Reviewerly/exHarmony huggingface dataset:
Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications

Abstract While the practicality of secure multi-party computation (MPC) has been extensively analyzed and improved over the past decade, we are hitting the limits of efficiency with the traditional approaches of representing the computed functionalities as generic arithmetic or Boolean circuits. This work follows the design principle of identifying and constructing fast and provably-secure MPC protocols to evaluate useful high-level algebraic abstractions; thus, improving the efficiency of all applications relying on them. We present Polymath, a constant-round secure computation protocol suite for the secure evaluation of (multi-variate) polynomials of scalars and matrices, functionalities essential to numerous data-processing applications. Using precise natural precomputation and high-degree of parallelism prevalent in the modern computing environments, Polymath can make latency of secure polynomial evaluations of scalars and matrices independent of polynomial degree and matrix dimensions. We implement our protocols over the HoneyBadgerMPC library and apply it to two prominent secure computation tasks: privacy-preserving evaluation of decision trees and privacy-preserving evaluation of Markov processes. For the decision tree evaluation problem, we demonstrate the feasibility of evaluating high-depth decision tree models in a general n -party setting. For the Markov process application, we demonstrate that Poly-math can compute large powers of transition matrices with better online time and less communication.
Universal construction of genuinely entangled subspaces of any size

We put forward a simple construction of genuinely entangled subspaces – subspaces supporting only genuinely multipartite entangled states – of any permissible dimensionality for any number of parties and local dimensions. The method uses nonorthogonal product bases, which are built from totally nonsingular matrices with a certain structure. We give an explicit basis for the constructed subspaces. An immediate consequence of our result is the possibility of constructing in the general multiparty scenario genuinely multiparty entangled mixed states with ranks up to the maximal dimension of a genuinely entangled subspace.
Intercloud Resource Discovery Using Blockchain

The intercloud represents a logical evolution of cloud computing that extends its computational scale and geographic footprint by collaborating with disparate cloud service providers (CSPs) for resource sharing. Discovering resources belonging to heterogeneous CSPs is not only the primary but critical operation for the intercloud. However, achieving resource discovery in a deterministic manner within this global distributed environment is non-trivial. The literature has proposed several resource discovery approaches for the federated intercloud based on trusted and centralized third-party entities. Few approaches, however, exist for the non-federated intercloud, which by definition has no central entity to enable the resource discovery process. Some P2P-based resource discovery techniques have been proposed by researchers, industry players and standardization bodies like Global InterCloud Technology Forum (GICTF). However, existing P2P-based approaches in the non-federated intercloud do not adequately address authentication, non-repudiation of resource information, secure storage and management of transactional records, management of trust/reputation and optimal resource selection and provisioning. This research paper presents BIRD, a Blockchain-based Intercloud Resource Discovery framework that involves participating CSPs connected in a P2P network using blockchain to manage resource information and maintain transactional records. The BIRD framework alleviates the requirement of a trusted third party for discovering and managing resources. The main features involved in the BIRD framework are i) latency optimization, ii) fine-grained control mechanism, and iii) Quality-of-Service, Trust and Reputation (QTR) indices. Latency optimization achieves faster resource discovery, fine-grained control mechanism for intercloud resource discovery, and QTR is for quality CSP or resource selection. BIRD uses blockchain to maintain transactions between CSPs securely.
Intelligent Short Answer Assessment using Machine Learning

Education is fundamental for human progress. A student is evaluated by the mark he/she scores. The evaluation of student’s work is a central aspect of the teaching profession that can affect students in significant ways. Though teachers use multiple criteria for assessing student work, it is not known if emotions are a factor in their grading decisions. Also, there are several mistakes that occur on the department's side like totaling error, marking mistakes. So, we are developing software to automate the evaluation of answers using Natural Language Processing and Machine Learning. There are two modules, in the first module, we use Optical Character Recognition to extract a handwritten font from the uploaded file and the second module evaluates the answer based on various factors and the mark is awarded. For every answer being entered, evaluation is done based on the usage of word, their importance and grammatical meaning of the sentence. With this approach we can save the cost of checking the answers manually and reduce the workload of the teachers by automating the manual checking process. The evaluation time is also reduced by using this software.
Robotic Mushroom Harvesting by Employing Probabilistic Road Map and Inverse Kinematics

