Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk-aware Data Replication to Massively Multi-sites against Widespread Disasters

Immediately after a widespread disaster, critical information such as census registers should be available in the disaster-affected area to confirm people’s safety. However, when a widespread disaster actually happened, such information was not available in the Great East Japan Earthquake of 2011 even though the information were protected in safe and remote site over the Internet. This is because the disaster damages both data and internet access of the area. To enable the information available even immediately after a widespread disaster occurs, we propose Risk-aware Data Replication (RDR), which replicates data to safe and nearby sites in the area without the wide area network. It evaluates the site-to-site risk of data loss, and seeks low-risk sites from massively multiple sites using mathematical programming approach. The result of a simple earthquake simulation shows that RDR improves data availability to more than 10% compared to the case of random selection of replication sites and the computation time is less than several seconds even under the 1350 sites. Our contribution is to provide an organized, effective, and feasible way to decide replication sites based on their disaster risks under massively multiple sites environment.
Improving Accuracy and Scalability of the PC Algorithm by Maximizing P-value 1

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Empirical Assessment of Machine Learning Based Software Defect Prediction Techniques

Automated reliability assessment is essential for systems that entail dynamic adaptation based on runtime mission-specific requirements. One approach along this direction is to monitor and assess the system using machine learning-based software defect prediction techniques. Due to the dynamic nature of software data collected, Instance-based learning algorithms are proposed for the above purposes. To evaluate the accuracy of these methods, the paper presents an empirical analysis of four different real-time software defect data sets using different predictor models. The results show that a combination of 1R and Instance-based learning along with Consistency-based subset evaluation technique provides a relatively better consistency in achieving accurate predictions as compared with other models. No direct relationship is observed between the skewness present in the data sets and the prediction accuracy of these models. Principal Component Analysis (PCA) does not show a consistent advantage in improving the.
A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler for use in Machine Learning

Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, inspired by the neocortex, consists of two primary components, namely the spatial pooler (SP) and the temporal memory (TM). The SP is utilized to map similar inputs into generalized sparse distributed representations (SDRs). Those SDRs are then utilized by the TM, which performs sequence learning and prediction. One challenge with HTM is ensuring that proper SDRs are generated from the SP. If the SDRs are not generalizable, the TM will not be able to make proper predictions. This work focuses on the SP and its corresponding output SDRs. A single unifying mathematical framework was created for the SP. The primary learning mechanism was explored, where a maximum likelihood estimator for determining the degree of permanence update was proposed. The boosting mechanisms were studied and found to only be relevant during the initial few iterations of the network. Observations were made relating HTM to well-known algorithms such as competitive learning and attribute bagging. Methods were provided for using the SP for classification as well as dimensionality reduction. Empirical evidence verified that given the proper parameterizations, the SP may be used for feature learning. Similarity metrics were created for scoring the SDRs produced by the SP. The overlap metric proved that the SP is extremely robust to noise. The SP was able to produce similar outputs for a given input, provided the noise did not cause the input to change classes. This overlap metric was further utilized to create a classifier for novelty detection. The SP proved to be able to withstand more noise than the well-known support vector machine (SVM).
Hierarchical structural approach to improving the browsability of Web search engine results

Web users have been mainly relying on Web search engines to find information of interest on the Web. However, two key issues remain with traditional Web search engines: the browsability of searching results and the capacity of Web coverage. The long ranked list presentation of search results, which is widely adopted by the industry adds a layer of confusion to users, especially when the number of matches returned from search engines can easily exceed ten thousand level. We designed an agent system based on hierarchically structural approach for organizing Web search results coupled with a metasearch approach for Web searching. The metasearch approach would help us extract the best of the Web from a larger Web coverage; and our ontological approach is aimed at providing a mechanism to categorize search results in a semantic hierarchical organization, and allow users to find target information in a progressive and interactive manner.
Online Real time Multiple Spatiotemporal Action Localisation and Prediction on a Single Platform

