Reviewerly/exHarmony huggingface dataset:
Resilient Abstractive Summarization Model with Adaptively Weighted Training Loss

Recently, abstractive summarization models are preferred over extractive summarization models as they can generate words that do not exist in the original text, whose summary descriptions are more flexible and natural. Neural network-based models learn the pattern of summary generation from the training data by modeling the relationship between the original text and the reference summary, which is very dependent on the reference summary. Although we intuitively feel that summary with higher Abstraction Degree (quantified by the number of words in the summary which do not appear in the original text) will be more general, manually generated summaries with high Abstraction Degree are most likely subliminally written with additional knowledge. It's difficult to learn the generation pattern of such reference summary using limited training data. What's more, such reference summaries can even harm the model performance. To this end, we design a learning method that can adaptively weighted difference training samples based on their Abstraction Degree, so that the model will pay less attention to the samples with higher Abstraction Degree. Experiments of LCSTS and CNN-DM dataset show that our method greatly improves the performance of the summarization model and is resilient in the face of training data containing low quality reference summaries.
Multi-Cloud Path Planning of Unmanned Aerial Vehicles with Multi-Criteria Decision Making: A Literature Review

In the yester years, drones or unmanned aerial vehicle (UAV) has stayed an attention to consider on with regard to associating innovation, security concerns, rules, and guidelines all inclusive. Wonderful progressions and utilizations in photogrammetry and remote sensing applications in multi-cloud region are justified. Moving right along, classification, hardware design, path planning, and challenges are also dealt with. Coverage path planning entitles figuring out the route that covers every location on an aspired area of interest. Exploration and analyzation of the already existing studies of literature in terms of various approaches being employed in coverage path planning problems are the key aim of this paper. Multi-cloud online processing of UAV captured data is a striking selection to real-time data processing, as it accommodates for the observation and examination of data from an assortment of sources, this makes the accessibility of data between devices much more seamless and easier to plan. The path planning techniques on UAVs categorized into three solid methods of classifications them being the following, representative, cooperative, and non-cooperative methods. What’s more, various open examination issues dependent on UAVs path planning and multi-cloud security are investigated to give profound bits of knowledge.
Two-Mode Squeezed and Entangled Light Production in Parametric Oscillations

We investigate the statistical and quadrature squeezings, as well as the entanglement properties, of a two-mode light generated by non-degenerate parametric oscillations coupled to a two-mode squeezed vacuum reservoir, by employing the solutions of the quantum Langevin equations. It is found that the two-mode light shows the two-mode squeezing and entanglement for all values of the time. Moreover, it is observed that the squeezed vacuum reservoir and the growing amplitude of the pump mode enhance the degrees of two-mode squeezing and entanglement. We have also shown that the amounts of squeezing and entanglement are significant in a region, where the mean photon number is higher, and the photon number correlation is lower.
Bulsarapp: Interactive Visual Analysis for Surname Trend Exploration

The study of surnames for a given population, together with their distribution and spatial patterns identification, has been a long-standing problem in the fields of human biology, public health, and social sciences. The ancestry inferred from surname information can be a useful means to understand the dynamics of human populations. This knowledge allows us to characterize geographically the ethnicity of populations, and to understand the complex relationships between identity, migration, and health issues in a demographic view. However, in most cases, a detailed geolocalization of this data can be a daunting task. We propose a visual analytic tool that summarizes the heterogeneous surname and geographic information collected from Argentinean electoral rolls. This tool allows a massive data analysis, and facilitates interdisciplinary studies about population dynamics related to ancestry, migration, and health. It also offers an easy-to-use interface that allows interactive exploration of isonymy and surname origins, their distribution, and spatial trends in a high population density context.
Interpretable OR for High-Stakes Decisions: Designing the Greek COVID-19 Testing System

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Using of sigil software in math education: e-module development and effects on self-regulated learning skills

