Reviewerly/exHarmony huggingface dataset:
Encoder-Decoder Network with Cross-Match Mechanism for Answer Selection

Answer selection (AS) is an important subtask of question answering (QA) that aims to choose the most suitable answer from a list of candidate answers. Existing AS models usually explored the single-scale sentence matching, whereas a sentence might contain semantic information at different scales, e.g. Word-level, Phrase-level, or the whole sentence. In addition, these models typically use fixed-size feature vectors to represent questions and answers, which may cause information loss when questions or answers are too long. To address these issues, we propose an Encoder-Decoder Network with Cross-Match Mechanism (EDCMN) where questions and answers that represented by feature vectors with fixed-size and dynamic-size are applied for multiple-perspective matching. In this model, Encoder layer is based on the "Siamese" network and Decoder layer is based on the "matching-aggregation" network. We evaluate our model on two tasks: Answer Selection and Textual Entailment. Experimental results show the effectiveness of our model, which achieves the state-of-the-art performance on WikiQA dataset.
Local Statistical Active Contour Energy Functional based on Cauchy-Schwarz Divergence for Image Segmentation

Active contour model is a widely used method for image segmentation. However, traditional active contour models often fail to segment objects in images with intensity inhomogeneity. In this paper, a local statistical active contour energy functional is proposed for image segmentation. It employs Cauchy-Schwarz divergence as the statistical distance measuring the difference between the estimated probability density inside and outside the evolving contour, which extracts local intensity information to guide the evolution of the contour. The devised energy functional can well integrate with traditional active contour model, thus it can help achieve improvement for traditional active contour models when they are used for segmenting objects out of intensity inhomogeneous backgrounds. Experimental results on synthetic and real images show that the proposed local energy functional improves the performance of traditional active contour models in images with intensity inhomogeneity.
Electronic Data in Air Traffic Controller Environment as a Workload Aspect

Human factor as a part of Aviation environment is playing a vital role in Air traffic flow organization. Workload of the controller position is continuously highly influenced by a modern technology implementation. One of domain, where the workload is reduced significantly by usage of an Information technology is area of an external and internal information flow. Effective and user friendly system for the electronic Air Traffic services (ATS) data exchange is oriented on the notification, coordination and transfer of flights between ATS units is main precondition of the safe and effective aviation. The time associated with data exchange is a legitimate reason for exploring this activity in conjunction with workload. Electronic data exchange is always associated with coordination of air traffic in areas of common interest. Analyzing the time in favor of this activity can bring important insights into changing the current system, especially in military aviation. The results of this survey can be beneficial to addressing the workload reduction.
Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task

Statistical learning (SL) involving sensitivity to distributional regularities in the environment has been suggested to be an important factor in many aspects of cognition, including language. However, the degree to which statistically-learned information is retained over time is not well understood. To establish whether or not learners are able to preserve such regularities over time, we examined performance on an artificial second language learning task both immediately after training and also at a follow-up session 2 weeks later. Participants were exposed to an artificial language (Brocanto2), half of them receiving simplified training items in which only 20% of sequences contained complex structures, whereas the other half were exposed to a training set in which 80% of the items were composed of complex sequences. Overall, participants showed signs of learning at the first session and retention at the second, but the degree of learning was affected by the nature of the training they received. Participants exposed to the simplified input outperformed those in the more complex training condition. A GLMM was used to model the relationship between stimulus properties and participants' endorsement strategies across both sessions. The results indicate that participants in the complex training condition relied more on an item's chunk strength than those in the simple training condition. Taken together, this set of findings shows that statistically learned regularities are retained over the course of 2 weeks. The results also demonstrate that training on input featuring simple items leads to improved learning and retention of grammatical regularities.
Achieving Accelerometer Wrist and Orientation Invariance in Physical Activity Classification via Domain Adaption

Purpose : Physical activity classifiers are typically trained on data obtained from sensors at a set orientation. Changes in this orientation (such as being on a different wrist) result in performance degradation. This work investigates a method to obtain sensor location and orientation invariance for classification of wrist-mounted accelerometry via a technique known as domain adaption. Methods : Data was gathered from 16 participants who wore accelerometers on both wrists. Physical activity classification models were created using data from each wrist and then used to predict activities when using data from the opposing wrist. Using subspace alignment domain adaption, this procedure was then repeated to align the training and testing data before the classification stage. Results : Prediction of activity when using data where the wearer’s wrist was incorrectly specified resulted in a significant ( p = .01) decrease in performance of 12%. When using domain adaption this drop in performance became negligible ( M difference &lt; 1%, p = .73). Conclusion : Domain adaption is a valuable method for achieving accurate physical activity classification independent of sensor orientation in wrist-worn accelerometry.
Accurate estimation of temperature distributions for IR signature monitoring with a dynamic thermal model and data assimilation