We present a method for multiple spatiotemporal (S/T) action localisation, classification and early prediction based on a single deep learning platform, able to perform in an online fashion and in real time. Our online action detection pipeline comprises of three main steps. In step one, two end-to-end trainable SSD (Single Shot MultiBox Detector) convolutional neural networks are employed to regress and classify detection boxes in each video frame potentially containing an action of interest, one from optical flow and one from RBG images. In step two, appearance and motion cues are combined by merging the detection boxes and classification scores generated by the two networks. In step three, sequences of detection boxes most likely to be associated with a single action instance, called action tubes, are constructed incrementally and efficiently in an online fashion, allowing the system to perform early action class prediction and spatial localisation in real time. Empirical results on the challenging UCF101 and J-HMDB-21 datasets demonstrate new state-of-the-art results in S/T action localisation on UCF101, even when compared to offline competitors, and superior results on early action label prediction across the board. To the best of our knowledge, ours is the first platform able to perform online spatial and temporal action localisation. The system works in real time (30fps) when computing optical flow incrementally, perform better than real time (52fps) when processing only RBG images.
Polar Codes: Speed of Polarization and Polynomial Gap to Capacity

We prove that, for all binary-input symmetric memory less channels, polar codes enable reliable communication at rates within ε > 0 of the Shannon capacity with a block length, construction complexity, and decoding complexity all bounded by a polynomial in 1/ε. Polar coding gives the first known explicit construction with rigorous proofs of all these properties. We give an elementary proof of the capacity achieving property of polar codes that does not rely on the martingale convergence theorem. As a result, we are able to explicitly show that polar codes can have block length (and consequently also encoding and decoding complexity) that is bounded by a polynomial in the gap to capacity. The generator matrix of such polar codes can be constructed in polynomial time using merging of channel output symbols to reduce the alphabet size of the channels seen at the decoder.
1 A Synthetic Dataset for Multitask Road Scene Understanding

Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation. This is achieved by pushing the network towards learning a robust representation that generalizes well to different atomic tasks. We extend this concept by adding auxiliary tasks, which are of minor relevance for the application, to the set of learned tasks. As a kind of additional regularization, they are expected to boost the performance of the ultimately desired main tasks. To study the proposed approach, we picked vision-based road scene understanding (RSU) as an exemplary application. Since multi-task learning requires specialized datasets, particularly when using extensive sets of tasks, we provide a multi-modal dataset for multi-task RSU, called synMT. More than 2.5 · 105 synthetic images, annotated with 21 different labels, were acquired from the video game Grand Theft Auto V (GTA V). Our proposed deep multi-task CNN architecture was trained on various combination of tasks using synMT. The experiments confirmed that auxiliary tasks can indeed boost network performance, both in terms of final results and training time.
Journal of Digital Forensics, Journal of Digital Forensics, Security and Law Security and Law

Communication between people counts to the most important information of today’s business. As a result, in case of forensic investigations in big companies, analysis of communication data in general and especially email, as the still most widely used business communication platform with an immense and still growing volume, is a typical task in digital forensics. One of the challenges is to identify the relevant communication partners and structures in the suspects surrounding as quickly as possible in order to react appropriately and identify further targets of evaluation. Due to the amount of emails in typical inboxes, reading through all the mails renders impractical. Therefore, forensic investigators need tools that support them in quickly receiving an impression of a suspect’s email communication, identifying the relevant communication partners, and realizing communication patterns in single or even multiple email accounts. We introduce an open source forensic email analysis tool that provides exactly by means of a responsive and interactive graph visualization of email data supported by statistical information
Speech Recognition with No Speech or with Noisy Speech

The performance of automatic speech recognition systems(ASR) degrades in the presence of noisy speech. This paper demonstrates that using electroencephalography (EEG) can help automatic speech recognition systems overcome performance loss in the presence of noise. The paper also shows that distillation training of automatic speech recognition systems using EEG features will increase their performance. Finally, we demonstrate the ability to recognize words from EEG with no speech signal on a limited English vocabulary with high accuracy.
Speech Recognition With No Speech Or With Noisy Speech Beyond English

In this paper we demonstrate continuous noisy speech recognition using connectionist temporal classification (CTC) model on limited Chinese vocabulary using electroencephalography (EEG) features with no speech signal as input and we further demonstrate single CTC model based continuous noisy speech recognition on limited joint English and Chinese vocabulary using EEG features with no speech signal as input. We demonstrate our results using various EEG feature sets recently introduced in [1] as well as we propose a new deep learning architecture in this paper which can perform continuous speech recognition using raw EEG signals on limited joint English and Chinese vocabulary.
Facilitated machine learning for image-based fruit quality assessment in developing countries

