Reviewerly/exHarmony huggingface dataset:
Human Action Recognition in Drone Videos using a Few Aerial Training Examples

Drones are enabling new forms of human actions surveillance due to their low cost and fast mobility. However, using deep neural networks for automatic aerial action recognition is difficult due to the need for a large number of training aerial human action videos. Collecting a large number of human action aerial videos is costly, time-consuming, and difficult. In this paper, we explore two alternative data sources to improve aerial action classification when only a few training aerial examples are available. As a first data source, we resort to video games. We collect plenty of aerial game action videos using two gaming engines. For the second data source, we leverage conditional Wasserstein Generative Adversarial Networks to generate aerial features from ground videos. Given that both data sources have some limitations, e.g. game videos are biased towards specific actions categories (fighting, shooting, etc.,), and it is not easy to generate good discriminative GAN-generated features for all types of actions, we need to efficiently integrate two dataset sources with few available real aerial training videos. To address this challenge of the heterogeneous nature of the data, we propose to use a disjoint multitask learning framework. We feed the network with real and game, or real and GAN-generated data in an alternating fashion to obtain an improved action classifier. We validate the proposed approach on two aerial action datasets and demonstrate that features from aerial game videos and those generated from GAN can be extremely useful for an improved action recognition in real aerial videos when only a few real aerial training examples are available.
Learning One-Clock Timed Automata

We present an algorithm for active learning of deterministic timed automata with a single clock. The algorithm is within the framework of Angluin's $L^*$ algorithm and inspired by existing work on the active learning of symbolic automata. Due to the need of guessing for each transition whether it resets the clock, the algorithm is of exponential complexity in the size of the learned automata. Before presenting this algorithm, we propose a simpler version where the teacher is assumed to be smart in the sense of being able to provide the reset information. We show that this simpler setting yields a polynomial complexity of the learning process. Both of the algorithms are implemented and evaluated on a collection of randomly generated examples. We furthermore demonstrate the simpler algorithm on the functional specification of the TCP protocol.
A Scalable Data Partitioning Framework to Enhance Cloud Security

The huge data on the websites due to rapid use of cloud computing increase the security concern.Cloud computing provides various services such as storage, scalable transactions, sharing and downloading.There are many techniques which provides security of these services but these are dynamic in nature and leads to overload the memory.In this paper, a novel framework has been presented based on data partitioning technique which avoids the data duplication and provides scalable consistent data with enhanced security.The proposed work divided into three parts in which Dice Coefficient and Cosine Similarity has been used to secure the similar data.The data has been further encrypted using the encryption system to enhance the data integrity and security level.The encrypted data further consider as input for the data partition techniques in which different algorithms has been developed to partition the data in blocks.The proposed work further optimized using the Artificial Bee Colony algorithm to optimize the partitioned data.The obtained results further cross validated using the classification technique Artificial Neural Network (ANN) in which data has been trained and tested to check the effectiveness of the proposed work.The experimental results finally validated by measuring the True Positive Value (TP), False Negative Value (FN), True Negative Value, and False Positive value.The good TP and bad FN shows that proposed framework efficiently partitioned the data in an effective and scalable way.
Weighted Shortest Common Supersequence Problem Revisited

A weighted string, also known as a position weight matrix, is a sequence of probability distributions over some alphabet. We revisit the Weighted Shortest Common Supersequence (WSCS) problem, introduced by Amir et al. [SPIRE 2011], that is, the SCS problem on weighted strings. In the WSCS problem, we are given two weighted strings $$W_1$$ and $$W_2$$ and a threshold $$\tfrac{1}{z} $$ on probability, and we are asked to compute the shortest (standard) string S such that both $$W_1$$ and $$W_2$$ match subsequences of S (not necessarily the same) with probability at least $$\tfrac{1}{z} $$ . Amir et al. showed that this problem is NP-complete if the probabilities, including the threshold $$\tfrac{1}{z} $$ , are represented by their logarithms (encoded in binary). We present an algorithm that solves the WSCS problem for two weighted strings of length n over a constant-sized alphabet in $$\mathcal {O}(n^2\sqrt{z} \log {z})$$ time. Notably, our upper bound matches known conditional lower bounds stating that the WSCS problem cannot be solved in $$\mathcal {O}(n^{2-\varepsilon })$$ time or in $$\mathcal {O}^*(z^{0.5-\varepsilon })$$ with time, where the $$\mathcal {O}^*$$ notation suppresses factors polynomial with respect to the instance size (with numeric values encoded in binary), unless there is a breakthrough improving upon long-standing upper bounds for fundamental NP-hard problems (CNF-SAT and Subset Sum, respectively). We also discover a fundamental difference between the WSCS problem and the Weighted Longest Common Subsequence (WLCS) problem, introduced by Amir et al. [JDA 2010]. We show that the WLCS problem cannot be solved in $$\mathcal {O}(n^{f(z)})$$ time, for any function f(z), unless $$\mathrm {P}=\mathrm {NP}$$ .
Human-AI Co-Learning for Data-Driven AI

