Reviewerly/exHarmony huggingface dataset:
A Bayesian Framework for Joint Target Tracking, Classification, and Intent Inference

Intent inference has attracted considerable interest for achieving situation awareness in the high-level information fusion community. Different from traditional tracking-then-inference methods for intent inference, a novel scheme for joint target tracking, classification, and intent inference (JTCI) is developed based on the Bayesian framework. The proposed JTCI scheme exploits the dependence of target state on target class and intent by defining intent and class dependent dynamic model sets. Then, the joint target state, intent, and class density are obtained recursively under the assumption, and the kinematic and attribute measurement processes are conditional independent. Finally, simulations about tracking in the air surveillance system are utilized to demonstrate the superiority of the proposed JTCI to the state-of-the-art JTC.
Recognition of Libras Static Alphabet with MyoTM and Multi-Layer Perceptron

A Sign Language is a structured set of corporal gestures used as a communication system, which uses movements of the arm, hand, forearm, facial expressions, and lips movements to ease the communication among deaf and/or hearing people. In Brazil, the official Sign Language is called Libras. This work presents the recognition of static alphabet of Libras (20 letters) using the armband MyoTM and a Multi-Layer Perceptron. MyoTM captures Electromyography signals from forearm and these signals are used to classification. The data were acquired from one male subject, 42 times for each gesture. The signals were segmented in periods of 750 ms using onset technique and 10 features were extract from these segments. The built MLP has one hidden layer, one input layer, and one output layer, trained by the backpropagation algorithm. The number of neurons in hidden layer was tested from 10 to 300 and the best approximation for MLP was 230 neurons. The classification has an accuracy of 91.3 ± 0.5% in training and 81.6 ± 0.9 in the test. Finally, the gestures presented accuracies above 80%, except the gestures ‘L’, ‘R’, and ‘W’.
A Parallel Graph Environment for Real-World Data Analytics Workflows

Economic competitiveness and national security depend increasingly on the insightful analysis of large data sets. The diversity of real-world data sources and analytic workflows impose challenging hardware and software requirements for parallel graph platforms. The irregular nature of graph methods is not supported well by the deep memory hierarchies of conventional distributed systems, requiring new processor and runtime system designs to tolerate memory and synchronization latencies. Moreover, the efficiency of relational table operations and matrix computations are not attainable when data is stored in common graph data structures. In this paper, we present HAGGLE, a high-performance, scalable data analytics platform. The platform's hybrid data model supports a variety of distributed, thread-safe data structures, parallel programming constructs, and persistent and streaming data. An abstract runtime layer enables us to map the stack to conventional, distributed computer systems with accelerators. The runtime uses multithreading, active messages, and data aggregation to hide memory and synchronization latencies on large-scale systems.
Pipeline-integrity: Scaling the use of authenticated data structures up to the cloud

Public cloud storage services are widely adopted for their scalability and low cost. However, delegating the management of the storage has serious implications from the security point of view. We focus on integrity verification of query results based on the use of Authenticated Data Structures (ADS). An ADS enables efficient updates of a cryptographic digest, when data changes, and efficient query verification against this digest. Since, the digest can be updated (and usually signed) exclusively with the intervention of a trusted party, the adoption of this approach is source of a serious performance degradation, in particular when the trusted party is far from the server that stores the ADS. In this paper, we show a protocol for a key–value storage service that provides ADS-enabled integrity-protected queries and updates without impairing scalability, even in the presence of large network latencies between trusted clients and an untrusted server. Our solution complies with the principle of the cloud paradigm in which services should be able to arbitrarily scale with respect to number of clients, requests rates, and data size keeping response time limited. We formally prove that our approach is able to detect server misbehaviour in a setting whose consistency rules are only slightly weaker than those guaranteed by previous results. We provide experimental evidence for the feasibility and scalability of our approach.
The Impact of Sentiment Analysis on Social Media to Assess Customer Satisfaction: Case of Rwanda

Customer satisfaction is an essential area of the industry in this 21st century sometimes as known as the information age. However, the perception of customer expectation remains a problem in today's businesses. The internet has enabled people to spread out their thoughts through Social Media (SM) platforms, forums, news comments, and blogs. Consequently, those platforms are generating exponentially the immense amounts of data. The extraction of opinions from those big data can actively allow to rate organizations, learn the consumer needs, and adjust the business's strategies. This paper presents a concept of building a rating system, using Big Data Analytics (BDA) techniques, that apply the existing Sentiment Analysis (SA) algorithms to gain insight into reviews gathered from SM applications. The system will allow to list the various categories of services and evaluate them based on the obtained the customers' reactions. Also, this study aims to manage a large volume of information to rank the institutions and provide a practical solution for competitive, marketing analysis, and track the improvement of customer satisfaction within both the public and private sectors to boost the excellent service delivery in Rwanda.
Continuous User Authentication by the Classification Method Based on the Dynamic Touchscreen Biometrics

