Reviewerly/exHarmony huggingface dataset:
Triple Key Security Algorithm Against Single Key Attack on Multiple Rounds

In cipher algorithms, the encryption and decryption are based on the same key.There are some limitations in cipher algorithms, for example in polyalphabetic substitution cipher the key size must be equal to plaintext otherwise it will be repeated and if the key is known then encryption becomes useless.This paper aims to improve the said limitations by designing of Triple key security algorithm (TKS) in which the key is modified on polyalphabetic substitution cipher to maintain the size of the key and plaintext.Each plaintext character is substituted by an alternative message.The mode of substitution is transformed cyclically which depends on the current position of the modified communication.Three keys are used in the encryption and decryption process on 8 or 16 rounds with the Exclusively-OR (XOR) of the 1 st key.This study also identifies a single-key attack on multiple rounds block cipher in mobile communications and applied the proposed technique to prevent the attack.By utilization of the TKS algorithm, the decryption is illustrated, and security is analyzed in detail with mathematical examples.
Impact of Attention on Adversarial Robustness of Image Classification Models

Adversarial attacks against deep learning models have gained significant attention and recent works have pro-posed explanations for the existence of adversarial examples and techniques to defend the models against these attacks. Attention in computer vision has been used to incorporate focused learning of important features and has led to improved accuracy. Recently, models with attention mechanisms have been proposed to enhance adversarial robustness. Following this context, this work aims at a general understanding of the impact of attention on adversarial robustness. This work presents a comparative study of adversarial robustness of non-attention and attention based image classification models trained on CIFAR-10, CIFAR-100 and Fashion MNIST datasets under the popular white box and black box attacks. The experimental results show that the robustness of attention based models may be dependent on the datasets used i.e. the number of classes involved in the classification. In contrast to the datasets with less number of classes, attention based models are observed to show better robustness towards classification.
How Should Software Engineering Secondary Studies Include Grey Material?

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context</i> : Recent papers have proposed the use of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grey literature</i> (GL) and multivocal reviews. These papers have raised issues about the practices used for systematic reviews (SRs) in software engineering (SE) and suggested that there should be changes to the current SR guidelines. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</i> : To investigate whether current SR guidelines need to be changed to support GL and multivocal reviews. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method</i> : We discuss the definitions of GL and the importance of GL and of industry-based field studies in SE SRs. We identify properties of SRs that constrain the material used in SRs: a) the nature of primary studies; b) the requirements of SRs to be auditable, traceable, and reproducible; and explain why these requirements restrict the use of blogs in SRs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</i> : SR guidelines have always considered GL as a possible source of primary studies and have never supported exclusion of field studies that incorporate the practitioners’ viewpoint. However, the concept of GL, which was meant to refer to documents that were not formally published, is now being extended to information from sources such as blogs/tweets/Q&A posts. Thus, it might seem that SRs do not make full use of GL because they do not include such information. However, the unit of analysis for an SR is the primary study. Thus, it is not the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">source</i> but the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">type</i> of information that is important. Any report describing a rigorous empirical evaluation is a candidate primary study. Whether it is actually included in an SR depends on the SR eligibility criteria. However, any study that cannot be guaranteed to be publicly available in the long term should not be used as a primary study in an SR. This does not prevent such information from being aggregated in surveys of social media and used in the context of evidence-based software engineering (EBSE). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions</i> : Current guidelines for SRs do not require extensions, but their scope needs to be better defined. SE researchers require guidelines for analysing social media posts (e.g., blogs, tweets, vlogs), but these should be based on qualitative primary (not secondary) study guidelines. SE researchers can use mixed-methods SRs and/or the fourth step of EBSE to incorporate findings from social media surveys with those from SRs and to develop industry-relevant recommendations.
Question Answering Systems With Deep Learning-Based Symbolic Processing

