Reviewerly/exHarmony huggingface dataset:
An Efficient Data Augmentation Network for Out-of-Distribution Image Detection

Since deep neural networks may classify out-of-distribution image data into in-distribution classes with high confidence scores, this problem may cause serious or even fatal hazards in certain applications, such as autonomous vehicles and medical diagnosis. Therefore, out-of-distribution detection (also called anomaly detection or outlier detection) of image classification has become a critical issue for the successful development of neural networks. In other words, a successful neural network needs to be able to distinguish anomalous data that is significantly different from the data used in training. In this paper, we propose an efficient data augmentation network to detect out-of-distribution image data by introducing a set of common geometric operations into training and testing images. The output predicted probabilities of the augmented data are combined by an aggregation function to provide a confidence score to distinguish between in-distribution and out-of-distribution image data. Different from other approaches that use out-of-distribution image data for training networks, we only use in-distribution image data in the proposed data augmentation network. This advantage makes our approach more practical than other approaches, and can be easily applied to various neural networks to improve security in practical applications. The experimental results show that the proposed data augmentation network outperforms the state-of-the-art approaches in various datasets. In addition, pre-training techniques can be integrated into the data augmentation network to make substantial improvements to large and complex data sets. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.github.com/majic0626/Data-Augmentation-Network.git</uri> .
Interactive Reinforcement Learning from Imperfect Teachers

Robots can use information from people to improve learning speed or quality. However, people can have short attention spans and misunderstand tasks. Our work addresses these issues with algorithms for learning from inattentive teachers that take advantage of feedback when people are present, and an algorithm for learning from inaccurate teachers that estimates which state-action pairs receive incorrect feedback. These advances will enhance robots' ability to take advantage of imperfect feedback from human teachers.
HybridTEE: Secure Mobile DNN Execution Using Hybrid Trusted Execution Environment

Deep neural networks (DNNs) have been increasingly adopted in many mobile applications involving security sensitive data and inference models. Therefore, there is an increasing demand for secure DNN execution on mobile devices. Catering to this demand, hardware-based trusted execution environments (TEEs), such as ARM TrustZone, have recently been considered for secure mobile DNN execution. However, it is challenging to run DNN models in TrustZone, due to the stringent resource and performance limitations posed by the mobile TEE. We develop HybridTEE, a novel hardware-based security framework to securely execute DNNs in the resource-constrained local TEE (i.e., ARM TrustZone), by offloading a part of the DNN model to a resource-rich remote TEE (i.e., Intel SGX). HybridTEE strategically divides the DNN model into privacy-aware local and remote partitions by employing two privacy-oriented metrics based on object recognition and Scale Invariant Feature Transform (SIFT). Also, it builds a trustworthy communication channel bridging TrustZone and SGX to enable secure offloading of the DNN model between the two TEEs. Our security and performance evaluations on real hardware systems show that HybridTEE can ensure the security and privacy of the DNN model with superior execution time compared to the non- TEE baseline.
A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.
CardSorter: Towards an Open Source Tool for Online Card Sorts

Structural design of shared information environments is rather challenging. Card sorting constitutes a valuable method towards this end. However, there is a lack of free software tools for conducting card sorts, especially from a distance, and analysing the collected data. This paper presents CardSorter, an open source, web-based, card sorting tool. Currently, CardSorter offers functionality for running open card sorts online and analysing the collected data in real time. In a user testing study involving 108 participants, CardSorter was found to be from Good to Excellent in terms of perceived usability. A heuristic evaluation of the tool involving 25 teams of novice evaluators identified issues for further improvement.
WITHDRAWN: Facial expression recognition from videos using CNN and feature aggregation

The facial expression recognition (FER) system has always been in trend and majorly when it comes to the FER from the video clips, then it becomes a crucial task to do. The differences seen in the visual descriptor and the emotions seen on the face need to be filled in the FER from videos. Here in our proposed work aggregation is done between the spatial and temporal convolutional features that are available in the whole video for the recognition of facial expressions in any video. We are using here 15–15 both spatial and temporal streams, in which every stream, i.e. spatial is corresponding with the temporal flow which creates a layer of aggregation for the training of end to end FER system from the video. This training gives a better representation of the video and can avoid the problem of over fitting from the limitation of available datasets. We have found that the proposed approach is best for the aggregation of the spatial–temporal features from video dataset as compared to other available methods. The dataset we have used are RML, MMI, BAUM-1 s, eNTERFACE05, FER-2013 and the results obtained after applying the proposed approach on this datasets are satisfactory.
A Modified Corner Detector for SAR Images Registration