When developing protection mechanisms of the confidential data on mobile devices, a balance of reliability and ease of use must be maintained. Such a balance can be provided by a biometric authentication system, which is quite easy to use while being sufficiently reliable. Introduction of the dynamic biometric and behavioral authentication factors into the system can further improve its reliability keeping the balance. Most smartphones have a touchscreen display, which is proven by the previous studies to be able to capture the dynamic biometric and behavioral characteristics of users' input events. This paper proposes a method of distinguishing a legitimate mobile device user from the intruder by analyzing dynamic biometric and behavioral characteristics of touch screen input events.
Nonparametric estimation of the ROC curve based on the Bernstein polynomial

The receiver operating characteristic (ROC) curve is a graphical representation of the relationship between false positive and true positive rates. It is a widely used statistical tool for describing the accuracy of a diagnostic test. In this paper we adopt Bernstein polynomials to construct the ROC curve estimator. The consistency rate of this estimator is studied. We also obtain explicit expressions for the asymptotic bias and variance and show the improvement of the asymptotic mean squared error compared to that of the classical empirical ROC estimator. Furthermore, the weak convergence of the Bernstein ROC process is established. Simulation studies are conducted to compare our proposed estimator with other popular nonparametric ROC estimators. Finally, the proposed method is illustrated by application to a real data set.
DE-Path: A Differential-Evolution-Based Method for Computing Energy-Minimizing Paths on Surfaces

Computing energy-minimizing paths that are general for different energy forms is a common task in science and engineering. Conventional methods adopt numerical solvers, such as conjugate gradient or quasi-Newton. While these are efficient, the results are highly sensitive with respect to the initial paths. In this paper we develop a method based on differential evolution (DE) for computing optimal solutions. We propose a simple strategy to encode paths and define path operations, such as addition and scalar multiplication, so that the discrete paths can fit into the DE framework. We demonstrate the effectiveness of our method on three applications: (1) computing discrete geodesic paths on surfaces with non-uniform density function; (2) finding a smooth path that follows a given vector field as much as possible; and (3) finding a curve on a terrain with (near-) constant slope.
Video summarisation by deep visual and categorical diversity

The authors propose a video‐summarisation method based on visual and categorical diversities using pre‐trained deep visual and categorical models. Their method extracts visual and categorical features from a pre‐trained deep convolutional network (DCN) and a pre‐trained word‐embedding matrix. Using visual and categorical information they obtain a video diversity estimation, which is used as an importance score to select segments from the input video that best describes it. Their method also allows performing queries during the search process, in this way personalising the resulting video summaries according to the particular intended purposes. The performance of the method is evaluated using different pre‐trained DCN models in order to select the architecture with the best throughput. They then compare it with other state‐of‐the‐art proposals in video summarisation using a data‐driven approach with the public dataset SumMe, which contains annotated videos with per‐fragment importance. The results show that their method outperforms other proposals in most of the examples. As an additional advantage, their method requires a simple and direct implementation that does not require a training stage.
Robust and Transparent Audio Watermarking based on Spread Spectrum in Wavelet Domain

This paper proposes a robust and blind audio watermarking scheme based on spread spectrum in Discrete Wavelet Transform (DWT) domain. For this purpose, the watermarks are embedded in low-frequency coefficients, which are less audible. The main aim is to divide the audio signal into small frames and modify the magnitude of the 6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> level approximation coefficients of DWT using the direct sequence spread spectrum (DSSS) technique. In addition, psychoacoustic model is used for enhancing in imperceptibility, and Savitsky-Golay filter is employed for increasing the extraction accuracy. The experimental results confirm high robustness against the most common attacks, including, re-sampling, requantizing, Gaussian noise addition MP3 compression, and low pass filter without significant perceptual distortion (ODG is higher than-1). The proposed scheme's data payload is about 83 bps.
Extraction of reordering rules for statistical machine translation