The authors propose methods to learn symbolic processing with deep learning and to build question answering systems by means of learned models.Symbolic processing, performed by the Prolog processing systems which execute unification, resolution, and list operations, is learned by a combination of deep learning models, Neural Machine Translation (NMT) and Word2Vec training.To our knowledge, the implementation of a Prolog-like processing system using deep learning is a new experiment that has not been conducted in the past.The results of their experiments revealed that the proposed methods are superior to the conventional methods because symbolic processing (1) has rich representations, (2) can interpret inputs even if they include unknown symbols, and (3) can be learned with a small amount of training data.In particular (2), handling of unknown data, which is a major task in artificial intelligence research, is solved using Word2Vec.Furthermore, question answering systems can be built from knowledge bases written in Prolog with learned symbolic processing, which, with conventional methods, is extremely difficult to accomplish.Their proposed systems can not only answer questions through powerful inferences by utilizing facts that harbor unknown data but also have the potential to build knowledge bases from a large amount of data, including unknown data, on the Web.The proposed systems are a completely new trial, there is no state-of-the-art methods in the sense of ''newest''.Therefore, to evaluate their efficiency, they are compared with the most traditional and robust system i.e., the Prolog system.This is new research that encompasses the subjects of conventional artificial intelligence and neural network, and their systems have higher potential to build applications such as FAQ chatbots, decision support systems and energyefficient estimation using a large amount of information on the Web.Mining hidden information through these applications will provide great value.
Extending Virtual Reality Display Wall Environments Using Augmented Reality

Two major form factors for virtual reality are head-mounted displays and large display environments such as CAVE®and the LCD-based successor CAVE2®. Each of these has distinct advantages and limitations based on how they're used. This work explores preserving the high resolution and sense of presence of CAVE2 environments in full stereoscopic mode by using a see-though augmented reality HMD to expand the user's field of regard beyond the physical display walls. In our explorative study, we found that in a visual search task in a stereoscopic CAVE2, the addition of the HoloLens to expand the field of regard did not hinder the performance or accuracy of the participant, but promoted more physical navigation which in post-study interviews participants felt aided in their spatial awareness of the virtual environment.
DNA Motif Finding Method Without Protection Can Leak User Privacy

DNA sequence analysis plays an important role in the study of gene regulatory networks.DNA motif finding has become a key discipline in the post-gene era and gradually become a research hotspot by mining key gene sequences corresponding to disease mechanism and important biological functions.However, the research of DNA motif finding is faced with a huge problem of privacy disclosure.DNA motif finding technology cannot manage and use data well under controllable conditions, and the mining process of DNA motif finding itself is prone to reveal private information such as individual traits, characteristics and disease defects.In this paper, we presented an overview of the privacy breaching of DNA motif finding, summarized the main methods and tools of the current DNA motif finding, analyzed its privacy risks, and used two case studies to verify that the DNA motif finding may identify individual privacy information.Finally, we discussed the privacy protection methods for motif finding and proposed the privacy protection solutions.
Synthetic IR Image Refinement Using Adversarial Learning With Bidirectional Mappings

Collecting a large dataset of real infrared (IR) images is expensive, time-consuming, and even unavailable in some specific scenarios. With recent progress in machine learning, it has become more feasible to replace real IR images with qualified synthetic IR images in learning-based IR systems. However, this alternative may fail to achieve the desired performance, due to the gap between real and synthetic IR images. Inspired by adversarial learning for image-to-image translation, we propose the Synthetic IR Refinement Generative Adversarial Network (SIR-GAN) to narrow this gap. By learning the bidirectional mappings between two unpaired domains, the realism of the simulated IR images generated from the IR Simulator are significantly improved, where the source domain contains a large number of simulated IR images, where the target domain contains a limited quantity of real IR images. Specifically, driven by the idea of transferring infrared characteristic and protect target semantic information simultaneously, we propose a SIR refinement loss to consider an infrared loss and a structure loss further to the adversarial loss and the consistency loss. To further reduce the gap, stabilize training, and avoid artefacts, we modify the proposed algorithm by developing a training strategy, adding the U-net in the generators, using the dilated convolution in the discriminators and invoking the N-Adam acts as the optimizer. Qualitative, quantitative, and ablation study experiments demonstrate the superiority of the proposed approach compared with the state-of-the-art techniques in terms of realism and fidelity. In addition, our refined IR images are evaluated in the context of a feasibility study, where the accuracy of the trained classifier is significantly improved by adding our refined data into a small real-data training set
A representation of proper BC domains based on conjunctive sequent calculi