In the delivery of content, it is important to have multimedia elements so that the learning material delivery can be well received by students. One of these elements is the e-module. This study aims to create an e-module based on Sigil software on enumeration principles, as well as to examine the effects on self-regulated learning (SRL) using the e-module. In this study, action research techniques were used. Some of the researchers are both practitioners and researchers in the research. This e-module development focuses on the development design phases of Alessi &amp;amp; Trollip (2001), which consist of three stages, namely: the planning stage, the design stage, and the development stage. Product tests were performed at the production stage to assess the feasibility of the e-module. The validation test performed was an alpha test and a beta test. The alpha test was carried out by seven content experts and two media experts. The beta test was carried out through a beta-1 test involving 30 students and a beta-2 test involving 27 students of Vocational High School, Lembang, Indonesia for the 2020/2021 academic year. Data were collected through interview guidelines, questionnaires for material experts and media experts, students who responded to questionnaires, and questions’ tests. Data analysis techniques for assessing the viability of e-modules used descriptive data analysis, Cochran Q Test for three or more paired samples As a result of the study, an increase has been observed in students' ability to design e-modules for mathematics lessons. At the same time, it supported the development of student's cognitive and affective factors related to the mathematics course. It has been determined that e-module design is a very effective instructional design in the development of students' self-regulated learning skills. In future research, the effects of e-module design stages on students can be examined in detail. In addition, it can be examined in detail which self-regulated learning skills are developed and to what extent.
A Distributed Virtual Reality Study Under COVID-19 Conditions – Comparing Continuous and Non-Continuous Locomotion Techniques in Mobile VR

The ongoing COVID-19 pandemic complicates empirical virtual reality (VR) research and especially laboratory studies. As an alternative study design, we conducted a distributed VR study that took place in the participants' homes, using mobile VR goggles and personal smartphones. We investigated the influence of two different locomotion techniques on presence, usability, and cybersickness. Subjects were asked to solve a navigation task once with the continuous "Look-Down-to-Move" technique and once with the non-continuous "Point-And-Teleport" technique. Surprisingly, the two locomotion techniques performed comparably well on all variables assessed. The study design showed to be understandable and conductible for the subjects. However, we found that (1) hardware limitations of the used smartphones and (2) confounding factors that are beyond the control of the experimenters could have distorted the results of the study. We propose using uniform hardware and imposing stricter rules for participation to achieve more controlled conditions. Our results hint that the impact of the continuity of locomotion on presence, usability, and cybersickness may be less severe in the context of mobile VR than in immersive high-end VR devices. If these findings are corroborated by subsequent studies, they could provide designers of mobile virtual environments (VEs) with more flexibility when developing a suitable locomotion technique for their application.
Rotated Ring, Radial and Depth Wise Separable Radial Convolutions

Simple image rotations significantly reduce the accuracy of deep neural networks. Moreover, training with all possible rotations increases the data set, which also increases the training duration. In this work, we address trainable rotation invariant convolutions as well as the construction of nets, since fully connected layers can only be rotation invariant with a one-dimensional input. On the one hand, we show that our approach is rotationally invariant for different models and on different public data sets. We also discuss the influence of purely rotational invariant features on accuracy. The rotationally adaptive convolution models presented in this work are more computationally intensive than normal convolution models. Therefore, we also present a depth wise separable approach with radial convolution.
A Quantum Activation Function for Neural Networks: Proposal and Implementation

A non-linear activation function is an integral component of neural network algorithms used for various tasks such as data classification and pattern recognition. In neu-romorphic/emerging-hardware-based implementations of neural network algorithms, the non-linear activation function is often implemented through dedicated analog electronics. This enables faster execution of the activation function during training and inference of neural networks compared to conventional digital implementation. Here, with a similar motivation, we propose a novel non-linear activation function that can be used in a neural network for data classification. Our activation function can be implemented by taking advantage of the inherent nonlinearity in qubit preparation and SU(2) operation in quantum mechanics. These operations are directly implementable on quantum hardware through single-qubit quantum gates as we show here. In addition, the SU(2) parameters are adjustable here making the activation function adaptable; we adjust the parameters through classical feedback like in a variational algorithm in quantum machine learning. Using our proposed quantum activation function, we report accurate classification using popular machine learning data sets like Fisher's Iris, Wisconsin's Breast Cancer (WBC), Abalone, and MNIST on three different platforms: simulations on a classical computer, simulations on a quantum simulation framework like Qiskit, and experimental implementation on quantum hardware (IBM-Q). Then we use a Bloch-sphere-based approach to intuitively explain how our proposed quantum activation function, with its adaptability, helps in data classification.
Old Frauds with a New Sauce: Digital Coins and Behavioral Paradigms

