Reviewerly/exHarmony huggingface dataset:
Strong Asymmetric PAKE Based on Trapdoor CKEM

Password-Authenticated Key Exchange (PAKE) protocols allow two parties that share a password to establish a shared key in a way that is immune to offline attacks. Asymmetric PAKE (aPAKE) [20] adapts this notion to the common client-server setting, where the server stores a one-way hash of the password instead of the password itself, and server compromise allows the adversary to recover the password only via the (inevitable) offline dictionary attack. Most aPAKE protocols, however, allow an attacker to pre-compute a dictionary of hashed passwords, thus instantly learning the password on server compromise. Recently, Jarecki, Krawczyk, and Xu formalized a Universally Composable strong aPAKE (saPAKE) [23], which requires the password hash to be salted so that the dictionary attack can only start after the server compromise leaks the salt and the salted hash. The UC saPAKE protocol shown in [23], called OPAQUE, uses 3 protocol flows, 3–4 exponentiations per party, and relies on the One-More Diffie-Hellman assumption in ROM. We propose an alternative UC saPAKE construction based on a novel use of the encryption+SPHF paradigm for UC PAKE design [19, 26]. Compared to OPAQUE, our protocol uses only 2 flows, has comparable costs, avoids hashing onto a group, and relies on different assumptions, namely Decisional Diffie-Hellman (DDH), Strong Diffie-Hellman (SDH), and an assumption that the Boneh-Boyen function $$f_ s (x)=g^{1/( s +x)}$$ [9] is a Salted Tight One-Way Function (STOWF). We formalize a UC model for STOWF and analyze the Boneh-Boyen function as UC STOWF in the generic group model and ROM. Our saPAKE protocol employs a new form of Conditional Key Encapsulation Mechanism (CKEM), a generalization of SPHF, which we call an implicit-statement CKEM. This strengthening of SPHF allows for a UC (sa)PAKE design where only the client commits to its password, and only the server performs an SPHF, compared to the standard UC PAKE design paradigm where the encrypt+SPHF subroutine is used symmetrically by both parties.
Dynamic Tree Growth Algorithm for Load Scheduling in Cloud Environments

The cloud computing is an emerging paradigm that enables dynamic provision of elastic, scalable and distributed computer resources to the end-users. One of the most important tasks of the cloud service provider is to deliver services to the end-users in an efficient manner from a finite pool of available physical and virtual resources. The efficiency in terms of both, cost-efficiency and computational-efficiency, can be accomplished through the load scheduling that has significant impact on the overall cloud system performance and represents one of the most important challenges in this domain. In this paper, we introduce two implementations of the original and improved versions of the tree growth algorithm for load scheduling in cloud computing environments. Tree growth algorithm is classified as swarm intelligence metaheuristic, that are able to successfully tackle NP hard problems such as cloud load scheduling. Both algorithms are implemented in the CloudSim environment and comparative analysis with other techniques and metaheuristics for this problem was performed. According to the obtained results, the improved version of the tree growth algorithm outperformed all other techniques and can be successfully applied to load scheduling in cloud systems.
A Lightweight Block Validation Method for Resource-Constrained IoT Devices in Blockchain-Based Applications

Secure access control to a wide variety of Internet of Things (IoT)devices has become critical. Blockchain-based access control frameworks are promising technologies to support secure access to IoT devices in pervasive computing applications. However, in most of the proposed solutions, the IoT devices rely on a trusted server to retrieve critical access control data from the blockchains. We propose a method for IoT devices to validate blockchain data without solely being dependent on a central server. In our approach, several witnesses on the network can be selected randomly by the devices to validate access control information. Our method is aided by Bloom filters, which are shown to be lightweight for resource-constrained devices.
Naive Bayes classifier for infant weight prediction of hypertension mother

Abstract Classification is one method of data analysis in data mining that is used to form a model in order to describe the appropriate data class or model that predicts data trends. The Usage of classification has been applied in various areas, including in health areas. One of the classification methods used is Naive Bayes. This study aims to predict the weight of infants in maternal hypertensive and nonhypertensive conditions with Naive Bayes method. Data were taken as many as 219 data from pregnant women based on the medical record in Obstetrics and Gynecology of Muhammadiyah Palembang Hospital from January 1017 until September 2017. Data is divided into two groups, 188 for training data and 31 data for testing data. The performance data analysis was using WEKA and the results showed that the Naive Bayes’s accuracy is 80.372%. the accuracy score means Naive Bayes works well to predict the weight of infants in maternal hypertensive and nonhypertensive mothers. The result is expected to be a reference for others research by comparing it with other classification methods and incorporating other factors in pregnancy and multiple births or other factors.
Managing Security in Software

