by communication overload or associated computation. In this paper, an unscented Kalman filter is developed to integrate time-delayed relative position measurements with non-delayed inertial measurements to accurately estimate the position and the attitude of a UAV relative to a ship. The proposed approach is composed of correction with the delayed measurement and forward-propagation to the current time. The efficacy of the proposed scheme is illustrated by a numerical example.
Sensitivity Context Aware Privacy Preserving Disease Prediction

In today’s world, healthcare has become one of the prominent domains of the real-world because it is associated with individuals looking after or retainment of health through disease prevention by early diagnosis, proper treatment, on-time recovery, relief from disease, a period of sickness, and injury. The diagnosis is treated as the major aspect of healthcare because the right diagnosis of disease at the right time avoids the individual from accidental death or helps to perform early-stage chronic disease detection and avoids further damage. The disease prediction helps to determine the existence of disease at earlier stages using the disease symptoms and patient personal details. The disease prediction is mainly achieved by a category of data analytics called predictive analytics, which makes use of patient disease symptoms and personal details to perform disease prediction. The patient privacy breach is treated as one of the critical issues related to disease prediction because of the usage of patient personal details. To preserve the patient privacy, the data used to perform prediction is altered using privacy-preserving techniques but it leads to another issue called data utility; i.e., what extent modified data become useful for predictive analytics after applying privacy-preserving operations. Hence, there is a tradeoff between prediction data privacy and utility. The existing privacy-preserving approaches mainly focus on preserving privacy and result in loss of data usefulness. Our proposed approach called sensitivity context aware privacy preserving disease prediction mainly deals with how to preserve the privacy of data and meantime retain its usefulness while performing disease prediction.
Sequential coding patterns: How to use them effectively in code recommendation

Some programming constructs frequently appear together in different parts of the code, representing sequential coding patterns throughout the project. These sequential coding patterns can be mined from the project repository and, whenever the code a developer is writing coincides with the beginning of a sequential pattern, the remainder of this pattern can be suggested to the developer. This is equivalent to the usual Code Completion, which suggests syntactic structures based on the line being programmed. However, instead of providing syntactic suggestions for completing the current line, such feature suggests code snippets containing multiple lines. This paper contributes with an in-depth study on how code pattern recommendation can be used effectively. We answer three research questions through a quantitative study using a robust experimental infrastructure with a corpus of five open-source projects: (1) "In a code recommendation, how many frequent coding patterns should be presented?", (2) "What is the impact of filtering sequential patterns by their confidence?", and (3) "Does the effectiveness of the sequential coding patterns degrade over time?". Our study shows that it is possible to achieve correctness above 80% when using suggestions with the highest confidence values and that a threshold confidence of 30% generally provides better outcomes. Furthermore, it shows that frequent code pattern completion effectiveness tends to degrade 50 commits after the patterns have been mined. We could observe that: (1) the top five ranked suggestions are the ones that deliver the best results; (2) the code recommendations that deliver the best results are the ones with the highest confidence values; and (3) the code recommendation performance degrades as the source code evolves because patterns become outdated.
A Quantale of Information

Information flow properties are the semantic cornerstone of a wide range of program transformations, program analyses, and security properties. The variety of information that can be transmitted from inputs to outputs in a deterministic system can be captured by representing information as equivalence relations over the sets of possible values, using an equivalence relation on the input domain to model what may be learned, and an equivalence relation on the output to model what may be observed. The set of equivalence relations over a given set of values form a lattice, where the partial order models containment of information, and lattice join models the effect of combining information. This elegant and general structure is sometimes referred to as the lattice of information.In this paper we identify an abstraction of information flow which has not been studied previously, namely disjunctive dependency (depending on x or y, as distinct from depending on both x and y). We argue that this refines the space of semantic models for dependency in a way which is both interesting in its own right and which has applications in security settings of practical interest (in particular, where so-called “Chinese wall policies” are in effect).To model disjunctive dependency we introduce a nontrivial generalisation of the lattice of information in the form of a richer structure, built on sets of equivalence relations closed under a novel condition called tiling-closure. This structure forms a quantale - a lattice equipped with a tensor operation - in which lattice join corresponds to disjunctive combination of information, and tensor corresponds to conjunctive combination. Using this we generalise the definition of information flow properties, and show that the definition has the key properties needed to support compositional reasoning about programs.
Performance analysis of encryption and decryption algorithm