As a first step for image processing operations, detection of corners is a vital procedure where it can be applied for many applications as feature matching, image registration, image mosaicking, image fusion, and change detection. Image registration can be defined as process of getting the misalignment of pixel's position between two or more images. In this paper, a modified corner detector named Synthetic Aperture Radar-Phase Congruency Harris (SAR-PCH) based on a combination between both phase congruency, named later PC, and Harris corner detector is proposed where PC image can supply fundamental and significative features although the complex changes of intensities. Also, the proposed approach overcomes the Harris limitation concerning the noise since the Harris is more sensitive to the noise. The performance was similitude with Shi-Tomasi, FAST, and Harris corner detectors where experiments are conducted first with simulated images and second with real ones. Mean square error (MSE) and peak signal-to-noise ratio (PSNR) are used for the simile. Experimental results, carried out in a standard computer, verify its effectiveness where it utilizes the privileges of image constitutional depicting, allowing extraction of the most powerful key points since it preserves robustness of co-registration process using image frequency properties which are not variant to illumination. Reasonable results compared to the state of art method as Shi-Tomasi, FAST, and Harris algorithms were achieved on the expense of high computational processing time that can be recovered using hardware having high capabilities.
A Predictive Technique for the Real-Time Trajectory Scaling Under High-Order Constraints

Modern robotic systems must be able to react to unexpected environmental events.To this purpose, planning techniques for the real-time generation/modification of trajectories have been developed in recent times.In the frequent case of applications which require following a predefined path, the assigned timing law must be inspected in real time so as to verify whether it satisfies the system constraints, or conversely, if it must be scaled in order to obtain a feasible trajectory.The problem has been addressed in several ways in the literature.One of the known approaches, based on the use of nonlinear filters, is revised in this article in order to return feasible solutions under any circumstances.Differently from alternative strategies, it manages constraints up to the torque derivatives and has evaluation times compatible with the ones required by modern control systems.The proposed technique is validated through simulations and real experiments.Comparisons are proposed with an algorithm based on a model predictive technique and with an alternative scaling system.
Estimation, Forecasting, and Anomaly Detection for Nonstationary Streams Using Adaptive Estimation

Streaming data provides substantial challenges for data analysis. From a computational standpoint, these challenges arise from constraints related to computer memory and processing speed. Statistically, the challenges relate to constructing procedures that can handle the so-called concept drift-the tendency of future data to have different underlying properties to current and historic data. The issue of handling structure, such as trend and periodicity, remains a difficult problem for streaming estimation. We propose the real-time adaptive component (RAC), a penalized-regression modeling framework that satisfies the computational constraints of streaming data, and provides the capability for dealing with concept drift. At the core of the estimation process are techniques from adaptive filtering. The RAC procedure adopts a specified basis to handle local structure, along with a least absolute shrinkage operator-like penalty procedure to handle over fitting. We enhance the RAC estimation procedure with a streaming anomaly detection capability. The experiments with simulated data suggest the procedure can be considered as a competitive tool for a variety of scenarios, and an illustration with real cyber-security data further demonstrates the promise of the method.
Spatial Data Mining Methods Databases and Statistics Point of Views

This article reviews the approaches used in data mining to perform a geographical study of regional datasets coupled with Geographic Information Systems (GIS). Firstly, we can look at the functions of data mining used by such data and then illustrate their precision compared to their classic data use. We will further explain the research conducted in this sector and point out that two separate methods exist: one is focused on space database learning while the other is based on space statistics. Finally, we will address the key distinctions between these two methods and their similar features.
On decision regions of narrow deep neural networks