Automated image classiﬁcation is a common task for supervised machine learning in food science. An example is the image-based classiﬁcation of the fruit’s external quality or ripeness. For this purpose, deep convolutional neural networks (CNNs) are typically used. These models usually require a large number of labeled training samples and enhanced computational resources. While commercial fruit sorting lines readily meet these requirements, the use of machine learning approaches can be hindered by these prerequisites, especially for smallholder farmers in the developing world. We propose an alternative method based on pre-trained vision transformers (ViTs) that is particularly suitable for domains with low availability of data and limited computational resources. It can be easily implemented with limited resources on a standard device, which can democratize the use of these models for smartphone-based image classiﬁcation in developing countries. We demonstrate the competitiveness of our method by benchmarking two diﬀerent classiﬁcation tasks on domain data sets of banana and apple fruits with well-established CNN approaches. Our method achieves a classiﬁcation accuracy of less than one percent below the best-performing CNN (0.950 vs. 0.958) on a training data set of 3745 images. At the same time, our method is superior when only a small number of labeled training samples is available. It requires three times less data to achieve a 0.90 accuracy compared to CNNs. In addition, visualizations of low-dimensional feature embeddings show that the model used in our study extracts excellent features from unseen data without allocating labels.
Struc 2 vec : learning node representations from structural identity

This is a paper about identifying nodes in graphs that play a similar role based solely on the structure of the graph, for example computing the structural identity of individuals in social networks. That’s nice and all that, but what I personally find most interesting about the paper is the meta-process by which the authors go about learning the latent distributed vectors that capture the thing they’re interested in (structural similarity in this case). Once you’ve got those vectors, you can do vector arithmetic, distance calculations, use them in classifiers and other higher-level learning tasks and so on. As word2vec places semantically similar words close together in space, so we want structurally similar nodes to be close together in space.
FaSTraP: Fast and Safe Trajectory Planner for Flights in Unknown Environments

—Planning high-speed trajectories for UAVs in unknown environments requires extremely fast algorithms able to solve the trajectory generation problem in real-time in order to be able to react quickly to the changing knowledge of the world, but that guarantee safety at all times. The desire of maintaining computational tractability typically leads to optimization problems that do not include the obstacles (collision checks are done on the solutions) or to formulations that use a convex decomposition of the free space and then impose an ad hoc allocation of each interval of the trajectory in a speciﬁc polyhedron. Moreover, safety guarantees are usually obtained by having a local planner that plans a trajectory with a ﬁnal “stop” condition in the free-known space. However, these two decisions typically lead to slow and conservative trajectories. We propose FaSTrap (Fast and Safe Trajectory Planner) to overcome these issues. FasTrap obtains faster trajectories by enabling the local planner to optimize in both free-known and unknown spaces. Safety guarantees are ensured by always having a feasible, safe back-up trajectory in the free-known space at the start of each replanning step. Furthermore, we present a Mixed Integer Quadratic Problem (MIQP) formulation in which the solver can choose the interval allocation and where a heuristics for the time allocation is computed efﬁciently using the result of the previous replanning iteration. This proposed algorithm is tested both in simulation and in real hardware, showing agile ﬂights in unknown cluttered environments.
Soil respiration in tropical seasonal rain forest in Xishuangbanna, SW China

With the static opaque chamber and gas chromatography technique, from January 2003 to January 2004 soil respiration was investigated in a tropical seasonal rain forest in Xishuangbanna, SW China. In this study three treatments were applied, each with three replicates: A (bare soil), B (soil+litter), and C (soil+litter+seedling). The results showed that soil respiration varied seasonally, low from December 2003 to February 2004, and high from June to July 2004. The annual average values of CO2 efflux from soil respiration differed among the treatments at 1% level, with the rank of C (14642 mgCO 2 ·m -2 ·h -1 )>B (12807 mgCO 2 ·m -2 ·h -1 )> A (9532 mgCO 2 ·m -2 ·h -1 ). Diurnal variation in soil respiration was not apparent due to little diurnal temperate change in Xishuangbanna. There was a parabola relationship between soil respiration and soil moisture at 1% level. Soil respiration rates were higher when soil moisture ranged from 35% to 45%. There was an exponential relationship between soil respiration and soil temperature (at a depth of 5cm in mineral soil) at 1% level. The calculated Q 10 values in this study, ranging from 2.03 to 2.36, were very near to those of tropical soil reported. The CO 2 efflux in 2003 was 5.34 kgCO 2 ·m -2 ·a -1 from soil plus litter plus seedling, of them 3.48 kgCO 2 ·m -2 ·a -1 from soil (accounting for 62.5%), 1.19 kgCO 2 ·m -2 ·a -1 from litter (22.3%) and 0.67 kgCO 2 ·m -2 ·a -1 from seedling (12.5%).
Implicit shock tracking using an optimization-based, r-adaptive, high-order discontinuous Galerkin method