A collision free path to a target location in a random farm is computed by employing a probabilistic roadmap (PRM) that can handle static and dynamic obstacles. The location of ripened mushrooms is an input obtained by image processing. A mushroom harvesting robot is discussed that employs inverse kinematics (IK) at the target location to compute the state of a robotic hand for holding a ripened mushroom and plucking it. Kinematic model of a two-finger dexterous hand with 3 degrees of freedom for plucking mushrooms was developed using the Denavit-Hartenberg method. Unlike previous research in mushroom harvesting, mushrooms are not planted in a grid or some pattern, but are randomly distributed. No human intervention is required at any stage of harvesting.
Conversion of Knill–Laflamme–Milburn Entanglement to Greenberger–Horne–Zeilinger Entanglement in Decoherence‐Free Subspace

Abstract In the process of quantum information processing, decoherence effect caused by the coupling between system and its environment will no doubt lead to the error of quantum information stored in the system. To decrease the influence of decoherence effect, decoherence‐free subspace (DFS) is introduced. In this work, several schemes to convert the polarized‐entangled Knill–Laflamme–Milburn (KLM) states into Greenberger–Horne–Zeilinger (GHZ) states in DFS by using the weak cross‐Kerr nonlinearity are proposed. Numerical analysis shows that the proposed schemes have high fidelity and success probability. Due to the unique applications of multi‐qubit quantum entangled states in quantum information processing and the robust feature of DFS, the proposed schemes may play positive roles in the future development of quantum communication and quantum information processing tasks.
Image Based Reconstruction of Liquids from 2D Surface Detections

In this work, we present a solution to the challenging problem of reconstructing liquids from image data. The challenges in reconstructing liquids, which is not faced in previous reconstruction works on rigid and deforming surfaces, lies in the inability to use depth sensing and color features due the variable index of refraction, opacity, and environmental reflections. Therefore, we limit ourselves to only surface detections (i.e. binary mask) of liquids as observations and do not assume any prior knowledge on the liquids properties. A novel optimization problem is posed which reconstructs the liquid as particles by minimizing the error between a rendered surface from the particles and the surface detections while satisfying liquid constraints. Our solvers to this optimization problem are presented and no training data is required to apply them. We also propose a dynamic prediction to seed the reconstruction optimization from the previous time-step. We test our proposed methods in simulation and on two new liquid datasets which we open source <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/ucsdarclab/liquid_reconstruction so the broader research community can continue developing in this under explored area.
Probabilistic Programs as Measures

This chapter is a tutorial about some of the key issues in semantics of the first-order aspects of probabilistic programming languages for statistical modelling – languages such as Church, Anglican, Venture and WebPPL. We argue that s-finite measures and s-finite kernels provide a good semantic basis.
Development of a component-based interactive visualization system for the analysis of ocean data

With the continuous development of various types of fixed marine observation equipment, satellite remote sensing technology and computer simulation technology, modern marine scientific research has entered the era of big data. Interactive ocean visualization has become ubiquitous owing to the use of ocean data in studies of marine disasters, global climate change and fisheries. However, the primary challenge in analyzing large amounts of ocean data originates from the complexity of the data themselves. Therefore, an interactive multi-scale, multivariate visualization system with dynamic expansion potential is needed for analyzing larger volumes of ocean data. In this study, a unified visual data service was constructed, and a component-based interactive visualization structure for multi-dimensional, spatiotemporal ocean data is presented in this paper. Based on this structure, users can easily customize the system to visualize other types of scientific data.
Real-time optical flow processing on embedded GPU: an hardware-aware algorithm to implementation strategy

Determining the optical flow of a video is a compute-intensive task essential for computer vision. For achieving this processing in real time, the whole algorithm deployment chain must be thought of for efficiency first. The development is usually divided into two parts: first, designing an algorithm that meets precision constraints, then, implementing and optimizing its execution on the targeted platform. We argue that unifying those operations enhances performance on the embedded processor. This paper is based on an industrial use case of computer vision. The objective is to determine dense optical flow in real time on an embedded GPU platform: the Nvidia AGX Xavier. The CLG (combined local–global) optical flow method, initially chosen, is analyzed to understand the convergence speed of its underlying optimization problem. The Jacobi solver is selected for implementation because of its parallel nature. The whole multi-level processing is then ported to the GPU, using several specific optimization strategies. In particular, we analyze the impact of fusing the solver’s iterations with the roofline model. As a result, with a 30 W power budget, our implementation runs at 60FPS, on $$640 \times 512$$ images, with a four-level processing. Hopefully, this example should provide feedback on the issues that arise when trying to port a method to a parallel platform and serve for further implementations of computer vision algorithms on specialized hardware.
Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze

The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure that adopts a serial strategy of two pruned Unet++ blocks based on residual connection. Compared with the simple Encoder–Decoder structure, the serial Unet++ module can better use the features extracted by encoders and promote contextual information fusion in different resolutions. In addition, we take some improvement measures to the Unet++ module, such as pruning, introducing the convolutional module with ResNet structure, and a residual learning strategy. Thus, the serial Unet++ module can generate more realistic images with less color distortion. Furthermore, following the serial Unet++ blocks, an attention mechanism is introduced to pay different attention to haze regions with different concentrations by learning weights in the spatial domain and channel domain. Experiments are conducted on two representative datasets: the large-scale synthetic dataset RESIDE and the small-scale real-world datasets I-HAZY and O-HAZY. The experimental results show that the proposed dehazing network is not only comparable to state-of-the-art methods for the RESIDE synthetic datasets, but also surpasses them by a very large margin for the I-HAZY and O-HAZY real-world dataset.
University Teacher Students’ Learning in Times of COVID-19

At the beginning of the COVID-19 pandemic in spring 2020, school and university learning were abruptly switched to distance learning, coming along with psychological strains and various learning lags on the part of the students. These problems come to a head when focusing on university teacher students, since an expectable competence lag on their part, similarly arising from pandemic-caused distance learning in university teacher training, could affect their future teaching in schools, possibly then disadvantaging school students a second time. To determine changes of teacher students’ self-concept of professional knowledge, we used data of a repeated cross-sectional survey carried out in a period from 2018 to 2021, including several comparable cohorts of overall N = 395 teacher students. This design allowed for splitting the participants in two groups relating to times before and after switching to distance learning. Our results show that the switch to distance learning goes hand in hand with lower scores on almost every dimension of teacher students’ self-concept of professional knowledge, although, in parallel, their scores on variables such as openness to experiences, agreeableness, and conscientiousness increased significantly, indicating a certain degree of compliance with the new situation. Beyond that, we report on an evaluative survey among N = 84 teacher students carried out in July 2020, offering further insights into their situation during the first semester of distance learning. Its results primarily show which specific aspects of distance learning the students consider in need of improvement. On the other hand, it becomes clear that they experienced handicaps in various areas, accompanied by a significant decrease of their core self-evaluations when comparing them to a reference sample. Practical implications and recommendations that can be derived from these results are discussed.
Academic Information System Audit Using Cobit 5 Domain APO Framework

The use of Information Technology (IT) in higher education is said to be useful if its application is in accordance with the vision and mission of the organization. Academic information system audit is a method of assessing or assessing academic information systems to ensure that the governance and management functions of the information system are implemented properly. Unimal is a university that has implemented information technology in its operational processes. In achieving the organization's vision, the use of IT in the organization must always be monitored so that the services provided to users can be maximized, including by conducting an audit process to identify the level of IT maturity so that IT management can find solutions from processes that are considered less than optimal. This study aims to measure the level of capability of Unimal Academic Information Technology using COBIT 5 Domain APO (Align, Plan, and Organise) in the APO01, APO02, APO04 and APO11 processes. The focus of the research is also based on the need for priority processes in the Malikussaleh University environment and research related to the APO domain has not been carried out. The purpose of this study in general is to analyze Academic Information Technology using the COBIT 5 framework in the APO domain at the Malikussaleh University, as well as to provide solutions for improving academic information technology and IT implementation at the Malikussaleh University.
An improved genetic algorithm and its application in neural network adversarial attack

The choice of crossover and mutation strategies plays a crucial role in the searchability, convergence efficiency and precision of genetic algorithms. In this paper, a novel improved genetic algorithm is proposed by improving the crossover and mutation operation of the simple genetic algorithm, and it is verified by 15 test functions. The qualitative results show that, compared with three other mainstream swarm intelligence optimization algorithms, the algorithm can not only improve the global search ability, convergence efficiency and precision, but also increase the success rate of convergence to the optimal value under the same experimental conditions. The quantitative results show that the algorithm performs superiorly in 13 of the 15 tested functions. The Wilcoxon rank-sum test was used for statistical evaluation, showing the significant advantage of the algorithm at 95% confidence intervals. Finally, the algorithm is applied to neural network adversarial attacks. The applied results show that the method does not need the structure and parameter information inside the neural network model, and it can obtain the adversarial samples with high confidence in a brief time just by the classification and confidence information output from the neural network.
Do Security Fear Appeals Work When They Interrupt Tasks? A Multi-Method Examination of Password Strength