We show that for neural network functions that have width less or equal to the input dimension all connected components of decision regions are unbounded. The result holds for continuous and strictly monotonic activation functions as well as for the ReLU activation function. This complements recent results on approximation capabilities by Hanin and Sellke (2017) and connectivity of decision regions by Nguyen et al. (2018) for such narrow neural networks. Our results are illustrated by means of numerical experiments.
Fine-Grained Evaluation of Knowledge Graph Embedding Model in Knowledge Enhancement Downstream Tasks

Knowledge graph (KG) embedding models are proposed to encode entities and relations into a low-dimensional vector space, in turn, can support various machine learning models on KG completion with good performance and robustness. However, the current entity ranking protocol about KG completion cannot adequately evaluate the impacts of KG embedding models in real-world applications. However, KG embeddings are not widely used as word embeddings. An asserted powerful KG embedding model may not be effective in downstream tasks. So in this paper, we commit to finding the answers by using downstream tasks instead of entity ranking protocol to evaluate the effectiveness of KG embeddings. Specifically, we conduct comprehensive experiments on different KG embedding models in KG based question answering, recommendation and natural language processing tasks. Utilizing different genre of downstream tasks, we try to mine the characteristics of each KGE model in actual application scenarios, and provide guidance for the research of KGE models and knowledge-enhanced downstream tasks.
The Nodality Disconnect of Data-Driven Government

We must ask critical questions regarding what actors are gaining influence, and regarding why the centrality of government is to be preserved in a data-intensive society. The article recognizes that the transformative capacity of big data—and its artificial intelligence (AI)-based companion data analytics—does not deterministically result from the technologies concerned. Instead, the direction of change depends on both the technical features and the intertwining of big data applications and governmental machinery. In short, the reconfiguration of the government nodality remains an open question. Overall, government is urged to think strategically about its future role within digital ecosystems.
Resource Efficient Edge Computing Infrastructure for Video Surveillance

The emerging edge computing applications often use high definition cameras as edge devices to capture video streams that need to be analyzed in real-time for situational understanding and answering queries. However, such devices suffer from limited energy (and hence limited computing power) and limited bandwidth available to stream the data to the edge controllers that provide much higher computing capacities. In this paper, we address these issues in the context of vehicular traffic monitoring and develop a scheme that has two components: YLLO and BATS. YLLO is a lightweight object recognition algorithm that runs on the edge device itself and substantially reduces the frame rate sent to the edge controller without removing the important information. BATS adapts the transmissions to the available bandwidth by taking advantage of further redundancy in the video stream in both single and multi-camera scenarios. We show that these mechanisms together can maintain object identification accuracy of above 95 percent, while transmitting just <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 5–10 percent of all the frames recorded by the cameras.
High accuracy data-driven heliostat calibration and state prediction with pretrained deep neural networks

The efficiency of solar tower power plants depends strongly on the ability to reflect the sun light onto a defined point on the receiver. Due to the high demands on the heliostats to achieve high accuracy at low costs, a regular calibration is necessary to reduce the tracking error. In this paper a new method for improving existing calibration methods using deep learning is presented. The results are validated by using calibration data recorded at the Solar Tower Jülich with the example of one heliostat. Through a combination of Self-Normalizing Neural Networks and transfer learning it is possible to benefit from the advantages of neural networks already with a training dataset of only 300 measuring points. With that a measured test accuracy of 0.42 mrad was achieved. This was approximately three times more accurate than the best result of the compared state-of-the-art regression algorithm used in Jölich. Furthermore we give recommendations on the structure of the dataset and the neural network (NN) pretraining necessary for these results.
The impact of conversational agents’ language on summary writing

This study investigated how computer agents’ language style affects summary writing in an Intelligent Tutoring System, called CSAL AutoTutor. Participants interacted with two computer agents in one of three language styles: (1) a formal language style, (2) an informal language style, and (3) a mixed language style. Primary results indicated that participants improved the quality of summary writing, spent less time writing summaries, and had lower syntactic complexity but more non-narrative summaries on posttest than pretest. However, this difference was not affected by the discourse formality that agents used during instruction. Results also showed participants rated peer summaries more accurately for cause/effect texts in the formal and mixed conditions, but generated summaries with lower referential cohesion in the informal condition on posttest than pretest.
Convolutional Neural Network-Based Sub-Pixel Line-Edged Angle Detection With Applications in Measurement