&lt;p class="Abstract"&gt;In this tumultuous 21st century, we are surrounding by lots of applications such as social media websites all over the internet or this era can also define as digital era in which everything is accessible over the internet. There are billions of internet users all over the world and they share their information over the same and because of this lots of people intentionally trying to steal the confidential data of other people, so it is always advisable to share and store data in encrypted form. In this paper, we discuss different encryption and decryption algorithms and compare them with respect to time take by these algorithms for encrypting and decrypting different sizes of files.&lt;/p&gt;
User-oriented Design of Virtual Reality Supported Learning Factory Trainings: Methodology for the Generation of Suitable Design Elements

The concept of the physical learning factory's concept includes numerous requirements to meet existing learning theory principles. Nevertheless, despite the versatile learning factory concepts available today, the high degree of complexity of the industrial production environment makes it challenging to transfer the competencies learned into the operational application situation. With Virtual Reality, training participants have the opportunity to learn in physical learning environments with transfer-oriented action tasks in virtual space directly after the training. The learning process can be personalized and adapted in the virtual learning environment. Each training participant can individually determine elements of the learning situation. For example, training participants choose the entire learning environment adapted to their unique real production environment. Virtual Reality enables new forms of reflection, e.g. recording the learning process and the associated actions. However, this sets new requirements in the context of design, which creates different challenges in Virtual Reality compared to the physical concept. The research project PortaL pursues a two-stage procedure to tailor the virtual learning environment to users in the best possible way. A pre-selection of potential design elements was presented to various stakeholders and evaluated by them in a Delphi study regarding their respective suitability for representing different aspects. A list of rated design elements resulted from this evaluation. This paper shows the methodology which was used for the design element identification and evaluation.
High Quality Facial Data Synthesis and Fusion for 3D Low-quality Face Recognition

3D face recognition (FR) is a popular topic in computer vision, since 3D face data is invariant to pose and illumination condition changes which easily affect the performance of 2D FR. Though many 3D solutions have achieved impressive performances on public high-quality 3D face databases, few works concentrate on low-quality 3D FR. As the quality of 3D face acquired by widely used low-cost RGB-D sensors is really low, more robust methods are required to achieve satisfying performance on these 3D face data. To address this issue, we propose a novel two-stage pipeline to improve the performance of 3D FR. In the first stage, we utilize pix2pix network to restore the quality of low-quality face. In the second stage, we launch a multi-quality fusion network (MQFNet) to fuse the features from different qualities and enhance FR performance. Our proposed network achieves the state-of-the-art performance on the Lock3DFace database. Furthermore, extensive controlled experiments are conducted to demonstrate the effectiveness of each model of our network.
Interpretable Locally Adaptive Nearest Neighbors

When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric. This metric is global. We extend this idea and, for the widely used family of k nearest neighbors algorithms, develop a method that allows learning locally adaptive metrics. These local metrics not only improve performance, but are naturally interpretable. To demonstrate important aspects of how our approach works, we conduct a number of experiments on synthetic data sets, and we show its usefulness on real-world benchmark data sets.
AI based e-Testing as a common yardstick for measuring human abilities

It is difficult to evaluate a person's ability or performances because there is no common yardstick for measuring individual human abilities. For example, in general, paper based tests have different characteristics (difficulties, accuracies, and so on) because they depend on items of each test. Recently, e-testing technologies from AI approach, for which each test form has equivalent measurement accuracy but with a different set of items, have become popular. Even if different examinees with the same ability take different tests, their test scores should be guaranteed to be equivalent. We introduce state-of the-art e-testing technologies from AI approach.
Modern methods of detecting single photons and their application in quantum communications

Abstract We consider main methods for detecting single photons used in quantum communications, including the quantum key distribution (QKD) technology. Two most promising single photon detectors (SPDs) based on superconducting nanowires and on a single-photon avalanche diode are described. The most effective SPD designs are presented and their advantages and disadvantages are analysed from the point of view of the possibility of their use in QKD devices. The results of the work of various scientific groups conducting research on QKD are discussed, which makes it possible to trace the trends in the global technological development of this industry over the past five years.
Futuristic Hybrid Image Enhancement Using Fuzzy and Cubic Interpolation Methods

