Reviewerly/exHarmony huggingface dataset:
Generating a Random String with a Fixed Weight

Generating, uniformly at random, a binary or a ternary string with a fixed length $$L$$ and a prescribed weight W, is a step in several quantum safe cryptosystems (e. g., BIKE, NTRUEncrypt, NTRU LPrime, Lizard, McEliece). This fixed weight vector selection generation is often implemented via a shuffling method or a rejection method, but not always in “constant time” side channel protected flow. A recently suggested constant time algorithm for this problem, uses Network Sorting and turns out to be quite efficient. This paper proposes a new method for this computation, with a side channel protected implementation. We compare it to the other methods for different combinations of $$L$$ and W values. Our method turns out to be the fastest approach for the cases where $$L$$ is (relatively) short and $$0.1 < W/L\le 0.5$$ . For example, this range falls within the parameters of NTRU LPrime, where our method achieves a $$3\times $$ speedup in the string generation. This leads to an overall $$1.14 \times $$ speedup for the NTRU LPrime key generation.
No-jump-into-latency in China's internet!

Last-mile geo-localization plays an essential role in many location-based services, such as fraud detection and targeted advertising. In this study, we point out that round trip time (RTT) latency shows an extremely weak correlation with physical distance estimation in China's Internet, since a path between a vantage point and a destination can often be circuitous and inflated by queuing and processing delays. To sidestep the latency measurement, we perform a three-tier hop count based IP geo-localization mapping for China's Internet, on the assumption that each provincial router only serves a limited area. The mapping approach begins at the first tier using a single vantage point to fetch large-scale traceroute paths from the server to landmarks and target IPs. At the second tier, we try to find the last common routers along the traceroute paths of targets and landmarks and aggregate their hop count distances. At the third tier, we estimate the physical distances from hop count distances and provincial router radii, and geo-localize the targets to the nearest landmarks. Through large-scale experiments, we show that our approach is both cost-efficient and reliable, and can achieve last-ten-kilometer IP geo-localization for approximately 65% of the total 48874 pingable target IP addresses with a single ping server, and our hop count based approach completely outperforms the RTT based method.
Detection and Tracking of Motorcycles in Congested Urban Environments Using Deep Learning and Markov Decision Processes

This research describes "EspiNet", a Deep Learning Convolutional Neural Network model, in conjunction with a Markov Decision Process (MDP) tracker for detection and tracking of occluded motorcycles in urban environments. The model is trained and evaluated, using a new public dataset with up to 10,000 annotated images, created for this research, and captured in real urban traffic scenes. Images were captured using a moving camera mounted in a drone, where more than 60% of the motorcycles are affected by occlusions. The network design involves many tests, where a promising result of 88.84% in average precision (AP) is achieved, despite the considerable number of occluded vehicles, the movement of the camera and the low angle used for capture. The model predictions are used as input to an MDP tracker, reaching results up to 85.2% in Multiple Object Tracking Accuracy (MOTA). The proposed network architecture outperforms state of the art YOLO (You Look Only Once) v3.0 and Faster R-CNN (VGG16 based) detection models, producing also better tracking results in comparison with the use of the other two models as detector base for the MDP tracker.
Modeling and system analysis of drinking water parameters in urban water supply systems

Abstract The article discusses the use of mathematical modeling and system analysis methods to solve problems of water preparation. Models for predicting indicators of drinking water based on the quality of the source water are proposed. The technological process of deodorization of drinking water is considered. The following system analysis methods were used in the models development: correlation analysis of data, the principal component method, regression modeling, and the least squares method. Hidden relationships between indicators of drinking and source water were identified. The adequacy of the obtained results was proved by comparing them with the actual values. The obtained results approximate well the actual values. The results can be used in water supply systems to improve the quality of drinking water.
A Novel Coalitional Game-Theoretic Approach for Energy-Aware Dynamic VM Consolidation in Heterogeneous Cloud Datacenters

Server consolidation technique plays an important role in energy management and load-balancing of cloud computing systems. Dynamic virtual machine (VM) consolidation is a promising consolidation approach in this direction, which aims at using least active physical machines (PMs) through appropriately migrating VMs to reduce resource consumption. The resulting optimization problem is well-acknowledged to be NP-hard optimization problems. In this paper, we propose a novel merge-and-split-based coalitional game-theoretic approach for VM consolidation in heterogeneous clouds. The proposed approach first partitions PMs into different groups based on their load levels, then employs a coalitional-game-based VM consolidation algorithm (CGMS) in choosing members from such groups to form effective coalitions, performs VM migrations among the coalition members to maximize the payoff of every coalition, and close PMs with low energy-efficiency. Experimental results based on multiple cases clearly demonstrate that our proposed approach outperforms traditional ones in terms of energy-saving and level of load fairness.
Cerebral edema segmentation using textural feature