In this paper, we explore the conversion of market frauds from stocks to virtual assets. We investigate the transition from traditional to digital for six schemes: ransomware, price manipulations, “pumps and dumps”, misrepresentation, “spoofing” and Ponzi schemes to find that the advent of cryptocurrencies may facilitate pseudonymous criminal behavior in the present regulatory environment. Furthermore, apply the Regulatory Dialectic Theory and use the behavioral finance literature to explain the persistence of the relationship between scammers and their victims, as fraudsters innovate in the digital realm, but but investors fail to adapt.
Offline Evaluation Standards for Recommender Systems

Offline evaluation has nowadays become a major step in developing Recommendation Systems in both academia and industry [4, 5]. While academia anchors on offline evaluation due to the lack of proper environments for conducting online tests with real users, the industry uses offline evaluation to filter the most promising solutions for further online testing, aiming at reducing costs and potential damage to customers. Despite the blunt advances observed on this topic recently, consolidating a reliable, replicable, flexible and efficient offline evaluation process capable of satisfactorily predicting online test results remains an open challenge [2]. The community still lacks an integrated and updated view on this topic, useful for practitioners to inspect and refine their current offline evaluation stack.
Convex strategies for trajectory optimisation: application to the Polytope Traversal Problem

Non-linear trajectory optimisation methods require good initial guesses to converge to a locally optimal solution. A feasible guess can often be obtained by allocating a large amount of time for the trajectory to be complete. However for unstable dynamical systems such as humanoid robots, this quasi-static assumption does not always hold. We propose a conservative formulation of the trajectory problem that simultaneously computes a feasible path and its time allocation. The problem is solved as a convex optimisation problem guaranteed to converge to a feasible local optimum. The approach is evaluated with the computation of feasible trajectories that traverse sequentially a sequence of polytopes. We demonstrate that on instances of the problem where quasi static solutions are not admissible, our approach is able to find a feasible solution with a success rate above 80% in all the scenarios considered, in less than 10ms for problems involving traversing less than 5 polytopes and less than 1s for problems involving 20 polytopes, thus demonstrating its ability to reliably provide initial guesses to advanced non linear solvers.
Missing Person Tracking System

In this paper, we propose a Missing Person Tracking System. It’s a web-based application for the police investigation team to track the missing person. This system, therefore, was to investigate weaknesses in the current system of searching for missing persons in India, with the interest to offer a more significant solution. The results indicate that most respondents do not have much confidence in the current process of searching for the missing persons. From the results, we can see that the current system is not efficient enough, because many people reported as missing are either found after a long search or not found at all. The paper concludes that we need a system and amore efficient method of finding missing persons more easily and faster. A new approach has been proposed by us for the police and government to trace out missing people easily.
Joining Forces for Reconstruction Inverse Problems

A spectral inverse problem concerns the reconstruction of parameters of a parent graph from prescribed spectral data of subgraphs. Also referred to as the P–NP Isomorphism Problem, Reconstruction or Exact Graph Matching, the aim is to seek sets of parameters to determine a graph uniquely. Other related inverse problems, including the Polynomial Reconstruction Problem (PRP), involve the recovery of graph invariants. The PRP seeks to extract the spectrum of a graph from the deck of cards each showing the spectrum of a vertex-deleted subgraph. We show how various algebraic methods join forces to reconstruct a graph or its invariants from a minimal set of restricted eigenvalue-eigenvector information of the parent graph or its subgraphs. We show how functions of the entries of eigenvectors of the adjacency matrix A of a graph can be retrieved from the spectrum of eigenvalues of A. We establish that there are two subclasses of disconnected graphs with each card of the deck showing a common eigenvalue. These could occur as possible counter examples to the positive solution of the PRP.
Black Box Attack on Speech Commands Classification Model

The field of Adversarial Machine Learning (AML) has emerged due to the rapid development of the machine learning field. The goal of an adversarial attack is to fool a machine learning model into giving false or manipulated results. In today’s world, speech is not only limited to humans, but machines also capture and process it. Nowadays, machines can "speak" and "hear" human languages. Quite a number of researchers are working to enable human-machine interaction using various deep learning techniques. Recently, AML researchers have demonstrated effective attacks against clean machine learning models. However, much of the research is still focused on deceiving image recognition models. The research in fooling speech recognition models is in its infancy. In this paper, we present a novel application of a targeted black-box adversarial attack on a speech classification model. The resulting adversarial example sounds the same as the original speech command even after adding attacker noise by using the SimBA (Simple Black-box Attack) algorithm that randomly adds perturbations in random orthonormal directions to the audio sample and queries the model for the probability of it being misclassified as the targeted class. We demonstrate that the algorithm is 90% successful in classifying a speech command as its antonym.
Research on the Application of Blockchain Technology in Accounting Information System