Word reordering is an important problem for translation between languages which have different structures such as Subject-Verb-Object and Subject-Object-Verb. This paper presents a statistical method for extraction of linguistic rules using chunk to reorder the output of the baseline statistical ma chine translation system for improved performance. The experiments are based on the TDIL sample tourism corpus of English-Hindi language pair which consists of 1000 sentence pairs out of which 900 sentence pairs are used for training, 50 sentences for tuning and 50 sentences for testing. Finally, the output of the machine translation system, augmented by these rules, is evaluated by using BLEU and NIST metrics. The BLEU score improves by more than 2% in comparison to the baseline SMT system. The results are compared with those of Google translation system which has been trained on a huge corpus. We got a 0.1 point improvement in terms of NIST score, in comparison to Google Translation. Thus, we have comparable results with such a small corpus of 900 sentence pairs for training. This paper is an effort to improve the performance of SMT with a small corpus by using linguistic rules where the rules are automatically generated instead of made by linguist.
Multi-task Stack Propagation for Neural Quality Estimation

Quality estimation is an important task in machine translation that has attracted increased interest in recent years. A key problem in translation-quality estimation is the lack of a sufficient amount of the quality annotated training data. To address this shortcoming, the Predictor-Estimator was proposed recently by introducing “word prediction” as an additional pre-subtask that predicts a current target word with consideration of surrounding source and target contexts, resulting in a two-stage neural model composed of a predictor and an estimator . However, the original Predictor-Estimator is not trained on a continuous stacking model but instead in a cascaded manner that separately trains the predictor from the estimator. In addition, the Predictor-Estimator is trained based on single-task learning only, which uses target-specific quality-estimation data without using other training data that are available from other-level quality-estimation tasks. In this article, we thus propose a multi-task stack propagation , which extensively applies stack propagation to fully train the Predictor-Estimator on a continuous stacking architecture and multi-task learning to enhance the training data from related other-level quality-estimation tasks. Experimental results on WMT17 quality-estimation datasets show that the Predictor-Estimator trained with multi-task stack propagation provides statistically significant improvements over the baseline models. In particular, under an ensemble setting, the proposed multi-task stack propagation leads to state-of-the-art performance at all the sentence/word/phrase levels for WMT17 quality estimation tasks.
Adversary resistant deep neural networks via advanced feature nullification

Deep neural networks (DNNs) have been achieving excellent performance in many learning tasks. However, recent studies reveal that DNNs are vulnerable to adversarial examples. Fortunately, a random feature nullification (RFN) algorithm is proposed to improve the robustness of DNNs against gradient-based adversarial examples. However, experimental results demonstrate that RFN ruins the availability of DNNs in some cases. To explore more efficient feature nullification (FN) algorithms, we theoretically prove that FN can improve the robustness of DNNs. Moreover, sliding window feature nullification (SWFN) and fixed stride feature nullification (FSFN) algorithms are proposed to improve the robustness of DNNs. The experimental results demonstrate that compared to RFN, the proposed algorithms can maintain the availability of DNNs without decreasing its robustness against gradient-based attacks.
A rapid knowledge‐based partial supervision fuzzy c‐means for brain tissue segmentation with CUDA‐enabled GPU machine

Abstract The proposed work aims to quicken the magnetic resonance imaging (MRI) brain tissue segmentation process using knowledge‐based partial supervision fuzzy c‐means (KPSFCM) with graphics processing unit (GPU). The proposed KPSFCM contains three steps: knowledge‐based initialization, modification, and optimization. The knowledge‐based initialization step extracts initial centers from input MR images for KPSFCM using Gaussian‐based histogram smoothing. The modification step changes the membership function of PSFCM, which is guided by the labeled patterns of cerebrospinal fluid portion. Finally, the optimization step is achieved through size‐based optimization (SBO), adjacency‐based optimization (ABO), and parallelism‐based optimization (PBO). SBO and ABO are algorithmic level optimization techniques in central processing unit (CPU), whereas PBO is a hardware level optimization technique implemented in GPU using compute unified device architecture (CUDA). Performance of the KPSFCM is tested with online and clinical datasets. The proposed KPSFCM gives better segmentation accuracy than 14 state‐of‐the‐art‐methods, but computationally expensive. When the optimization techniques (SBO and ABO) were included, the execution time reduces by 13 times in CPU. Finally, the inclusion of PBO yields 19 times faster than the optimized CPU implementation.
Boosting DL Concept Learners

We present a method for boosting relational classifiers of individual resources in the context of the Web of Data. We show how weak classifiers induced by simple concept learners can be enhanced producing strong classification models from training datasets. Even more so the comprehensibility of the model is to some extent preserved as it can be regarded as a sort of concept in disjunctive form. We demonstrate the application of this approach to a weak learner that is easily derived from learners that search a space of hypotheses, requiring an adaptation of the underlying heuristics to take into account weighted training examples. An experimental evaluation on a variety of artificial learning problems and datasets shows that the proposed approach enhances the performance of the basic learners and is competitive, outperforming current concept learning systems.
A data-constrained algorithm for the emulation of long-range turbulence-degraded video