Context: Security work in software development is generally under-prioritized. Software developers are not aware of security engineering practices, or find them external to the software development process. To the management, security work presents itself in the form of reactive testing performed out of necessity, incurring only costs in terms of time and resources. The long-term benefits of the security work are more difficult to demonstrate and the security investment harder to justify.
A Hybrid Method for Tracking of Objects by UAVs

Object tracking remains one of the fundamental problems of computer vision since it becomes difficult under some realistic conditions such as fast camera movement, occlusion and similar of objects to the tracked targets. As a real-world application, tracking objects using cameras mounted on unmanned aerial vehicles (UAVs) has become very popular. With the increasing availability of small single board computers with high parallel processing power capabilities, tracking of objects by using onboard computers within UAVs in real-time has become feasible. Although these onboard computers allow a wide variety of computer vision methods to be executed on a UAV, there is still a need to optimize these methods for running time and power consumption. In this paper, we propose a hybrid method for a UAV to detect and track other UAVs efficiently. To detect the target UAV at the beginning of the video and in the case where the tracked UAV has been lost, we use the deep learning-based YOLOv3 and YOLOv3-Tiny models, which provide one of the best trade-offs between speed and accuracy in the literature. To track the detected UAVs in real-time, a kernelized correlation filter is used. Combining these two methods provides high accuracy and speed even on onboard computers. To train the neural nets and test our method, we have collected a new dataset composed of videos of various UAVs in flight, captured from another UAV. The performance of the proposed method has been compared with other state-of-the-art methods in the literature on this dataset. Additionally, we also tested the proposed trackers on aerial videos captured from UAVs. Experimental results show that the proposed hybrid trackers achieve the state-of-the-art performance on all tested datasets. The code is available at https://github.com/bdrhn9/hybrid-tracker.
Continuous Benchmarking: Using System Benchmarking in Build Pipelines

Continuous integration and deployment are established paradigms in modern software engineering. Both intend to ensure the quality of software products and to automate the testing and release process. Today's state of the art, however, focuses on functional tests or small microbenchmarks such as single method performance while the overall quality of service (QoS) is ignored. In this paper, we propose to add a dedicated benchmarking step into the testing and release process which can be used to ensure that QoS goals are met and that new system releases are at least as "good" as the previous ones. For this purpose, we present a research prototype which automatically deploys the system release, runs one or more benchmarks, collects and analyzes results, and decides whether the release fulfills predefined QoS goals. We evaluate our approach by replaying two years of Apache Cassandra's commit history.
Visual search over billions of aerial and satellite images

We present a system for performing visual search over billions of aerial and satellite images. The purpose of visual search is to find images that are visually similar to a query image. We define visual similarity using 512 abstract visual features generated by a convolutional neural network that has been trained on aerial and satellite imagery. The features are converted to binary values to reduce data and compute requirements. We employ a hash-based search using Bigtable, a scalable database service from Google Cloud. Searching the continental United States at 1-meter pixel resolution, corresponding to approximately 2 billion images, takes approximately 0.1 s. This system enables real-time visual search over the surface of the earth, and an interactive demo is available at https://search.descarteslabs.com.
Is the Paper Within Scope? Are You Fishing in the Right Pond?

Outright rejection from the editors' desk, better known as pre-screening or desk-rejection is an unfortunate yet common occurrence in academic peer review. In spite of having merit, many papers are rejected from the desk merely because they are a misfit to the scope of the journal. However, this phenomena costs a considerable time of both the editors and the authors. In this work, we present an investigation towards automation of desk rejection for out-of-scope submissions. We model the problem as a binary classification decision of an article being within scope or outside. We carry our experiments on six different Elsevier Computer Science journals. Our approach based on supervised machine learning outperforms a state-of-the-art by a wide margin in terms of accuracy (at least ~8%). We believe that our proposed method is generic, and with requisite set-up could be applied to articles of other journals. An appropriate system developed with our features could also help prospective authors to check beforehand whether they are submitting to the right venue.
A Method of Visibility Detection Based on the Transfer Learning

Abstract Atmospheric visibility is an important element of meteorological observation. With existing methods, defining image features that reflect visibility accurately and comprehensively is difficult. This paper proposes a visibility detection method based on transfer learning using deep convolutional neural networks (DCNN) that addresses issues caused by a lack of sufficient visibility labeled datasets. In the proposed method, each image was first divided into several subregions, which were encoded to extract visual features using a pretrained no-reference image quality assessment neural network. Then a support vector regression model was trained to map the extracted features to the visibility. The fusion weight of each subregion was evaluated according to the error analysis of the regression model. Finally, the neural network was fine-tuned to better fit the problem of visibility detection using the current detection results conversely. Experimental results demonstrated that the detection accuracy of the proposed method exceeds 90% and satisfies the requirements of daily observation applications.
An Ontology-based Two-Stage Approach to Medical Text Classification with Feature Selection by Particle Swarm Optimisation