For infrared signature monitoring, knowledge on the surface temperature distribution of an object is required. TNO has developed a model to estimate the real-time evolution of the surface temperature distribution of an object that requires only sensor data from a standard weather station as an input. First, heat fluxes from and to surface elements are estimated based on these meteorological data. The dynamic heat balance is then solved numerically with the Finite Element Method. Radiation exchange, convection, heat conduction between surface facets and shadowing are all taken into account in the model. The model results were compared to half a year of thermocouple data of a CUBI test-object. The temperatures of all facets of the CUBI could be estimated with a diurnal root mean squared error smaller than 2 C. Similar accuracy was achieved when facets of the CUBI were cooled down or heated because they were rotated towards or from the sun. The performance of the model is mainly limited by estimates of the solar irradiance on tilted surfaces. Data assimilation techniques were used to further improve the estimates of the real-time CUBI surface temperature distribution. It is demonstrated that estimates of the temperature distribution of the CUBI are significantly improved when the data of a limited number of thermocouples is used to update the model results with a Kalman filter.
Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation Using Neural Network Classifier

This paper inspects a structure for search-based-face annotation using mining weakly labeled web facial images. Facial photographs are candidly present on the Internet, from that a few facial photographs are properly labeled, however, some of them are not correctly labeled. These facial photographs are repeatedly incomplete and noisy. For enhancing tag quality of weakly net facial photographs, ULR approach is also advantageous for cleansing or filtering the tags of net facial photographs (Wang et al. in IEEE Trans Knowl Data Eng 26, [1]). Big headache issue for search-based face annotation scheme is, whenever the given test facial portrait is not a common person, there are no much more same facial photographs present on the web. A supervised appropriate name tag can be given to a test face portrait by employing face annotation using search-based paradigm, but it also increases the efficiency and scalability. The supervised neural network classifier approach is looking to optimize the tag quality of face portrait by majority voting against the face annotation by search-based paradigm.
Towards Universal Evaluation of Image Annotation Interfaces

To guide the design of interactive image annotation systems that generalize to new domains and applications, we need ways to evaluate the capabilities of new annotation tools across a range of different types of image, content, and task domains. In this work, we introduce Corsica, a test harness for image an- notation tools that uses calibration images to evaluate a tool's capabilities on general image properties and task requirements. Corsica is comprised of sets of three key components: 1) synthesized images with visual elements that are not domain- specific, 2) target microtasks that connects the visual elements and tools for evaluation, and 3) ground truth data for each mi- crotask and visual element pair. By introducing a specification for calibration images and microtasks, we aim to create an evolving repository that allows the community to propose new evaluation challenges. Our work aims to help facilitate the robust verification of image annotation tools and techniques.
Distributed Fault-tolerant Fusion Estimation Based On Multiple-model Extended Kalman Filter

As practical nonlinear systems become progressively complex, the extended Kalman filter (EKF) is limited for many applications because of its performance degradation. In this paper, we propose a novel multiple-model extended Kalman filter (MMEKF), which remarkably reduced its large deviation. The expansion points designed in the MMEKF algorithm obey the Gaussian distribution in the process of probabilistic models, which are used to approximately represent the whole state space by using multiple probabilistic weighted method. Compared with other filters such as EKF, UKF and CKF, the MMEKF shows higher estimation accuracy for unreliable measurements especially in multi-sensor systems. This paper also considers fault-tolerant distributed data fusion estimation, whose feasibility and effectiveness through a numerical example.
Image Similarity Index Tradeoff Model for Industrial Network

Industrial image processing and computer vision play significant role in factory automation since industries now employ human-robot interaction for the monitoring of products in areas considered risky and dangerous for humans. The challenge however, is to ensure reliability in image processing such that image sizes and image similarity index are expected to be perfect representation of actual objects. This paper investigated the statistical relationship between the image ratio size and similarity index after compression. Using correlation analysis, a statistical relationship was established between the image size ratio and similarity index of selected images under review. It was observed that an inverse and high correlation existed between the image similarity index and image size ratio of compressed images. The result of the validation shows that the proposed regression model has predictability or good fit of 95%.
Semantic Rules-Based Classification of Outdoor Natural Scene Images