Diagnostic imaging provides a vital tool in detection and analysis of Brain pathologies. Magnetic resonance imaging (MRI) provides an effective means for non-invasive mapping of anatomy and pathology in the brain. Pathologies like cerebral edema and tumors can spread in different tissues in the brain and can affect cognitive and other functions in the body. Accurate segmentation is therefore a challenging task. Human Brain consists of different soft tissues. These tissues can be characterized using different textures. The work presents an automatic method for segmentation using textural feature of the MR image. The texture of MR image is exploited using the gray co-occurrence matrix (GLCM). GLCM creates a textural feature map by taking into account the spatial dependence of the pixels and its angular relationship between the neighboring cell pairs. Local entropy as second order textural feature is used to capture the texture of MR image. Entropy computes the randomness in pixel intensities and helps in defining a unique texture of edema for segmentation. The marked contrast enhancement obtained in FLAIR sequence of the MR image is captured as textural information by local entropy and GLCM combination. The proposed method obtains a definite textural signature of edema as well as tumor for threshold selection. Experiments on publically available BRATS database yields an average accuracy of 96%, specificity of 97%, sensitivity of 61%, Dice Coefficient as 50% and structural similarity index of 0.88 for edema. The proposed method demonstrates encouraging results in automatic segmentation of edema as well as tumor core.
Electronic System for Detection and Control of Preeclampsia in Pregnant Women

We present the development of an electronic system for detection and monitoring of preeclampsia for pregnant women. Considering the fundamental indicators in the presence of this disorder that can be detected after 20 weeks of gestation, such as hypertension and proteins in the urine. The main objective is to keep a frequent monitoring of the pregnancy of women in pregnancy, with the aim of reducing maternal mortality by detecting possible complications in its initial stage. The proposed electronic system allows: to measure the variables of pressure (mmHg)and proteinuria (mg / dL), keep a record of the measured variables, visualize the data through an interface and generate reports of the stored data. Using WIFI technology, the measured data is sent to a concentrator (PC), which hosts a server that contains a MySQL database for the storage and presentation of the data. When the data is presented in a graphical interface, the system generates a report by patient considering the values of the variables of the last dates, as a result, a promising electronic system that provides assistance to pregnant woman in different situation and circumstances, and especially for women who have fixed jobs with eight-hour work days, because with the help of the implemented device they can be kept in constant monitoring.
Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach

When modeling real world domains we have to deal with information that is incomplete or that comes from sources with different trust levels. This motivates the need for managing uncertainty in the Semantic Web. To this purpose, we introduced a probabilistic semantics, named DISPONTE, in order to combine description logics with probability theory. The probability of a query can be then computed from the set of its explanations by building a Binary Decision Diagram (BDD). The set of explanations can be found using the tableau algorithm, which has to handle non-determinism. Prolog, with its efficient handling of non-determinism, is suitable for implementing the tableau algorithm. TRILL and TRILLP are systems offering a Prolog implementation of the tableau algorithm. TRILLP builds a pinpointing formula, that compactly represents the set of explanations and can be directly translated into a BDD. Both reasoners were shown to outperform state-of-the-art DL reasoners. In this paper, we present an improvement of TRILLP, named TORNADO, in which the BDD is directly built during the construction of the tableau, further speeding up the overall inference process. An experimental comparison shows the effectiveness of TORNADO. All systems can be tried online in the TRILL on SWISH web application at http://trill.ml.unife.it/.
On the Combination of IMU and Optical Flow for Action Recognition

Different Action recognition methods use Inertial Measurement Unit (IMU) and optical flow independently. This research aims to explore the usefulness of combining IMU and Optical flow for action recognition. We are investigating the effectiveness of using statistical features to build an expandable feature vector space.
Searching Objects in a Video Footage

Due to the increase in video content being generated from surveillance cameras and filming, videos analysis becomes imperative. Sometimes it becomes tedious to watch a video captured by a surveillance camera for hours, just to find out the desired footage. Current state of-the-art video analysis methods do not address the problem of searching and localizing a particular object in a video using the name of the object as a query and to return only a segment of the video clip showing the instances of that object. In this research the authors make use of combined implementations from existing work and also applied the dropping frames algorithm to produce a shorter, trimmed video clip showing the target object specified by the search tag. The resulting video is short and specific to the object of interest.
Hybrid Approach to Estimate a Collision-Free Velocity for Autonomous Surface Vehicles