Contrast enhancement is a significant image processing operation to improve the images taken with a poor camera or poor environmental conditions. Contrast enhancement can be completed through a spatial domain by mapping the original intensity level to an enhanced intensity level. However, most methods are based on applying complex mathematical interpolation to find the enhanced intensity level. We propose a hybrid fuzzy logic-based intensity mapping along with cubic interpolation method to find the best intensity level mapping. The fuzzy interpolation reduces complexity and enhances image intensity as a local optimization method, while cubic interpolation smooths the image as a global optimization method. We run several experiments using a benchmark dataset, and we show an enhancement of our integrated model with more than 52% over spline interpolation and 8% over fuzzy interpolation.
Policy Gradient-based Deep Reinforcement Learning for Deadline-aware Transfer over Wide Area Networks

Deadline-aware job scheduling problems have been attracting attention in the application domains of scientific workflows and commercial data-center wide area networks. Satisfying all of the demands before the corresponding deadlines requires optimally spreading traffic along future times. In this paper, we propose a novel deadline-aware job scheduling method that leverages a deep reinforcement learning policy gradient algorithm. We obtain the following findings through several experiments on evaluating the proposed method. The EDF-trained initial policy with the proposed method outperforms a random initial policy. We evaluate how the training period influence the performance of the proposed method and the relationship between the training period length and the success ratio. We also demonstrate the proposed method outperformed with other job-scheduling methods, i.e., round-robin, first-come-first-served, and earliest-due-first, with the gaps between these methods and the proposed method expanding as the traffic load increased.
Planning and Reporting Method Validation Studies Print Email. A Supplement to the Eurachem Guide «The Fitness for Purpose of Analytical Methods»

Ukrainian translation of The Fitness for Purpose of Analytical Methods - A Laboratory Guide to Method Validation and Related Topics (2nd ed.), which was published in 2014, is offered. Since Document publication the Method Validation Working Group has identified areas where extra guidance would be appropriate. This extra guidance has been prepared in the form of supplementary documents, which should be used in conjunction with the Guide. Original Document is published on the Euracem website. https://www.eurachem.org/index.php/publications/guides/planning-validation-studies
Blockchain Infrastructure with Fragments Dependent Security Deposit Solution

Abstract Blockchain is considered a shared ledger technology to be a viable alternative for heavily focused control process applications. However, most of the current blockchain networks are used for the proof of work called the Hash-Puzzle-Solving Consensus Protocol. The competition to solve the puzzle leads to high latency, leading directly to a lengthy cycle of transaction processing. One solution to this problem is the establishment of a shards blockchain network. In this article, we concentrate on the blockchain network and follow a consensus protocol on security deposits to explore the issue of how to reconcile the economic reward and incentive. The knowledge asymmetric dilemma between beacon and users is also posed by the intrinsic features of the blockchain, i.e. transparency and decentralization. The philosophy of the contract is used to describe the dilemma. The optimum incentives for the various types of validators as well as the required deposits will also be obtained. The flexible deposits may provide ample economic stimulus for participants relative to fixed deposits without sacrificing the safety incentives. Moreover, the results of simulation suggest that the contract theory strategy will optimize the effectiveness of the beacon chain and fulfil the participants’ reward compatibility and individual rationality.
Wooden pallet image segmentation based on Otsu and marker watershed

Abstract Pallet detection is the key step of cargo handling for warehouse robots. In the visual detection, pallet needs to be segmented from the image background. In order to increase the recognition rate and reduce the influence of the surrounding environment and the pattern of the pallet, Otsu algorithm and marker watershed algorithm are combined to realize the image segmentation of the pallet contour. Based on the difference of colour information between the pallet and the background, Otsu algorithm is used to segment the pallet for the first time, and marker watershed is used to complete the target recognition. The experimental results show that the method can effectively solve the problem of over segmentation of the pallet object, and improve the recognition rate, which has some reference value for the design of the visual detection system of the warehouse robot wooden pallet.
Supercomputing in Python With Legate

Legate is a recently developed software system for constructing scalable simulation and data analysis programs using convenient, familiar notation. It demonstrates how coupling Python, with a runtime system originally designed for high-performance computing, can enable the creation of libraries that mimic the familiar interface of NumPy and Pandas for execution on both desktops and supercomputers.
Designing a Gender-Inclusive Conversational Agent For Pair Programming: An Empirical Investigation