This paper proposes the classification of outdoor natural scene images using semantic rules. The proposed work is divided into three stages: segmentation, object recognition and image classification. Segmented images are generated by applying SPDBSCAN with user interaction on original image. CNN model is trained of using these segmented images for recognition of the objects such as sky, water, green land and sand. Semantic rules are designed using information of object class and its spatial location in an image for classification of scene image into either of four classes: green_ground, desert, sea_water and beach class. For this research work, we have used images from SUN-397 dataset. The work has achieved F-ratio of 84% for scene classification.
Visual System for Tracking Specific Human Body Extraction of 3D Skeletal Points Based on Monocular

Abstract In this paper, we introduce the Extracting specific human 3D skeleton point system based on monocular tracking. The system mainly consists of two parts. The first part is the detection and tracking of specific human body. This article uses simple online and real time tracking with a deep association metric (DEEP SORT)[1] algorithm, which is simple but effective, and meets system requirements in terms of efficiency and real-time. The second part is to extract the 3D bone points for the specific target of the tracking. We refer to Xingyi Zhou’s research work[2] in this area. Utilizing the correlation between 2D pose and depth estimation subtasks, the training is end-to-end, and the algorithm introduces 3D geometric constraints to normalize 3D pose prediction, which is effective without ground truth value depth labels. In this paper, the two methods are combined by improvement, and the Extracting specific human 3D bone point system based on monocular tracking is designed. It can realize the tracking of 3D skeletal points of specific targets. The system has high practical value in human-computer interaction, virtual reality and motion recognition.
A Game Theory Based Reputation System for Security

Security mechanisms play very crucial role while using the system. It is very important to secure the system from external as well as internal threats. External threats affect the system from outside and only when the system is connected to internet. Internal threats affect from inside the organization. In case of internal threats it is very hard to know who the actual threats are. It is easy for a system to secure it from external threats in comparison to internal threats. When it comes to secure the system from internal threats, it’s really tough to define security policies because here the actual threats to system are unknown. Any crisp policies define for this purpose also affects the legitimate users. In this kind of scenario a flexible security mechanism simulating the real world’s policy of reputation and trust is required. So in this paper a two layered architecture, one using traditional security model and other using game theoretical security model for system security is discussed.
Inertial Monocular Visual Odometry Based on RUPF Algorithm

The accurate autonomous positioning performance of the unmanned vehicle is an important prerequisite for autonomous navigation. The combination of visual and inertial sensors is a cheap, compact and complementary solution. In this paper, in order to improve the positioning accuracy of the monocular vision/IMU integrated system, an visual inertial odometry based on the Random sampling consistency of the Unscented Kalman Particle Filter (RUPF) was proattituded. With respect to feature point tracking, the method of optical flow tracking is used to reduce the time required for feature matching. At the same time, a random sampling consistency algorithm is used to eliminate the mis-matching feature points in the three-view feature point tracking process, and then the Epipolar geometry and the three constraints formed by the focus tensor is integrated into the observation equations, and unconstrained particle filtering is used to fuse the IMU and monocular visual information. The fusion algorithm is verified by the KITTI sports car data set. The experimental results show that the visual inertial odometry based on the RUPF filtering algorithm has accurate positioning accuracy, the final positioning error is controlled at around 0.19%.
Scalability Evaluation of Big Data Processing Services in Clouds

Currently, many cloud providers deploy their big data processing systems as cloud services, which helps users conveniently manage and process their data in clouds. Among different service providers’ big data processing services, how to evaluate and compare their scalability is an interesting and challenging work. Most traditional benchmark tools focus on performance evaluation of big data processing systems, such as aggregated throughput and IOPS, but fail to conduct a quantitative analysis of their scalability. In this paper, we propose a measurement methodology to quantify the scalability of big data processing services, which makes the cloud services scalability comparable. We conduct a group of comparative experiments on AliCloud E-MapReduce and Baidu MRS, and collect their respective scalability characteristics under Hadoop and Spark workloads. The scalability characteristics observed in our work could help cloud users choose the best cloud service platform to set up an optimized big data processing system to achieve their specific goals more successfully.
An MPC-based Strategy Design for an Autonomous Aerial Robot