Human and AI are increasingly interacting and collaborating to accomplish various complex tasks in the context of diverse application domains (e.g., healthcare, transportation, and creative design). Two dynamic, learning entities (AI and human) have distinct mental model, expertise, and ability; such fundamental difference/mismatch offers opportunities for bringing new perspectives to achieve better results. However, this mismatch can cause unexpected failure and result in serious consequences. While recent research has paid much attention to enhancing interpretability or explainability to allow machine to explain how it makes a decision for supporting humans, this research argues that there is urging the need for both human and AI should develop specific, corresponding ability to interact and collaborate with each other to form a human-AI team to accomplish superior results. This research introduces a conceptual framework called "Co-Learning," in which people can learn with/from and grow with AI partners over time. We characterize three key concepts of co-learning: "mutual understanding," "mutual benefits," and "mutual growth" for facilitating human-AI collaboration on complex problem solving. We will present proof-of-concepts to investigate whether and how our approach can help human-AI team to understand and benefit each other, and ultimately improve productivity and creativity on creative problem domains. The insights will contribute to the design of Human-AI collaboration.
Applying Augmented Reality to New or Existing Remote Access Laboratories

Augmented reality and remote access laboratories unions are currently very limited. For the end-user, the experience must be about the execution of the experiment and understanding the significance of the lesson. Most educational institutions practical hands-on experiments have been developed many years ago and remained static. These systems may have only been revisited as a result of technological upgrades. Remote access laboratories have driven new thought processes on how to deliver content effectively to students. Developing effective remote access configurations can suffer from weary developer syndrome, where proper development cycles are ignored and once a system is functional, the development ceases. Little regard is taken on how to continue the improvement cycle or even if it is required. Adding augmented reality may be outside of the capabilities of many facilities due to a lack of understanding surrounding the technology. What augmented reality is, what its capabilities are and how it can be implemented are key questions for management, developers and end-users alike.
The CASPER Project Approach Towards User-Centric Mobile Networks

This paper presents an overview of the project CASPER, a Marie Curie Research and Innovation Staff Exchange (RISE) project running from 2016 until 2020. The main objective of CASPER is to combine academic and industrial forces towards leveraging the expected benefits of Quality of Experience (QoE) exploitation in future digital networks. In particular, CASPER exploits the most recent advances in communication networks, such as the Software Defined Networking (SDN) and the Network Functions Virtualisation (NFV) in order to design and implement a middleware architecture for QoE-driven service provisioning. CASPER, therefore, addresses the challenges that mobile network operators and service providers face in the current fully digitalized era of communications, concerning the need to provide customer-oriented, reliable, efficient, flexible and dynamic service management.
Delving into VoxCeleb: environment invariant speaker recognition

Research in speaker recognition has recently seen significant progress due to the application of neural network models and the availability of new large-scale datasets. There has been a plethora of work in search for more powerful architectures or loss functions suitable for the task, but these works do not consider what information is learnt by the models, apart from being able to predict the given labels. In this work, we introduce an environment adversarial training framework in which the network can effectively learn speaker-discriminative and environment-invariant embeddings without explicit domain shift during training. We achieve this by utilising the previously unused `video' information in the VoxCeleb dataset. The environment adversarial training allows the network to generalise better to unseen conditions. The method is evaluated on both speaker identification and verification tasks using the VoxCeleb dataset, on which we demonstrate significant performance improvements over baselines.
Hyperspectral Super-resolution: A Coupled Nonnegative Block-term Tensor Decomposition Approach