Shipping transportation mode needs to be even more efficient, profitable and secure as more than 80 % of the world's trade is done by sea. Autonomous ships will provide the possibility to eliminate the likelihood of human error, reduce unnecessary crew costs and increase the efficiency of the cargo spaces. Although a significant work is being made, and new algorithms are arising, they are still a mirage and still have some problems regarding safety, autonomy and reliability. This paper proposes an online obstacle avoidance algorithm for Autonomous Surfaces Vehicles (ASVs) introducing the reachability with the protective zone concepts. This method estimates a collision-free velocity based on inner and outer constraints such as, current velocity, direction, maximum speed and turning radius of the vehicle, position and dimensions of the surround obstacles as well as a movement prediction in a close future. A non-restrictive estimative for the speed and direction of the ASV is calculated by mapping a conflict zone, determined by the course of the vehicle and the distance to obstacles that is used to avoid imminent dangerous situations. A set of simulations demonstrates the ability of this method to safely circumvent obstacles in several scenarios with different weather conditions.
Vision and Acceleration Modalities: Partners for Recognizing Complex Activities

Wearable devices have been used widely for human activity recognition in the field of pervasive computing. One big area of in this research is the recognition of activities of daily living where especially inertial and interaction sensors like RFID tags and scanners have been used. An issue that may arise when using interaction sensors is a lack of certainty. A positive signal from an interaction sensor is not necessarily caused by a performed activity e.g, when an object is only touched but no interaction occurred afterwards. In our work, we aim to overcome this limitation and present a multi-modal egocentric-based activity recognition approach which is able to recognize the critical activities by looking at movement and object information at the same time. We present our results of combining inertial and video features to recognize human activities on different types of scenarios where we achieve a F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -measure up to 79.6%.
Automated Support to Capture Creative Requirements via Requirements Reuse

Increasingly competitive software industry, where multiple systems serve the same application domain and compete for customers, favors software with creative features. To promote software creativity, research has proposed multi-day workshops with experienced facilitators, and semi-automated tools to provide a limited support for creative thinking. Such approach is either time-consuming and demands substantial involvement from analysts with creative abilities, or useful only for existing large-scale software with a rich issue tracking system. In this paper, we present a novel framework, useful for both new and existing systems, providing an end-to-end automation to support creativity. In particular, the framework reuses freely available requirements for similar software, leverages state-of-the-art natural language processing and machine learning techniques, and generates candidate creative requirements. We apply the framework on three application domains: Antivirus, Web Browser, and File Sharing, and further report a human subject evaluation. The results demonstrate our framework’s ability to generate creative features and provoke innovative thinking among developers with various experience levels.
Modeling Multidimensional User Preferences for Collaborative Filtering

A popular idea in collaborative filtering is to map users and items to latent vectors in the same Euclidean space and make recommendations based on their inner products. The idea of user/item clustering has also been exploited. However, the possibility of obtaining latent user and item feature vectors from user/item clusters has not been investigated. In this paper, we propose such a method for implicit feedback data. We cluster users along multiple latent dimensions, with each latent dimension being defined by a distinct subset of items. User clustering along a latent dimension results in two soft groups of users: those who have a tendency to consume the corresponding items and those who do not. The first group is called a taste group. As there are multiple latent dimensions, we get multiple taste groups. We map users and items to latent feature vectors based on the taste groups such that the vector for a user tells us what tastes she possesses, and the vector for an item tells us how popular it is for users with various tastes. We call the method Multidimensional User Clustering for Collaborative Filter (MUC-CF). In comparison with other methods, MUC-CF leads to more meaningful latent factors and hence its recommendations are easier to explain. MUC-CF is also scalable and in empirical evaluations, it outperforms state-of-the-art baselines.
Developing a Robust Acquisition System for Fringe Projection Profilometry

Abstract Since Fringe Projection Profilometry (FPP) is an intensity-based coding strategy, it is prone to improper optical setup arrangement, surface texture and reflectance, uneven illumination distribution, among others. These conditions introduce errors in phase retrieval which lead to an inaccurate 3-D reconstruction. In this paper, we describe a dynamic approach toward a robust FPP acquisition in challenging scenes and objects. Our aim is to acquire the best possible fringe pattern image by adjusting the object closer to an ideal system-object setup. We describe the software implementation of our method and the interface design using LabVIEW. Experimental results demonstrate that the proposed method greatly reduces sources of error in 3-D reconstruction.
Simplifying Long Short-Term Memory for Fast Training and Time Series Prediction

Abstract Long short Term Memory(LSTM) has been widely used in sequencial problems. However, for the time series prediction problems, its complex structure limits its running speed and performance. In order to solve this problem, this paper simplified the standard LSTM model by reducing the number of gates and the parameters involved in gates computation. Experiments on univariate data set and multivariate data set show that the proposed simplified model not only has better accuracy, but also has higher running speed.
Multi-Agent neurocognitive models of semantics of spatial localization of events