In this paper, a strategy design problem is investigated for an aerial robot which is required to autonomously select and interact with ground targets so as to complete a specified task in a complex envirioment with moving obstacles. First, the kinematic models of the aerial robot, ground targets and obstacles. Then, an MPC-based strategy design is proposed in which the current state of the aerial robot, the state prediction of the targets and obstacle avoiding are considered. Since the motion periods of targets are different which leads to incorrect estimations, an IMM filter is given to obtain more precise future state and motion period of the targets. Finally, simulation and experimental results show the effectiveness of the proposed method.
Towards a Semantic Video Analysis using Deep Learning and Ontology

With the technological advances in the field of multimedia, associated with the generalization of their uses in many applications such as television archiving, motion tracking, video surveillance, etc. Semantic analysis and automatic understanding of large collections of video documents become a major problem. Consequently, the need for a system, which will allow to effectively manipulate video content is undeniable. This paper presents an approach that allows the systematic video analysis using deep learning and ontology generation. The proposed approach permits the extraction and the building of an ontology using results obtained by the deep learning techniques such as key frames, detected objects and actions (movements).
Performance Evaluation of Neural Networks for Speaker Recognition

Speaker Recognition is one of the principle problems in Speech processing. The performance of speaker recognition systems can be improved by carefully choosing and calculating suitable features, which is an arduous task. Therefore, the learning based approach has been found to be simpler, more general and with the rapid growth in Artificial Intelligence, more accurate. This paper is a comparative study of the performance of different neural networks in speaker recognition. The focus of this work is to find which of these learning algorithms is more accurate, less complex, and more generic when it comes to speaker recognition. A database of 5000 utterances, 100 for each of the 50 different speakers, in both clean and noisy environment, with varying levels of noise was used. The MFCC (Mel Frequency Cepstral Coefficients) of these utterances were used as features to train and evaluate the neural networks. Accuracy of all neural networks was expectedly very high (>90%) for clean data, large variations coming in with introduction and change in the level of noise. RBFNN has been shown to consistently perform well under all conditions. DNN was the other consistent performer and has the potential to outperform other techniques, if trained on more data.
Optimization of solar photovoltaic efficiency using angular inclination: Effect on voltage output

The physical optimization processes of maximizing the solar photovoltaic (PV) panel via the angular adjustment of PV inclination to the sun were examined. The efficiency may vary within geographic locations. In this study, the measured dataset of a location in the tropical region of Nigeria was examined with the inclination ranging from 0° to 45° It was observed that at 30 ° in the south –east direction yielded a more stable result that is essential basically for the stability of the solid state content of the PV panel. Though there were spikes of higher voltage in the 30 °, it is recommended that PV users in the region to adopt 30 ° to enhance efficiency and longevity of the PV panel.
Two-stage Cubature Kalman Filtering Fusion Algorithms for Nonlinear Systems

The traditional two-stage Kalman filtering fusion method can not deal with the case of non-linear systems, which limits its application scope and level in practical engineering. Aiming at the above problems, this paper studies the design of two-stage cubature Kalman filter fusion algorithm for non-linear systems, and establishes a two-stage cubature Kalman filter fusion algorithm for multi-sensor systems. The main filter uses the output information of each local filter, fuses the global values, and feeds back to each local filter reasonably. The simulation results show that the proposed two-stage cubature Kalman filter fusion estimator (MTSCKF) has better performance than the two-stage cubature Kalman filter estimator (TSCKF) in filtering estimation accuracy.
A Vehicle Recognition Method Based on Multi-Camera Information

Disadvantages of the single-camera-based vehicle identification method include insensitivity to vehicle angle information, vulnerability to occlusion and limited viewing angle of a single camera. In order to solve the above problems, a multi-camera based vehicle identification method is proposed, which detects the vehicle objects in the multi-camera, binds the object bounding box belonging to the same vehicle, and distinguish the vehicle by using the fine-grained identification network combined with the multi-camera information fusion method, Solved the problem of shooting dead ends, insufficient vehicle angle information and occlusion. Experiments show that the proposed method can achieve better recognition accuracy than the single camera recognition method.
Application of Linguistic Knowledge in Factored Language Modeling for Hindi Language

A language model is a technique that shows which words are more or less likely to be generated during some conversation in any natural language. N-gram language modeling is the pioneer technology used to construct language models. N-gram technique considers preceding words only to predict the upcoming word. Factored language modeling is a formalism that provides a facility to undertake other linguistic knowledge of the words like gender, number, part of speech, stem of word along with word itself to predict next word in a sentence. This paper discusses the effect of various combinations of linguistic features of word on predictability of next word in Hindi-language sentence. The paper also discusses how use of linguistic features decreases the perplexity by 31.71% as compared to perplexity of baseline N-gram language model.