Atmospheric turbulence can cause significant image quality degradation in long-range, ground-to-ground imagery. There is recent interest in characterizing the performance of machine learning algorithms for long-range imaging applications. However, such as task requires a databases of realistic turbulence-degraded imagery. Modeling and simulation provides a reliable, repeatable means of generating long-range data at a substantial cost-savings compared to live field collections. We present updates to the Night Vision Electronic Sensors Directorate (NVESD) Turbulence Simulation algorithm that simulates the effect of turbulence on imagery by imposing realistic blur and distortion on pristine input imagery for a given range, turbulence condition, and optical parameters. Key improvements to the model are: (1) the incorporation of the exact short-exposure atmospheric modulation transfer function into the blurring routine; (2) a random walk algorithm that generates blur and distortion statistics on-the-fly at the characteristic frequency of turbulence degradations. The algorithm is fast and lightweight, computationally-speaking, so as to be scalable to high-performance computing. We perform a qualitative assessment of the results with real field imagery, as well as a quantitative comparison using the structural similarity metric (SSIM).
Novel Unconventional-Active-Jamming Recognition Method for Wideband Radars Based on Visibility Graphs

Radar unconventional active jamming, including unconventional deceptive jamming and barrage jamming, poses a serious threat to wideband radars. This paper proposes an unconventional-active-jamming recognition method for wideband radar. In this method, the visibility algorithm of converting the radar time series into graphs, called visibility graphs, is first given. Then, the visibility graph of the linear-frequency-modulation (LFM) signal is proved to be a regular graph, and the rationality of extracting features on visibility graphs is theoretically explained. Therefore, four features on visibility graphs, average degree, average clustering coefficient, Newman assortativity coefficient, and normalized network-structure entropy, are extracted from visibility graphs. Finally, a random-forests (RF) classifier is chosen for unconventional-active-jamming recognition. Experiment results show that recognition probability was over 90% when the jamming-to-noise ratio (JNR) was above 0 dB.
Persuasive Technologies for Sustainable Smart Cities: The Case of Urban Mobility

In this paper, we study the effectiveness of personalized persuasive interventions to change urban travelers' mobility behavior and nudge them towards more sustainable transport choices. More specifically, we embed a set of persuasive design elements in a route planning application and investigate how they affect users' travel choices. The design elements take into consideration the style, the intensity, the target of persuasive interventions as well as users' characteristics and the trip purpose. Our results show evidence that our proposed approach motivates users on a personal level to change their mobility behavior and make more sustainable choices. Furthermore, by personalizing the persuasive interventions while considering combinations of interventions styles (in our case messages and visualizations) as well as adjusting the intensity of persuasive interventions according to the trip purpose and the transport modes of the routes which the user is nudged to follow, the effects of the persuasive interventions can be increased.
A Novel Approach for Dealing with Missing Values in Machine Learning Datasets with Discrete Values

One of the problems that faces machine learning researchers is the incomplete datasets with missing values, knowing that most machine learning algorithms deal with complete datasets. ILA is one of these algorithms which deal only with datasets with complete discrete values. In this paper, a novel approach for dealing with missing values has been developed and tailored with ILA where the treatment of missing values is performed during the induction process. The proposed system is called ILA4. ILA4 has been tested on several datasets with different percentages of missing values. Its results also compared with some common methods for treating missing values. The results show that most of the time, the results ILA4 appear to be comparable to the best cases of some other well-known methods for dealing with missing values problem, namely; the most common value, the most common value restricted to a concept, and delete strategy.
The Adaptive Avionics Platform

Current avionics systems are composed of standardized components. Configuring these components requires millions of configuration parameters. The creation of configurations requires a lot of time and effort in the avionics development process. The AAP consists of standardized modules for computing and peripheral interaction that are self-configuring and fault tolerant. During an organizational phase, the actual topology is detected, the applications are distributed, and all message routes are configured. With an ACMS, the feasibility of the AAP is shown. The ACMS utilizes COTS components, POSIX-API, and Ethernet. In 2.5 min., it is able to configure itself. Moreover, it can withstand a single hardware fault in CPMs, I0Ms, and the network without extra effort. Due to the lack of a reconfigurable certifiable operating system, missing hard real-time capabilities and certifiability are the greatest drawback that will be addressed in the future. The aim is to enable self-configuring and self-fault-tolerant avionics in the safety-critical domain up to DAL A.