The purpose of the study is to develop a learning system for internal representation of the events localization space to realize orientation and navigation of autonomous mobile systems. The task of the research is the development of simulation models of the semantics of the event localization space based on multi-agent neurocognitive architectures. The paper proves that the multi-agent neurocognitive architecture is an effective formalism for describing the semantics of the spatial localization of events. Main theoretical foundations have been developed for the simulation of spatial relations using the so-called multi-agent facts, consisting of software agents-concepts, reflecting semantic categories corresponding to parts of speech. It is shown that locative software agents that describe the spatial location of objects and events, forming homogeneous connections, compose the so-called field locations. The latter describes a holistic view of the intellectual agent about the environment. The paper defines conceptual foundations of multi-agent modeling of the semantics of subjective reflexive mapping of the interaction between real objects, space and time.
Is Artificial Intelligence New to Multimedia?

The field of multimedia covers a broad range of research and technologies that aim to develop solutions for individual disciplines as well as multidisciplinary domains. From the beginning, multimedia research has employed Artificial Intelligence (AI) techniques to address various challenges in this area. Specifically, AI has been extensively used for multimedia retrieval, management, and analysis. Over the past few years, multimedia has continued to utilize AI to bring revolutionary advancement in numerous applications and domains, mainly thanks to the recent progress in machine learning algorithms and computing powers. However, many challenges remain, which calls for the need to extend Multimedia AI to tackle these problems and to lead to great opportunities in the near future.
A new device characteristic model generation by machine learning

This paper proposes a method to generate a characteristic model of a new device by machine learning. It is difficult new emerging devices and materials to have exact physical model to fit the behavior because developing physical models is complex and takes a long time. Therefore, we propose a regression method by machine learning as an alternative intermediate step of the physical model, and present guidelines for date preprocessing and proper architecture for efficient learning. It makes it easier and faster to solve general regression problems such as function generation.
Multi-Scale Network with the Deeper and Wider Residual Block for MRI Motion Artifact Correction

Magnetic resonance imaging (MRI) motion artifact is common in clinic which affects the doctor to accurately locate the lesion and diagnose the condition. MRI motion artifact is caused by the physiological movements of the patient while scanning the organ. Most of the current methods do artifact suppression and image restoration on the inverse Fourier transform level. They are neither effective nor efficient and can not be utilized in clinic. In this paper, the method that transfers deep learning into this domain with adopting a novel approach in Multi-scale mechanism for MRI motion artifact correction was proposed. What' more, a newer residual block with the deeper and wider architecture was proposed. With the deeper and wider residual block, the correction effect is greatly improved. The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) were adopted as the evaluation metrics. In short, our model is trainable in an end-to-end network, can be tested in real-time and achieves the state-of-the-art results for MRI motion artifact correction.
A Lightweight Quantum-Safe Security Concept for Wireless Sensor Network Communication

The ubiquitous internetworking of devices in all areas of life is boosted by various trends for instance the Internet of Things. Promising technologies that can be used for such future environments come from Wireless Sensor Networks. It ensures connectivity between distributed, tiny and simple sensor nodes as well as sensor nodes and base stations in order to monitor physical or environmental conditions such as vibrations, temperature or motion. Security plays an increasingly important role in the coming decades in which attacking strategies are becoming more and more sophisticated. Contemporary cryptographic mechanisms face a great threat from quantum computers in the near future and together with Intrusion Detection Systems are hardly applicable on sensors due to strict resource constraints. Thus, in this work a future-proof lightweight and resource-aware security concept for sensor networks with a processing stage permeated filtering mechanism is proposed. A special focus in the concepts evaluation lies on the novel Magic Number filter to mitigate a special kind of Denial-of-Service attack performed on CC1350 LaunchPad ARM Cortex-M3 microcontroller boards.
Automatic Segmentation and Reconstruction of Coronary Arteries Based on Sphere Model and Hessian Matrix using CCTA Images

Abstract Cardiovascular disease (CVD) is the most common cause of death in the world. Accurate coronary arteries segmentation is the basis for heart disease diagnose. But in coronary computed tomography angiography(CCTA) images, the complex structure of coronary vessels and the low contrast of thin vessels cause difficulty in coronary arteries segmentation. In this paper, an automatic segmentation algorithm is proposed to extract vessels from CCTA. Firstly, the hessian matrix is used to enhance vessels in CCTA images, and then, the moving sphere model we proposed is used to extract centerline of coronary arteries. Experimental results showed that the proposed model provides good-quality segmentation.