Reviewerly/exHarmony huggingface dataset:
An Efficient Artificial Intelligence Framework for UAV Systems

The recent breakthrough of artificial intelligence (AI) in many fields has recently shown its impact on drone technology as well. However, most of the provided solutions either entirely rely on commercial software or provide a weak integration interface which denies the development of additional techniques. This leads us to propose a novel and efficient frame-work for the drone technology. Specifically, we introduce the multi-layer AI (MLAI) framework which allows easy integration of ad-hoc AI applications. To demonstrate the benefits of the proposed framework, we implemented deep learning models to track and detect objects based on MLAI.
The Autoencoder-Kalman Filter: Theory and Practice

Given a noisy signal, it is often of interest to estimate its noise-free state. One of the more common state estimation techniques is the Kalman Filter, which is optimal under certain conditions, one of which is that the measurements are a Gaussian random process with known covariance R. However, in practical applications the covariance may not be known or the noise process may not be Gaussian. Accordingly, here we employ a method called the Autoencoder-Kalman Filter (AEKF) to learn a mapping from noisy measurements to inputs for the Kalman Filter. Training the AEKF uses a technique called domain randomization and the AEKF has been shown to outperform the Kalman Filter and a LSTM neural network on a variety of noise types. However, up to this point the training has been limited to specific classes of functions. In this research, utilizing domain randomization, we propose to train an AEKF on random polynomial functions, which is able to accurately filter a larger class of smooth curves with multiple noise types.
Secure VM management with strong user binding in semi-trusted clouds

Abstract In Infrastructure-as-a-Service (IaaS) clouds, remote users access provided virtual machines (VMs) via the management server. The management server is managed by cloud operators, but not all the cloud operators are trusted in semi-trusted clouds. They can execute arbitrary management commands to users’ VMs and redirect users’ commands to malicious VMs. We call the latter attack the VM redirection attack . The root cause is that the binding of remote users to their VMs is weak. In other words, it is difficult to enforce the execution of only users’ management commands to their VMs. In this paper, we propose UVBond for strongly binding users to their VMs to address this issue. UVBond boots user’s VM by decrypting its encrypted disk inside the trusted hypervisor. Then it issues a VM descriptor to securely identify that VM. To bridge the semantic gap between high-level management commands and low-level hypercalls, UVBond uses hypercall automata , which accept the sequences of hypercalls issued by commands. We have implemented UVBond in Xen and created hypercall automata for various management commands. Using UVBond, we confirmed that a VM descriptor and hypercall automata prevented insider attacks and that the overhead was not large in remote VM management.
Improvement of job completion time in data-intensive cloud computing applications

Abstract Task stragglers in MapReduce jobs dramatically impede job execution of data-intensive computing in cloud data centers. This impedance is due to the uneven distribution of input data, heterogeneous data nodes, resource contention situations, and network configurations. Data skew of intermediate data in MapReduce job causes delay failures due to the violation of job completion time. Data-intensive computing frameworks, such as MapReduce or Hadoop YARN, employ HashPartitioner. This partitioner may cause intermediate data skew, which results in straggler reducers. In this paper, we strive to make Hadoop YARN more efficient in cloud environments. We present, a new partitioning scheme, called balanced data clusters partitioner (BDCP), to handle straggler Reduce tasks based on sampling of input data and feedback information about the current processing task. Our extensive experimental results show that BDCP can outperform the default Hadoop HashPartitioner and Range partitioner. BDCP can assist in straggler mitigation during reduce phase and minimize the job completion time in MapReduce jobs within data-intensive cloud computing.
Transitioning to Agile—In a Large Organization

Change is difficult. A big organization that is set in their old ways of doing things will have big resistance to change. Transitioning to agile is one such change. There are several problems an enterprise would face when transitioning to agile from waterfall software development methodology. It might be as trivial as a team not being colocated, or it could be complete lack of trust between the development team and business partners. In this article, I will describe various problems that my team and I faced when we transitioned to agile in my previous organization and how we successfully overcame those problems and became a model team.
Prediction of Cloud Movement from Satellite Images Using Neural Networks

Predicting cloud movement and dynamics is an important aspect in several areas, including prediction of solar energy generation.Knowing where a cloud will be or how it evolves over a given geographical area can help energy providers to better estimate their production levels.In this paper we propose a novel approach to predicting cloud movement based on satellite imagery.It combines techniques of generating motion vectors from sequential images with neural networks.First, the images are masked to isolate cloud pixels, then Farneback's version of the Optical Flow algorithm is used to detect motion from one image to the next and generate motion vector flow for each pair of images.After that, a feed forward back propagation neural network is trained with the vector data derived from the dataset imagery.Different parameters for the duration of the training, size of the input, and the neighborhood radius of one point in the scene are used.Promising results are presented and discussed to weight the potential of the proposed algorithm for forecasting cloud cover and cloud position in a scene.
Network video summarization based on key frame extraction via superpixel segmentation