Document classification (DC) is the task of assigning pre-defined labels to unseen documents by utilizing a model trained on the available labeled documents. DC has attracted much attention in medical fields recently because many issues can be formulated as a classification problem. It can assist doctors in decision making and correct decisions can reduce the medical expenses. Medical documents have special attributes that distinguish them from other texts and make them difficult to analyze. For example, many acronyms and abbreviations, and short expressions make it more challenging to extract information. The classification accuracy of the current medical DC methods is not satisfactory. The goal of this work is to enhance the input feature sets of the DC method to improve the accuracy. To approach this goal, a novel two-stage approach is proposed. In the first stage, a domain-specific dictionary, namely the Unified Medical Language System (UMLS), is employed to extract the key features belonging to the most relevant concepts such as diseases or symptoms. In the second stage, PSO is applied to select more related features from the extracted features in the first stage. The performance of the proposed approach is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set which is a widely used medical text dataset. The experimental results show substantial improvement by the proposed method on the accuracy of classification.
Set-based Inverse Kinematics Control of an Anthropomorphic Dual Arm Aerial Manipulator

The paper presents a multiple task-priority inverse kinematics algorithm for a dual-arm aerial manipulator.Both tasks defined as equality constraints and inequality constraints are handled by means of a singularity robust method based on the Null-Space based Behavioral control.The proposed schema is constituted by the inverse kinematics control, that receives the desired behavior of the system and outputs the reference values for the motion variables, i.e. the UAV pose and the arm joints position, and a motion control, that computes the vehicle thrusts and the joint torques.The method has been experimentally validated on a system composed by an underactuated aerial hexarotor vehicle equipped with two lightweight 4-DOF manipulators, involved in operations requiring the coordination of the two arms and the vehicle.
Storage Mechanism Optimization in Blockchain System Based on Residual Number System

Huge storage volume is one of the main bottlenecks for the development of blockchain, so how to release the burden by optimizing the storage mechanism has become an important problem. Most of the current solutions would modify the architecture of blockchain, which weakens the characteristics of the decentralization, such as cloud storage. In this paper, a storage optimization mechanism based on residual number system is proposed to reduce the storage volume on each node. In addition, the recovery procedure of CRT-II (The new Chinese Remainder Theorem) is used to detect garbled data from devil nodes, which enable the proposed storage mechanism with strong fault tolerance capability. Both theoretical analysis and simulation results prove the effectiveness and reliability of the proposed scheme.
Real-Time Minimum Snap Trajectory Generation for Quadcopters: Algorithm Speed-up Through Machine Learning

This paper addresses the problem of generating quadcopter minimum snap trajectories for real time applications. Previous efforts addressed this problem by either employing a gradient descent method, or by greatly sacrificing optimality for faster solutions that are amenable for onboard implementation. In this work, outputs of the gradient descent method are used offline to train a supervised neural network. We show that the use of neural networks results typically in two orders of magnitude reduction in computational time. Our proposed approach can be used for warm-starting onboard implementable iterative methods with an “educated ” initial guess. This work is motivated by the application for human-machine interface in which a human provides desired trajectory through a smart-tablet interface, which has to be translated into a dynamically feasible trajectory for a quadcopter. The proposed solution is tested in thousands of different examples, demonstrating its effectiveness as a booster for minimum snap trajectory generation for quadcopters.
Radar Signal Processing for Human Identification by Means of Reservoir Computing Networks

Along with substantial advances in the area of image processing and, consequently, video-based surveillance systems, concerns about preserving the privacy of people have also deepened. Therefore, replacing conventional video cameras in surveillance systems with less-intrusive and yet effective alternatives, such as micro-wave radars, is of high interest. The aim of this work is to explore the application of Reservoir Computing Networks (RCNs) to the problem of identifying a limited number of people in an indoor environment, leveraging gait information captured by micro-wave radar measurements. These measurements are done using a commercial low-power linear frequency-modulated continuous-wave (FMCW) radar. Besides the low quality of the outputs of such a radar sensor, walking spontaneously as opposed to controlled situations adds another level of complexity to the targeted use case. In this context, RCNs are interesting tools, given that they have shown a high effectiveness in capturing temporal information and handling noise, while at the same time being easy to setup and train. Using Micro-Doppler features as inputs, we follow a structured procedure towards optimizing the parameters of our RCN-based approach, showing that RCNs have a great potential in processing the noisy features provided by a low-power radar.
An efficient technique for image compression and quality retrieval using matrix completion