In order to overcome the problems existing in the traditional accounting information system, and to improve the application efficiency of the blockchain technology and the working efficiency of the accounting information system, this paper puts forward a method of constructing the application system of the accounting information system based on the blockchain technology. This construction method combines block chain technology scientifically and organically, and gives full play to the advantages of block chain technology, such as decentralization and data in-variance. Based on this, the construction method also studies and analyzes the problems existing in the current accounting information system, such as data quality, data security and so on, and analyzes the unfavorable factors that affect the current accounting information system, for example, hacker attacks, data theft and so on. The research results show that this construction method can break through the limitations of the traditional accounting information system, optimize the current accounting information system, and fundamentally improve the real-time, and privacy protection of the accounting information system.
Channel Capacity of Neural Networks

Occam’s Razor principle suggests preference for simpler models and triggers an enduring question: what is the proper definition of complexity of a model? In this work, we regard neural networks as communication channels and measure the complexity of neural networks by means of their channel capacity—the maximum information reserved in the output of a neural network. Furthermore, we show a connection between the L2-norm of the weight matrix of the linear model and its channel capacity through the singular values of the weight matrix. On image classification problems, we find regularizing different neural networks by constraining their channel capacity effectively boosts the generalization performance and outperforms other information-theoretic regularization methods.
Real-Time Face Mask Detection Using Raspberry Pi and Camera

We all know that this is the ERA of Coronavirus diseases (COVID-19). It is a kind of pandemic which is growing at a fast rate and causing a health crisis. It caused many deaths last years and still counting. So, the doctors and scientists are working on the vaccine for this virus and also, they have suggested some ways to be safe from this virus, face masks are one of the most common measures to fight such virus. In this paper, we have discussed on the project about face mask detection using Raspberry Pi and a live video streaming camera. The face mask detection model was done with the help of a computer vision, CNN, image classification algorithm based on the MobileNetV2 neural network. The steps involved in the project are: firstly, we have taken the data-set of people wearing the face mask and then those not wearing the face mask, after then we pre-processed it, split the data, trained the model using MobileNetV2 neural network, tested the model, and finally implemented the model. The model has been trained with an accuracy of 95.85%. This system automatically opens the door when people are wearing the face mask and sends an alert if they don’t have the face mask, to the authorities or the owner of the place. It can be used in a variety of places, include educational institutions, hospitals, churches, and retail outlets.
Short Paper: Ballot Secrecy for Liquid Democracy

Certain advances in election technology, such as online voting, promise to reduce the administrative overhead of running an election. This has breathed new life into the idea of direct democracy, where voters play a more active role in setting legislation. However it is anticipated that a steady stream of referendums would generate voter fatigue. To combat this fatigue, voters could be allowed to delegate their votes to other (more knowledgeable) voters. This idea is old but has been recently reinvented under the name liquid democracy. In this paper, we consider how ballot secrecy should be defined for liquid democracy. We first show that a natural definition of full secrecy leads to several undesirable outcomes. We then show that these are very difficult to address without enabling voter coercion and vote buying. The purpose of the paper is not to affirm liquid democracy; rather, it is to raise awareness of unseen complexity hiding under our initial presumption that liquid democracy could effortlessly support a secret ballot.
Find and Dig: A Privacy-Preserving Image Processing Mechanism in Deep Neural Networks for Mobile Computation

In recent years, there have been increasing demands for using deep neural networks (DNNs) to provide image processing services for mobile devices. Considering the privacy of users' images, we utilize a two-tiers DNN which deploys the shallow and deep model on mobile devices and the cloud respectively. Then we propose a novel privacy protection mechanism which is deployed on the mobile device to satisfy the differential privacy. Meanwhile, based on the convolution kernel analysis, we also propose a novel method to improve the computation efficiency of mobile devices. The highlight of our mechanism is that it not only provides customized privacy protection which can resist the attack of Generative Adversarial Network (GAN), but also improves the accuracy of the neural network model. The experimental results on the ImageNet dataset show that we have improved the top-5 accuracy of image classification by 2%-3%. Under the premise of ensuring that the accuracy of the network is not degraded, our method reduces the CPU consumption on the VGG16 and ResNet50 networks to 74.6% and 48.9%, respectively, and can reduce 90% of the memory overhead. This improvement makes it possible to enable mobile deep neural network applications.