Abstract The spread of insecure online video has been a serious social problem. The video summarization becomes one of key step for automatic filtering the expected video from the Internet. At present, the most existing video summarization methods are based on calculating the image similarity between video frames, so that the key frame can be selected properly. In this article, we introduce a superpixel segmentation based image similarity calculation, and then the metric is applied into video summarization. To identify the video key frames, we introduce superpixel segmentation to cluster the pixels locally by estimating the optical flow displacement field between successive frames, which can extract key frames and reduce video redundancy. On the VSUMM dataset and YouTube dataset, the experimental results demonstrate that the proposed method has clear advantages on both subjectively qualitative analysis and objectively quantitative evaluation comparing with the state of the art methods.
Systematicity of students’ independent work in the course of operating systems

The paper is devoted to the study of systematicity of students’ learning activity as a parameter of student’s model, and influence of systematicity at learning results in the course “Operating Systems”. The necessity to equip the student himself as the subject of the educational process with the skills and appropriate pedagogical forecasting tools for independent choice of the appropriate variant of educational activity is shown as theoretical framework. Parameters of models in such pedagogical diagnostics system are suggested and discussed. Empirical work has been realised on the base of learning management system Moodle and give possibility to analyse correlation between timeliness of completing the learning tasks by students and their educational achievements as well as to analyse the structure of students’ time planning at homework. Recommendations to improve the educational process have been suggested
Reciprocal Crowdsourcing: Building Cooperative Game Worlds on Blockchain

Crowd intelligence has become increasingly popular during recent years, thanks to the exploding global Internet usage. However, most crowd intelligence platforms ceased to operate due to the untrustworthy data or lack of motivated participants. The blockchain technologies propose decentralization as the potential solution to this dilemma. This work introduces reciprocal crowdsourcing, a novel decentralized cooperative crowdsourcing model powered by the blockchain to strengthen trust among crowd intelligence participants, who perform transparent collaborative work in the system thereafter. To validate our proposal, we implemented “Cell Evolution”, a blockchain game, in which the players can build cooperative game worlds on the blockchain with reciprocal crowdsourcing.
Ship Detection Based on RetinaNet-Plus for High-Resolution SAR Imagery

Ship detection in high-resolution synthetic aperture radar (SAR) imagery is a fundamental and challenging problem due to the complex environments. In this paper, a RetinaNet-Plus method is presented for ship detection in high-resolution SAR imagery based on RetinaNet network modified. In this approach, instead of setting the score for neighboring region proposals to zero as in Non-Maximum Suppression (NMS), Soft-NMS decreases the detection scores as an increasing function of overlap to avoid loss of precision. In addition, focal loss is used to address the class imbalance and to increase the importance of the hard examples during training. The experiments on SAR ship SSDD dataset and TerraSAR-X image from Barcelona port, show that our method is more accurate than the existing algorithms and is effective for ship detection of high-resolution SAR imagery.
Augmented Reality with Application in Physical Rehabilitation

Gait training is a significant part of physical rehabilitation for patients that suffer stroke events. AR (Augmented Reality) represents a promissory solution for training assessment providing information to the patients and physiotherapists about exercises to be done and the reached results. The paper presents an IOS application developed in unity 3D for stroke patients, immersing patients in a mixed environment that combine real-world and virtual objects. The human computer interface is materialized by an iPhone, head-mounted 3D display and a set of wireless sensors for physiological and motion parameters measurement. The position and velocity of the patient during the gait training is recorded by a smart carpet that includes capacitive sensors connected to a computation unit characterized by Wi-Fi communication capabilities. Several AR training scenarios and the corresponding experimental results are included in the paper.
Resilient and Efficient Blockchain Consensus Protocol for Internet-of-Things

The rapid advance of Blockchain has been used for the Internet-of-Things to provide reliable distributed infrastructure and applications without a centralized authority. This paper aims to create a new consensus protocol that can be efficiently and resiliently used for the Internet-of-Things based on the Practical Byzantine Fault Tolerance algorithm (PBFT), which is critical for IoT scalability and reliability. The traditional PBFT algorithm fails to provide two significant features: a) resiliency against denial of service (DoS) attacks, and b) high-throughput to quickly reach final consensus when under attack. This paper analyzes the current implementation of PBFT consensus protocol and proposes a new secure approach for achieving consensus by adding the properties of random hash generation and threshold comparison in order to defend against DoS attacks.
Tracking Multiple Objects with Multimodal Dependent Measurements: Bayesian Nonparametric Modeling