A novel framework for resolving discontinuous solutions of conservation laws, e.g., contact lines, shock waves, and interfaces, using implicit tracking and a high-order discontinuous Galerkin (DG) discretization was introduced in [38]. Central to the framework is an optimization problem whose solution is a discontinuity-aligned mesh and the corresponding high-order approximation to the flow that does not require explicit meshing of the unknown discontinuity surface. The method was shown to deliver highly accurate solutions on coarse, high-order discretizations without nonlinear stabilization and recover optimal convergence rates even for problems with discontinuous solutions. This work extends the implicit tracking framework such that robustness is improved and convergence accelerated. In particular, we introduce an improved formulation of the central optimization problem and an associated sequential quadratic programming (SQP) solver. The new error-based objective function penalizes violation of the DG residual in an enriched test space and is shown to have excellent tracking properties. The SQP solver simultaneously converges the nodal coordinates of the mesh and DG solution to their optimal values and is equipped with a number of features to ensure robust, fast convergence: Levenberg-Marquardt approximation of the Hessian with weighted elliptic regularization, backtracking line search, and rigorous convergence criteria. We use the proposed method to solve a range of inviscid conservation laws of varying difficulty. We show the method is able to deliver accurate solutions on coarse, high-order meshes and the SQP solver is robust and usually able to drive the first-order optimality system to tight tolerances.
DERIVING 3D POINT CLOUDS FROM TERRESTRIAL PHOTOGRAPHS - COMPARISON OF DIFFERENT SENSORS AND SOFTWARE

Abstract. Terrestrial photogrammetry nowadays offers a reasonably cheap, intuitive and effective approach to 3D-modelling. However, the important choice, which sensor and which software to use is not straight forward and needs consideration as the choice will have effects on the resulting 3D point cloud and its derivatives. We compare five different sensors as well as four different state-of-the-art software packages for a single application, the modelling of a vegetated rock face. The five sensors represent different resolutions, sensor sizes and price segments of the cameras. The software packages used are: (1) Agisoft PhotoScan Pro (1.16), (2) Pix4D (2.0.89), (3) a combination of Visual SFM (V0.5.22) and SURE (1.2.0.286), and (4) MicMac (1.0). We took photos of a vegetated rock face from identical positions with all sensors. Then we compared the results of the different software packages regarding the ease of the workflow, visual appeal, similarity and quality of the point cloud. While PhotoScan and Pix4D offer the user-friendliest workflows, they are also “black-box” programmes giving only little insight into their processing. Unsatisfying results may only be changed by modifying settings within a module. The combined workflow of Visual SFM, SURE and CloudCompare is just as simple but requires more user interaction. MicMac turned out to be the most challenging software as it is less user-friendly. However, MicMac offers the most possibilities to influence the processing workflow. The resulting point-clouds of PhotoScan and MicMac are the most appealing.
On The Power of Exact Quantum Polynomial Time

We investigate the power of quantum computers when they are required to return an answer that is guaranteed correct after a time that is upper-bounded by a polynomial in the worst case. In an oracle setting, it is shown that such machines can solve problems that would take exponential time on any classical bounded-error probabilistic computer.
Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques

Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, optimum dispatch, etc. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty and granularity in the SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems considering the different models and architectures. Traditional statistical and machine learning-based forecasting methods are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid methods and data pre-processing techniques for better forecasting accuracy is also highlighted. A comparative case study using the Victorian electricity consumption benchmark and American electric power (AEP) datasets is conducted to analyze the performance of different forecasting methods. The analysis demonstrates higher accuracy of the recurrent neural network (RNN) and long-short term memory (LSTM) methods when sample sizes are larger and hyperparameters are appropriately tuned. Furthermore, hybrid methods such as CNN-LSTM are also highly effective to deal with long sequences in energy data.
VS YNC : Push-Button Veriﬁcation and Optimization for Synchronization Primitives on Weak Memory Models (Extended Abstract)

Modern multicore architectures, such as ARM, Power, and RISC-V, follow weak memory models (WMMs) [4, 12, 17, 30], which allow them to execute independent memory operations out of order. WMMs are becoming increasingly pervasive (latest releases from Apple [33], Microsoft [32] and Huawei [20] run on ARM). So, a lot of concurrent software designed for older, fairly strong memory models such as SPARC/x86 TSO [31] needs to be ported to these modern WMMs.