Weak passwords are one of the most pervasive threats in cybersecurity. Facing this threat, users require guidance on how to protect themselves. A method frequently used by IS practitioners and researchers to provide this guidance is fear appeals, persuasive messages intended to prompt behavioral changes in response to a threat. However, previous research has not considered a key element of fear appeal effectiveness: task primacy. When fear appeals are a part of the primary or focal task, users’ cognitive engagement will be high by default. However, when fear appeals are delivered as secondary tasks, such as interruptive security messages, users’ engagement is likely to be low because the primary task takes priority in attentional and cognitive resources. In such cases, a remedy is needed to elicit engagement with the fear appeal. In this research note, we theorize that cognitive engagement acts as a contextual moderator that is critical to the effectiveness of fear appeals under the boundary condition of task primacy. Further, we theorize that interactivity, a mechanism that adapts message content through tailored real-time feedback in response to a user’s actions, is a key remedy to enhance engagement with fear appeals. However, to date fear appeals have largely been tested in noninteractive primary tasks, and no study has provided a theoretical explanation for why interactivity enhances the power of a fear appeal. We empirically examined engagement as a contextual moderator in two ways. First, we conducted a field experiment, which manipulated messages on a password creation form on a real-world website. Second, we performed a qualitative focus group study to triangulate the experimental results and more fully reify our theoretical model. Together, the findings reveal that interactivity acts as a catalyst to engage participants with a fear appeal, which then allows the persuasive message of the fear appeal to be internalized. The concepts of boundary condition of task primacy and engagement suggest ways that fear appeals can be more effectively applied in research and practice.
Identifying genuine quantum teleportation

An unknown quantum state can be teleported by using quantum measurements and the maximally entangled Einstein-Podolsky-Rosen (EPR) pair. It is well known that the usual nonclassical teleportation that cannot be simulated by the seminal classical measure-prepare strategy can be demonstrated with all entangled states. Herein, we propose a benchmark which reveals that not all such nonclassical teleportations are truly quantum mechanical. Rather, there exists a more robust classical-teleportation model, which includes the measure-prepare mimicry as a special case, that can describe certain nonclassical teleportations. Invalidating such a general classical model indicates genuine quantum teleportation wherein both the pair state and the measurement are truly quantum mechanical. We prove that EPR steering empowers genuine quantum teleportations, rather than entanglement. The benchmark can be readily used in practical experiments for ensuring that genuine quantum teleportation is implemented. The results presented herein provide strict criteria for implementing quantum-information processing where genuine quantum teleportation is indispensable.
Crosstalk analysis for simultaneously driven two-qubit gates in spin qubit arrays

One of the challenges when scaling up semiconductor-based quantum processors consists in the presence of crosstalk errors caused by control operations on neighboring qubits. In previous work, crosstalk in spin qubit arrays has been investigated for non-driven single qubits near individually driven quantum gates and for two simultaneously driven single-qubit gates. Nevertheless, simultaneous gates are not restricted to single-qubit operations but also include frequently used two-qubit gates such as the CNOT gate. We analyse the impact of crosstalk drives on qubit operations, such as the CNOT and CPHASE gates. We investigate the case of parallel $Y$ and CNOT gates, and we also consider a two-dimensional arrangement of two parallel CNOT gates and find unavoidable crosstalk. To minimize crosstalk errors, we develop appropriate control protocols.
Real Time Object Detection Using YoloReal Time Object Detection Using Yolo

Abstract: Object detection is related to computer vision and involves identifying the kinds of objects that have been detected. It is challenging to detect and classify objects. Recent advances in deep learning have allowed it to detect objects more accurately. In the past, there were several methods or tools used: R-CNN, Fast-RCNN, Faster-RCNN, YOLO, SSD, etc. This research focuses on "You Only Look Once" (YOLO) as a type of Convolutional Neural Network. Results will be accurate and timely when tested. So, we analysed YOLOv3's work by using Yolo3-tiny to detect both image and video objects. Keywords: YOLO, Intersection over Union (IOU), Anchor box, Non-Max Suppression, YOLO application, limitation.