We address the problem of multi-sensor multi-object tracking with unknown state parameter information. We develop algorithms capable of dealing with unknown time-dependent object and measurement cardinality and object identities. Additionally, given the dependent observations from sensors, our model takes advantage of the additional information to improve tracking performance. We robustly estimate the evolving objects and measurement cardinality with use of nonparametric modeling. In particular, we employ a dependent Dirichlet process to provide a prior on the time-varying object state distributions, and a hierarchical Dirichlet process (HDP) mixture to model measurement dependency. We demonstrate through simulations that providing multimodal dependent measurements the proposed method can accurately estimate the trajectory of objects and robustly determine the time-dependent cardinality. We also compare this method to the dependent Dirichlet process evolutionary Markov modeling (DDP-EMM) and demonstrate its improved tracking performance.
Optimizing Investments in Cyber Hygiene for Protecting Healthcare Users

Cyber hygiene measures are often recommended for strengthening an organization’s security posture, especially for protecting against social engineering attacks that target the human element. However, the related recommendations are typically the same for all organizations and their employees, regardless of the nature and the level of risk for different groups of users. Building upon an existing cybersecurity investment model, this paper presents a tool for optimal selection of cyber hygiene safeguards, which we refer as the Optimal Safeguards Tool (OST). The model combines game theory and combinatorial optimization (0-1 Knapsack) taking into account the probability of each user group to being attacked, the value of assets accessible by each group, and the efficacy of each control for a particular group. The model considers indirect cost as the time employees could require for learning and trainning against an implemented control. Utilizing a game-theoretic framework to support the Knapsack optimization problem permits us to optimally select safeguards’ application levels minimizing the aggregated expected damage within a security investment budget. We evaluate OST in a healthcare domain use case. In particular, on the Critical Internet Security (CIS) Control group 17 for implementing security awareness and training programs for employees belonging to the ICT, clinical and administration personnel of a hospital. We compare the strategies implemented by OST against alternative common-sense defending approaches for three different types of attackers: Nash, Weighted and Opportunistic. Our results show that Nash defending strategies are consistently better than the competing strategies for all attacker types with a minor exception where the Nash defending strategy, for a specific game, performs at least as good as other common-sense approaches. Finally, we illustrate the alternative investment strategies on different Nash equilibria (called plans) and discuss the optimal choice using the framework of 0-1 Knapsack optimization.
Minimizing Vehicle Re-Identification Dataset Bias Using Effective Data Augmentation Method

Datasets are the important part of vehicle re-identification (re-id) research. The dataset which represents real world environment is crucial to vehicle re-id steps such as learning visual features, vehicle detection, examining performance of vehicle re-id algorithms, and so on. Often vehicle re-id datasets lacks in this context. In this paper, firstly, we investigate the vehicle re-id datasets bias problem using deep CNN model inception-v3 (Dataset classification). Dataset classification results indicates that current available vehicle re-id datasets are highly biased. Secondly, we present novel data augmentation technique to mitigate this issue by inserting additional type of variability in training set. Extensive experimental results shows that our approach can be helpful to minimize training set bias. Consequently, cross dataset vehicle re-id performance improves.
Towards a Model-Driven Product Line Engineering Process

Many organizations developing software-intensive systems face challenges with high product complexity and large numbers of variants. In order to effectively maintain and develop these product variants, Product-Line Engineering methods are often considered, while Model-based Systems Engineering practices are commonly utilized to tackle product complexity. In this paper, we report on an industrial case study concerning the ongoing adoption of Product Line Engineering in the Model-based Systems Engineering environment at Volvo Construction Equipment (Volvo CE) in Sweden. In the study, we identify and define a Product Line Engineering process that is aligned with Model-based Systems Engineering activities at the engines control department of Volvo CE. Furthermore, we discuss the implications of the migration from the current development process to a Model-based Product Line Engineering-oriented process. This process, and its implications, are derived by conducting and analyzing interviews with Volvo CE employees, inspecting artifacts and documents, and by means of participant observation. Based on the results of a first system model iteration, we were able to document how Model-based Systems Engineering and variability modeling will affect development activities, work products and stakeholders of the work products.
Kernel dependence analysis and graph structure morphing for novelty detection with high-dimensional small size data set