Abstract We build a logical system named a conjunctive sequent calculus which is a conjunctive fragment of the classical propositional sequent calculus in the sense of proof theory. We prove that a special class of formulae of a consistent conjunctive sequent calculus forms a bounded complete continuous domain without greatest element (for short, a proper BC domain), and each proper BC domain can be obtained in this way. More generally, we present conjunctive consequence relations as morphisms between consistent conjunctive sequent calculi and build a category which is equivalent to that of proper BC domains with Scott-continuous functions. A logical characterization of purely syntactic form for proper BC domains is obtained.
Detection of a mixed bipartite entangled state in arbitrary dimension via a structural physical approximation of partial transposition

It is very crucial to know that whether the quantum state generated in the experiment is entangled or not. In the literature, this topic was studied extensively and researchers proposed different approaches for the detection of mixed bipartite entangled state in arbitrary dimension. Proceeding in this line of research, we also propose three different criteria for the detection of mixed bipartite negative partial transpose (NPT) entangled state in arbitrary dimension. Our criteria is based on the method of structural physical approximation (SPA) of partial transposition (PT). We have shown that the proposed criteria for the detection of NPT entangled state can be realized experimentally. Two of the proposed criteria is given in terms of the concurrence of the given state in arbitrary dimension so it is essential to find out the concurrence. Thus, we provide new lower and upper bound of concurrence of the quantum state under investigation in terms of average fidelity of two quantum states and hence these bounds can be realized experimentally. Moreover, we have shown how to perform SPA map on qutrit-qubit system and then explicitly calculated the matrix elements of the density matrix describing the SPA-PT of the qutrit-qubit system. We then illustrate our criteria for the detection of entanglement by considering a class of qubit-qubit system and a class of qutrit-qubit system.
Algorithms for simultaneous Hermite–Padé approximations

We describe how to compute simultaneous Hermite–Padé approximations, over a polynomial ring K[x] for a field K using O∼(nω−1td) operations in K, where d is the sought precision, where n is the number of simultaneous approximations using t<n polynomials, and where O(nω) is the cost of multiplying n×n matrices over K. We develop two algorithms using different approaches. Both algorithms return a reduced sub-basis that generates the complete set of solutions to the input approximation problem that satisfy the given degree constraints. Previously, the cost O∼(nω−1td) has only been reached with randomized algorithms finding a single solution for the case t<n. Our results are made possible by recent breakthroughs in fast computations of minimal approximant bases and Hermite–Padé approximations for the case t≥n.
Simplified Salp Swarm Algorithm

Aiming at the problem that the basic salp swarm algorithm(SSA) is complex and the operation efficiency is not high, a simplified salp swarm algorithm(SSSA) is proposed. Firstly the position update method of the leader in the basic SSA was analyzed. Then the random search radius was used to optimize the leader's search range and the generation process of a random number was reduced. The follower's position update in the basic SSA was followed. Finally, the operation flow of the simplified algorithm was given. Through the optimization experiment of 23 common benchmark functions, the simplified algorithm and the basic algorithm were performed 30 times, and the average value, standard deviation and average running time were compared. The results show that the simplification algorithm reduces the average running time by 2.1%~11.5% and improves the running efficiency on the basis of fully maintaining the convergence speed, convergence precision and robustness of the basic algorithm.
Clustering-based multiple instance learning with multi-view feature

Multi-instance learning (MIL) is a special kind of classification problem where samples (called “instances”) are grouped into bags and labels are given only on bag level instead of instance level. From the expert and intelligent system perspective, MIL does not require full ground-truth labels which helps to reduce the cost of data labeling in real tasks. Many inviting problems such as image classification, video annotation and object detection can be formulated in MIL frameworks. In this paper, we propose a similarity-based method with clustering in a multi-view feature manner to solve MIL problems efficiently. The clustering is introduced as a novel strategy for instance selection. Considering unlabeled data, clustering methods fit well for obtaining the hidden structure information in feature space, and thus we utilize a clustering-based strategy to exploit such information on discovering positive instances. To fully use the original input data, we further develop our method in multi-view feature space, in order to make our model capture information from different feature space and provide support for the whole system. Experiments on benchmark datasets for classification have been conducted, and we also perform image retrieval to examine the extended multi-view model on a large image dataset, MIL-NUS-WIDE. The promising results demonstrate the effectiveness of our clustering-based MIL (CMIL) model.
Consistent Distributed Track Fusion Under Communication Constraints