Hyperspectral super-resolution (HSR) aims at fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image (SRI). Recently, a coupled tensor factorization approach was proposed to handle this challenging problem, which admits a series of advantages over the classic matrix factorization-based methods. In particular, modeling the HSI and MSI as low-rank tensors following the {\it canonical polyadic decomposition} (CPD) model, the approach is able to provably identify the SRI, under some mild conditions. However, the latent factors in the CPD model have no physical meaning, which makes utilizing prior information of spectral images as constraints or regularizations difficult---but using such information is often important in practice, especially when the data is noisy. In this work, we propose an alternative coupled tensor decomposition approach, where the HSI and MSI are assumed to follow the {\it block-term decomposition (BTD)} model. Notably, the new method also entails identifiability of the SRI under realistic conditions. More importantly, when modeling a spectral image as a BTD tensor, the latent factors have clear physical meaning, and thus prior knowledge about spectral images can be naturally incorporated. Simulations using real hyperspectral images are employed to showcase the effectiveness of the proposed approach with nonnegativity constraints.
Multiple instance learning for sequence data with across bag dependencies

In Multiple Instance Learning (MIL) problem for sequence data, the instances inside the bags are sequences. In some real world applications such as bioinformatics, comparing a random couple of sequences makes no sense. In fact, each instance may have structural and/or functional relations with instances of other bags. Thus, the classification task should take into account this across bag relation. In this work, we present two novel MIL approaches for sequence data classification named ABClass and ABSim. ABClass extracts motifs from related instances and use them to encode sequences. A discriminative classifier is then applied to compute a partial classification result for each set of related sequences. ABSim uses a similarity measure to discriminate the related instances and to compute a scores matrix. For both approaches, an aggregation method is applied in order to generate the final classification result. We applied both approaches to solve the problem of bacterial Ionizing Radiation Resistance prediction. The experimental results of the presented approaches are satisfactory.
Security Management and Prevention on the Campus Network against Hacking

At present, the Internet had got great development in our country.Internet security problem has received widespread attention.After a long period of development, network security technology has become a comprehensive discipline.In our future work and life, the internet is not only a tool, but also a patent only for the minority.It will become a kind of cultural symbol and the way of life into all areas of our lives.Through the analysis of campus network security management of hacking and prevent, this study had put forward some of the techniques and methods against the hackers in the campus network security management.The campus network security management not only could prevent hackers invasion, but also remind the users to pay attention to network security management methods.
Learning Boolean Circuits with Neural Networks

While on some natural distributions, neural-networks are trained efficiently using gradient-based algorithms, it is known that learning them is computationally hard in the worst-case. To separate hard from easy to learn distributions, we observe the property of local correlation: correlation between local patterns of the input and the target label. We focus on learning deep neural-networks using a gradient-based algorithm, when the target function is a tree-structured Boolean circuit. We show that in this case, the existence of correlation between the gates of the circuit and the target label determines whether the optimization succeeds or fails. Using this result, we show that neural-networks can learn the (log n)-parity problem for most product distributions. These results hint that local correlation may play an important role in separating easy/hard to learn distributions. We also obtain a novel depth separation result, in which we show that a shallow network cannot express some functions, while there exists an efficient gradient-based algorithm that can learn the very same functions using a deep network. The negative expressivity result for shallow networks is obtained by a reduction from results in communication complexity, that may be of independent interest.
Hybrid between Genetic Algorithm and Artificial Bee Colony for Key Generation Purpose

Cryptography is a significant tool for guarantee a protected and secured data. In asymmetric key algorithms, the key generation plays an important role for applied the security. This research focus on enhance genetic algorithm's population by using artificial bee colony to generate keys that are unique and random. The five statistical tests ( Frequency test (monobit test), Serial test (two-bit test), Poker test, runs test and autocorrelation test) are used to compute the fitness. The programming language Pythom3 was used in programming the proposed method. The proposed algorithm is acquired higher quality solutions within a reasonable computational time compared to traditional genetic algorithm.
Effect of Learning Goal Orientation on Leadership Development

Effective leadership development has eluded many organizations and academia for a long time. Those who have attempted to improve the practice and study of leadership development focus on the same old elements of leadership development. The traditional elements of leadership development that have been studied and implemented previously include: the content of the leadership development programs, the delivery of leadership development programs, the length of the programs and advocating for leadership development programs that are entrenched in leadership theory. Despite numerous studies and implementation of the studies’ findings on leadership development, there exists widespread dissatisfaction with leadership development outcomes. Some scholars have observed that leader developmental readiness may be the missing ingredient in leadership development. Learning goal orientation is one of the five elements of leader developmental readiness. Therefore, this study examines the effect of learning goal orientation on leadership development. The study reveals that learning goal orientation has a significant effect on leadership development, F(1,286) = 62.346, p &lt; 001. In addition, learning goal orientation accounts for 17.9% of the variation in leadership development. Thus, enhancing participants’ learning goal orientation should be part and parcel of any leadership development program in order to improve the effectiveness of leadership development.
Comparative Analysis of Digital Image Watermarking Based on Lifting Wavelet Transform and Singular Value Decomposition