In this paper, an efficient technique for image compression and quality retrieval using matrix completion is presented. The proposed technique is based on low-rank matrix completion using singular value truncation and thresholding. Here, an image is decomposed using singular value decomposition (SVD) to obtain a low rank of image data, which is approximated in compressed form. Later on, singular value thresholding algorithm is exploited to retrieve visual quality of the compressed image. The presented method is easily applicable for various visual characteristics of the image for different compression efficiency. A detailed analysis has been presented to show the efficiency of proposed method in term of compression as well as quality retrieval. It is evident from experimental results that a maximum of 80% compression is achieved with acceptable visual quality as per human vision system (HVS).
Optimal memory-aware backpropagation of deep join networks

Deep learning training memory needs can prevent the user from considering large models and large batch sizes. In this work, we propose to use techniques from memory-aware scheduling and automatic differentiation (AD) to execute a backpropagation graph with a bounded memory requirement at the cost of extra recomputations. The case of a single homogeneous chain, i.e. the case of a network whose stages are all identical and form a chain, is well understood and optimal solutions have been proposed in the AD literature. The networks encountered in practice in the context of deep learning are much more diverse, both in terms of shape and heterogeneity. In this work, we define the class of backpropagation graphs, and extend those on which one can compute in polynomial time a solution that minimizes the total number of recomputations. In particular, we consider join graphs which correspond to models such as siamese or cross-modal networks. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.
A Virtual-real Occlusion Method Based on GPU Acceleration for MR

The mixed reality makes the user's realism strong or weak, depending on the fusion effect of the virtual object and the real scene, and the key factor affecting the virtual-real fusion effect is whether the virtual and real object in the synthetic scene has occlusion consistency. In this paper, a method is proposed that utilizes color and depth information provided by an RGB-D camera to improved occlusion. In this method, based on the GPU, extracting the ROI area where the virtual object is located (the area where the virtual-real occlusion occurs),then introducing the real scence color image as the guide image to modified joint bilateral filtering the depth image to repair the wrong edge information and “black hole” of the depth image;pixel-by-pixel point to determine the depth relationship between the virtual object and real object, to achieve virtual-real occlusion rendering blend;for the still existing “sawtooth artifacts” using delay-coloring only fuzzy local boundary algorithm, to achieve accurate and smoothing edges virtual-real occlusion to enhance visual effects.
ZK-GanDef: A GAN Based Zero Knowledge Adversarial Training Defense for Neural Networks

Neural Network classifiers have been used successfully in a wide range of applications. However, their underlying assumption of attack free environment has been defied by adversarial examples. Researchers tried to develop defenses; however, existing approaches are still far from providing effective solutions to this evolving problem. In this paper, we design a generative adversarial net (GAN) based zero knowledge adversarial training defense, dubbed ZK-GanDef, which does not consume adversarial examples during training. Therefore, ZK-GanDef is not only efficient in training but also adaptive to new adversarial examples. This advantage comes at the cost of small degradation in test accuracy compared to full knowledge approaches. Our experiments show that ZK-GanDef enhances test accuracy on adversarial examples by up-to 49.17% compared to zero knowledge approaches. More importantly, its test accuracy is close to that of the state-of-the-art full knowledge approaches (maximum degradation of 8.46%), while taking much less training time.
Virtual Reality: Whence, How and What For

In the 1990s, the term "virtual reality" brought to mind futuristic scenarios based on sci-fi fantasies popularized in books such as Gibson's (1984) Neuromancer and movies such as the Wachowski brothers' The Matrix. These media classics pointed to and perhaps raised expectations for digital three-dimensional sensory displays rendered by some complex yet to be invented or at least not-quite-ready for prime time hardware.
Optimization of Solar Panel Orientation Considering Temporal Volatility and Scenario-Based Photovoltaic Potential: A Case Study in Seoul National University

University campuses accommodate large numbers of people and are suitable places to organize a microgrid. The solar potential in the university area is estimated and the optimal orientation of solar panels is presented in this study. The optimal orientation is analyzed considering temporal volatility to increase the stability of the grid. Several variables are selected and scenarios are designed to consider various investments and technologies. Scenario-specific photovoltaic potentials were estimated using Geographic Information Systems analysis technology. Analysis of temporal volatility was conducted based on the difference between demand and supply of electricity. Optimal panel orientations were presented according to project objectives, such as highest efficiency or low volatility. As a result, the total potential of the study area was tens to hundreds of GWh/year depending on the scenario. The university has an advantage in hourly volatility, but has some problems in monthly volatility. The optimal orientation varies according to objectives and solar power supply ratio. The results of this study are expected to help researchers and technicians in the solar energy industry and assist in urban planning.