Recently, research has shown that replacing a human with an agent in a pair programming context can bring similar benefits such as increased code quality, productivity, self-efficacy, and knowledge transfer as it does with a human. However, to create a gender-inclusive agent, we need to understand the communication styles between human-human and human-agent pairs. To investigate the communication styles, we conducted gender-balanced studies with human-human pairs in a remote lab setting with 18 programmers and human-agent pairs using Wizard-of-Oz methodology with 14 programmers. Our quantitative and qualitative analysis of the communication styles between the two studies showed that humans were more comfortable asking questions to an agent and interacting with it than other humans. We also found men participants showed less uncertainty and trusted agent solutions more, while women participants used more instructions and apologized less to an agent. Our research results confirm the feasibility of creating gender-inclusive conversational agents for programming.
Hierarchical Visual Concept Interpretation for Medical Image Classification

Most state-of-the-art local interpretation methods explain the behavior of deep learning classification models by assigning importance scores to image pixels based on how influential each pixel was towards the final decision. These interpretations are unable to provide further details to aid understanding of a complex concept in a domain such as medicine. We propose a novel Hierarchical Visual Concept (HVC) interpretation framework for CNN-based image classification models. As an explanation of the classification decision of a given image, HVC presents a concept hierarchy of most relevant visual concepts at multiple semantic levels. These concepts are automatically learned during training such that the lower-level concepts in the hierarchy support the corresponding higher-level concepts. Our quantitative and qualitative evaluation of the interpretation of VGG16 and ResNet50 classifiers on public and private colonoscopy image datasets shows very promising results.
Memory Deduplication as a Protective Factor in Virtualized Systems

We introduce a method for protection against a side-channel attack made possible by the use of a cloud-computing feature called memory deduplication. Memory deduplication improves the efficiency with which physical memory is used by the virtual machines (VMs) running on the same server by keeping in memory only one copy of the libraries and other software used by multiple VMs. However, this allows an attacker’s VM to find out the memory locations (and thus the operations) used by a victim’s VM, as these locations are cached and can be accessed faster than memory locations not used by the victim. To perform the attack, the malicious VM needs to execute an abnormal sequence of cache flushes, and our new method detects this by monitoring memory locations associated with sensitive (e.g., encryption) operations and using logistic regression to identify the abnormal cached operations. Furthermore, by using its own cache flushing, our method disrupts the side channel, making it more difficult for the attacker to acquire useful information. The experiments we ran using the KVM hypervisor and Ubuntu 18.04 LTS VMs on both Debian 10 and CentOS physical servers show that our method can detect attacks with 99% accuracy, and can feed fake information to an attacker with between 2–8% CPU overheads.
Preserving Cross-Linguality of Pre-trained Models via Continual Learning

Recently, fine-tuning pre-trained language models (e.g., multilingual BERT) to downstream cross-lingual tasks has shown promising results.However, the fine-tuning process inevitably changes the parameters of the pretrained model and weakens its cross-lingual ability, which leads to sub-optimal performance.To alleviate this problem, we leverage continual learning to preserve the original cross-lingual ability of the pre-trained model when we fine-tune it to downstream tasks.The experimental result shows that our fine-tuning methods can better preserve the cross-lingual ability of the pre-trained model in a sentence retrieval task.Our methods also achieve better performance than other fine-tuning baselines on the zero-shot cross-lingual part-of-speech tagging and named entity recognition tasks.
Awareness to Deepfake: A resistance mechanism to Deepfake

The goal of this study is to find whether exposure to Deepfake videos makes people better at detecting Deepfake videos and whether it is a better strategy against fighting Deepfake. For this study a group of people from Bangladesh has volunteered. This group were exposed to a number of Deepfake videos and asked subsequent questions to verify improvement on their level of awareness and detection in context of Deepfake videos. This study has been performed in two phases, where second phase was performed to validate any generalization. The fake videos are tailored for the specific audience and where suited, are created from scratch. Finally, the results are analyzed, and the study’s goals are inferred from the obtained data.
On the Generalized Covering Radii of Reed-Muller Codes

We study generalized covering radii, a fundamental property of linear codes that characterizes the trade-off between storage, latency, and access in linear data-query protocols such as PIR. We prove lower and upper bounds on the generalized covering radii of Reed-Muller codes, as well as finding their exact value in certain extreme cases. With the application to linear data-query protocols in mind, we also construct a covering algorithm that gets as input a set of points in space, and find a corresponding set of codewords from the Reed-Muller code that are jointly not farther away from the input than the upper bound on the generalized covering radius of the code. We prove that the algorithm runs in time that is polynomial in the code parameters.