The various kinds of unauthorized use and modification of data has been constantly increasing day by day and very common due to the enormous growth of multimedia information on the Internet. Thus, authenticity of the data is one of the major issues in the defence data transmission as well as in secured transmission. To overcome this sort of problem, digital watermarking is supposed to be one of the legitimate solutions as it provides possible validation, safe, and secure transmission of secret data. Here, a robust and secure digital image watermarking method for the protection of copyright has been developed which involves LWT in combination with SVD. The latest approach to wavelet transform is LWT while the significant transform technique is SVD and the obtained experimental results are cross-validated by using inverse LWT and SVD. In order to create and insert a digital signature, the signature mechanism has been used after embedding the watermark and the ownership is authenticated before extracting watermarks in this research work. The robustness of the proposed method for several common image processing attacks like Mean, Median, Gaussian, Shear, Rotation, and Crop is checked by computing the two significant performance metrics, namely, PSNR and MSE. Here, the similarity is also checked by determining the NCC values. Finally, the watermarking scheme suggested in this work is compared with various existing research works which show that the algorithm used in the proposed scheme works fairly in terms of PSNR, MSE, and NCC.
Revisiting Multivariate Ring Learning with Errors and its Applications on Lattice-based Cryptography.

The “Multivariate Ring Learning with Errors” problem was presented as a generalization of Ring Learning with Errors (RLWE), introducing efficiency improvements with respect to the RLWE counterpart thanks to its multivariate structure. Nevertheless, the recent attack presented by Bootland, Castryck and Vercauteren has some important consequences on the security of the multivariate RLWE problem with “non-coprime” cyclotomics; this attack transforms instances of m-RLWE with power-of-two cyclotomic polynomials of degree n=∏ini into a set of RLWE samples with dimension maxi{ni}. This is especially devastating for low-degree cyclotomics (e.g., Φ4(x)=1+x2). In this work, we revisit the security of multivariate RLWE and propose new alternative instantiations of the problem that avoid the attack while still preserving the advantages of the multivariate structure, especially when using low-degree polynomials. Additionally, we show how to parameterize these instances in a secure and practical way, therefore enabling constructions and strategies based on m-RLWE that bring notable space and time efficiency improvements over current RLWE-based constructions.
An overview of virtual machine live migration techniques

In a cloud computing the live migration of virtual machines shows a process of moving a running virtual machine from source physical machine to the destination, considering the CPU, memory, network, and storage states. Various performance metrics are tackled such as, downtime, total migration time, performance degradation, and amount of migrated data, which are affected when a virtual machine is migrated. This paper presents an overview and understanding of virtual machine live migration techniques, of the different works in literature that consider this issue, which might impact the work of professionals and researchers to further explore the challenges and provide optimal solutions.
Deformation Vector D of Quantum Density is the Basis of Quantum Gravity

The fact of unification the general theory of relativity (GR) and quantum theory takes place in the theory of Superunification. The Standard Model (SM) is an imperfect model of physics that is incapable of unification with gravity. Physicists have been searching for a new particle such as graviton, strings, Higgs boson and others particles to create the Superunification for many decades. But they could not do this. It was a difficult task and I solved it. I made the theory of Superunification [1]. Quanton is a new four-dimensional particle of time and space which forms the basis of the theory of Superunification. The quantum density of a medium is the concentration of quantons in a unit volume of quantized space-time. The deformation (Einstein's curvature) of quantized space-time is the basis of gravity. The deformation vector D of the quantum density of the medium determines the magnitude and direction of gravitational forces. Gravity occurs when there is a gradient of the quantum density of the medium inside the quantized space-time. The gradient of the quantum density of the medium characterizes the strength of the gravitational field and is described by the deformation vector D. This concerns the creation of artificial gravity (antigravity) forces in the development of non-reactive quantum engines for space. My company has created several types of Leonov's quantum engines that have been successfully tested. The test results of quantum engines were formalized in a protocol and published in scientific journals [1, 2, 3]. A quantum engine is 100 times more efficient than a liquid-propellant rocket engine (LRE). We see the triumph of the theory of Superunification.