High precision measurement is becoming an imperative requirement in many applications. A novel sub-pixel line-edged angle detection method based on convolutional neural network is proposed in this paper. The line edges of targets are accurately estimated by their geometric slope angles with an edge point located on the line. Specifically, the pixel level line-edged images are first obtained by image preprocessing. Then, two separate convolutional neural networks are effectively constructed to boost their discriminative capabilities for the sub-pixel line-edged angle classification. The pixel level line-shaped edge images are used as input and the final network outputs are the specific sub-pixel level line-edged angles. Finally, the sub-pixel level diameter measurements are precisely performed with the estimated angles. Compared with existing methods, the proposed method can estimate the sub-pixel line-edged angle with 0.1 degree accuracy in end-to-end way, even for the noisy images. Simulation results for angle measurement and the real-world experiment for diameter measurement reveal the validity of the proposed method.
Modeling and Controlling the Dynamic Behavior of an Aerial Manipulator

Unmanned Aerial Vehicles (UAVs) installed with a gripper is an effective and robust way to grab the wanted object from inaccessible locations. In this study, we develop a novel control mechanism to regulate the nonlinear dynamics of the aerial manipulator. In this research, hex-rotor UAV is chosen in order to fulfill the mission requirement in terms of size and weight of the object. It is equipped with a manipulator and the gimbal-based camera that will help to see the desired object and then transport it. The aerial vehicle has six-degrees-of-freedom (6-DOF) and the installed manipulator has 4-DOF which in total makes the 10-DOF aerial manipulator vehicle. At the time of clutching the desired object to eliminate or reduce the external noise, and stabilize the dynamic behavior of the aerial manipulator, we need a robust and efficient controller. To solve the aforementioned problems, this study develops a hybrid control mechanism that tracks and controls the altitude and attitude of UAV after clutching the desired object. The main contribution of this study is to design a control mechanism that includes Model Reference Adaptive Control with an Integrator (MRACI) in conjunction with regulation, pole-placement and tracking (RST) control algorithm. On one hand, the simulation results using MATLAB demonstrate the efficiency of the proposed control mechanism. On the other hand, to cross verify the validity of the proposed control algorithm, we perform the experiment by clutching the desired object at hovering and normal flight operation.
A Lightweight Cross-Domain Proximity-Based Authentication Method for IoT Based on IOTA

Nowadays, electronic industry witnesses a massive explosion of offering Internet of Things (IoT) devices with cellular technology to the market for machinery type communication (MTC). Due to usually unmanned deployments, MTC requires authentication for security reasons before exchanging actual information. Today, IoT cross-domain authentication executed at a blockchain backend side is well studied. However, lightweight proximity-based authentication for cross-domain IoT devices is still lack of consideration. In this paper, we show the first attempt to solve this problem based on IOTA blockchain technology. Specifically, our solution benefits both from the advantages of IOTA blockchain and the capabilities of mobile edge computing (MEC) so that a lightweight authentication procedure can be achieved by reducing involvements of the heavy backend side. A small in-house prototype system is implemented in order to validate the feasibility of the proposed solution.
Multistage Model for Robust Face Alignment Using Deep Neural Networks

The ability to generalize unconstrained conditions such as severe occlusions and large pose variations remains a challenging goal to achieve in face alignment. In this paper, a multistage model based on deep neural networks is proposed which takes advantage of spatial transformer networks, hourglass networks and exemplar-based shape constraints. First, a spatial transformer-generative adversarial network which consists of convolutional layers and residual units is utilized to solve the initialization issues caused by face detectors, such as rotation and scale variations, to obtain improved face bounding boxes for face alignment. Then, stacked hourglass network is employed to obtain preliminary locations of landmarks as well as their corresponding scores. In addition, an exemplar-based shape dictionary is designed to determine landmarks with low scores based on those with high scores. By incorporating face shape constraints, misaligned landmarks caused by occlusions or cluttered backgrounds can be considerably improved. Extensive experiments based on challenging benchmark datasets are performed to demonstrate the superior performance of the proposed method over other state-of-the-art methods.