This paper addresses the problem of retrieving consistent estimates in a distributed network where the communication between the nodes is constrained such that only the diagonal elements of the covariance matrix are allowed to be exchanged. Several methods are developed for preserving and/or recovering consistency under the constraints imposed by the communication protocol. The proposed methods are used in conjunction with the covariance intersection method and the estimation performance is evaluated based on information usage and consistency. The results show that among the proposed methods, consistency can be preserved equally well at the transmitting node as at the receiving node.
Beyond the Input Stream

Method-independent text entry evaluation tools are often used to conduct text entry experiments and compute performance metrics, like words per minute and error rates. The input stream paradigm of Soukoreff & MacKenzie (2001, 2003) still remains prevalent, which presents a string for transcription and uses a strictly serial character representation for encoding the text entry process. Although an advance over prior paradigms, the input stream paradigm is unable to support many modern text entry features. To address these limitations, we present transcription sequences: for each new input, a snapshot of the entire transcribed string unto that point is captured. By comparing adjacent strings within a transcription sequence, we can compute all prior metrics, reduce artificial constraints on text entry evaluations, and introduce new metrics. We conducted a study with 18 participants who typed 1620 phrases using a laptop keyboard, on-screen keyboard, and smartphone keyboard using features such as auto-correction, word prediction, and copy/paste. We also evaluated non-keyboard methods Dasher, gesture typing, and T9. Our results show that modern text entry methods and features can be accommodated, prior metrics can be correctly computed, and new metrics can reveal insights. We validated our algorithms using ground truth based on cursor positioning, confirming 100% accuracy. We also provide a new tool, TextTest++, to facilitate web-based evaluations.
Random forests in medical image computing

Abstract The Random Forests algorithm had a substantial impact on medical image computing over the last decade. This chapter presents basic algorithmic details, some variations proposed in the recent years and applications in medical image computing. Arguably, Random Forests' main impact was on the analysis tasks that required understanding spatial context within the images. We take a specific angle and view Random Forests as a machine learning tool that can integrate contextual information. We position the algorithm and its contributions within the larger field from this respect. Lastly, we briefly discuss how Random Forests and deep learning methods relate to each other and how they differ.
HyperLearn

Multimodal datasets contain an enormous amount of relational information, which grows exponentially with the introduction of new modalities. Learning representations in such a scenario is inherently complex due to the presence of multiple heterogeneous information channels. These channels can encode both (a) inter-relations between the items of different modalities and (b) intra-relations between the items of the same modality. Encoding multimedia items into a continuous low-dimensional semantic space such that both types of relations are captured and preserved is extremely challenging, especially if the goal is a unified end-to-end learning framework. The two key challenges that need to be addressed are: 1) the framework must be able to merge complex intra and inter relations without losing any valuable information and 2) the learning model should be invariant to the addition of new and potentially very different modalities. In this paper, we propose a flexible framework which can scale to data streams from many modalities. To that end we introduce a hypergraph-based model for data representation and deploy Graph Convolutional Networks to fuse relational information within and across modalities. Our approach provides an efficient solution for distributing otherwise extremely computationally expensive or even unfeasible training processes across multiple-GPUs, without any sacrifices in accuracy. Moreover, adding new modalities to our model requires only an additional GPU unit keeping the computational time unchanged, which brings representation learning to truly multimodal datasets. We demonstrate the feasibility of our approach in the experiments on multimedia datasets featuring second, third and fourth order relations.
A Multi-Trip Vehicle Routing Problem for Small Unmanned Aircraft Systems-Based Urban Delivery

With the emergence of new technologies for small unmanned aircraft systems (sUAS), such as lightweight sensors and high-efficiency batteries, the operation of small unmanned aerial vehicles (sUAVs) will expand from military use to commercial use. A promising commercial application of sUAS is package delivery because of its potential to reduce acquisition and operating costs of the last-mile delivery system, while enabling new services such as same-day delivery. Moreover, in urban areas, sUAVs can deliver packages to customers without negatively affecting street traffic. To conduct urban operations, sUAS-based delivery systems must obey regulations for sUAS operations and avoid urban obstacles. In this paper, a maximum-flight-time-constrained multitrip vehicle routing problem with time windows optimization model is used to create routes for sUAS-based delivery missions. To address the actual urban environment with the optimization model, a two-layered urban flight network is built by feeding an airborne light detection and ranging sensor data into an algorithm that uses a Voronoi diagram to create collision-free paths. This paper uses this approach to study a possible package delivery using sUAS in San Diego, CA.