In this study, we propose a new approach for novelty detection that uses kernel dependence techniques for characterizing the statistical dependencies of random variables (RV) and use this characterization as a basis for making inference. Considering the statistical dependencies of the RVs in multivariate problems is an important challenge in novelty detection. Ignoring these dependencies, when they are strong, may result in inaccurate inference, usually in the form of high false positive rates. Previously studied methods, such as graphical models or conditional classifiers, mainly use density estimation techniques as their main learning element to characterize the dependencies of the relevant RVs. Therefore, they suffer from the curse of dimensionality which makes them unable to handle high-dimensional problems. The proposed method, however, avoids using density estimation methods, and rather, employs a kernel method, which is robust with respect to dimensionality, to encode the dependencies and hence, it can handle problems with arbitrarily high-dimensional data. Furthermore, the proposed method does not need any prior information about the dependence structure of the RVs; thus, it is applicable to general novelty detection problems with no simplifying assumption. To test the performance of the proposed method, we apply it to realistic application problems for analyzing sensor networks and compare the results to those obtained by peer methods.
AI-Powered Lethal Autonomous Weapon Systems in Defence Transformation. Impact and Challenges

Robotics and artificial intelligence (AI) are changing business, and transforming defense and warfare. In particular, the paper addresses the challenges from the easy availability of AI resources to be applied to drones that are already an asymmetric threat, and the issues concerning the Lethal Autonomous Weapons Systems (LAWS) that promise military advantages like the reduction of deployed soldiers, loss of civilians, damage of infrastructures, and the rapid support to decision-making. Autonomous systems can also help in disaster and humanitarian operations. However, the deployment of systems that are more and more autonomous from humans poses multidimensional challenges for their certification, compliance with ethics and international humanitarian law and present risks to security. Mechanisms for the mitigation of their proliferation to malicious non-state actors and a meaningful human control should be implemented. War gaming in a theoretical, risk-safe environment can assess the threats and impact of robotic weapons on future defense planning and operations. From the example of the lessons learned in the automotive industry, simulation can be applied to digital twin models of the AI system and the environment to train and test under unexpected and extreme conditions, unanticipated hardware and software failure situations and in complex dynamic operational contexts including interaction with civilian entities.
Combiners for Functional Encryption, Unconditionally

Functional encryption (FE) combiners allow one to combine many candidates for a functional encryption scheme, possibly based on different computational assumptions, into another functional encryption candidate with the guarantee that the resulting candidate is secure as long as at least one of the original candidates is secure. The fundamental question in this area is whether FE combiners exist. There have been a series of works Ananth et al. (CRYPTO ’16), Ananth-Jain-Sahai (EUROCRYPT ’17), Ananth et al. (TCC ’19) on constructing FE combiners from various assumptions. We give the first unconditional construction of combiners for functional encryption, resolving this question completely. Our construction immediately implies an unconditional universal functional encryption scheme, an FE scheme that is secure if such an FE scheme exists. Previously such results either relied on algebraic assumptions or required subexponential security assumptions.
Affine equivalence for quadratic rotation symmetric Boolean functions

Let $$f_n(x_0, x_1, \ldots , x_{n-1})$$ denote the algebraic normal form (polynomial form) of a rotation symmetric (RS) Boolean function of degree d in $$n \ge d$$ variables and let $$wt(f_n)$$ denote the Hamming weight of this function. Let $$(0, a_1, \ldots , a_{d-1})_n$$ denote the function $$f_n$$ of degree d in n variables generated by the monomial $$x_0x_{a_1} \ldots x_{a_{d-1}}.$$ Such a function $$f_n$$ is called monomial rotation symmetric (MRS). It was proved in a 2012 paper that for any MRS $$f_n$$ with $$d=3,$$ the sequence of weights $$\{w_k = wt(f_k):~k = 3, 4, \ldots \}$$ satisfies a homogeneous linear recursion with integer coefficients. This result was gradually generalized in the following years, culminating around 2016 with the proof that such recursions exist for any rotation symmetric function $$f_n.$$ Recursions for quadratic RS functions were not explicitly considered, since a 2009 paper had already shown that the quadratic weights themselves could be given by an explicit formula. However, this formula is not easy to compute for a typical quadratic function. This paper shows that the weight recursions for the quadratic RS functions have an interesting special form which can be exploited to solve various problems about these functions, for example, deciding exactly which quadratic RS functions are balanced.
Building a Product Origins Tracking System Based on Blockchain and PoA Consensus Protocol

In recent years, the traceability of product origins is strongly concerned, particularly for food products as they directly influence human health. Therefore, there have been some efforts to develop product origins tracking systems. In this paper, we propose an approach to building a supply chain management system based on the blockchain technology for agriculture product origins tracking. The supply chain model is borrowed from Walmart's and it is implemented based on the Ethereum framework using the PoA (Proof of Authority) consensus algorithm. Our experiment shows that the proposed system not only fulfills the requirements of a product origins tracking but also takes the advantages of the blockchain technology such as the immutability and security of data, the low cost in making the transactions, and so on.