Reviewerly/exHarmony huggingface dataset:
Image and Ultrasonic Sensor Fusion for Object Size Detection

Sensor fusion is the combination of sensory data or data derived from sensors such that the information obtained would be more accurate and complete than when they were used individually. Sensor fusion is applicable for object detection and autonomous navigation systems. The techniques that have been developed for object detection and object size calculation are computationally expensive and costly. Hence, a new technique is proposed which combines visual information from the camera with distance calculated from the sensors to detect an object, measure the distance and calculate the size of the object.
Effect of Augmented Reality on Faults Leading to Human Failures in Socio-technical Systems

With the ultimate purpose of assessing risk within augmented reality-equipped socio-technical systems, in our previous work, we systematically organized and extended state-of-the-art taxonomies of human failures to include the failures related to the extended capabilities enabled by AR technologies. The result of our organization and extension was presented in form of a feature diagram. Current state-of-the-art taxonomies of faults leading to human failures do not consider augmented reality effects and the new types of faults leading to human failures. Thus, in this paper, we develop our previous work further and review state-of-the-art taxonomies of faults leading to human failures in order to: 1) organize them systematically, and 2) include the new faults, which might be due to AR. Coherently with what done previously, we use a feature diagram to represent the commonalities and variabilities of the different taxonomies and we introduce new features to represent the new AR-caused faults. Finally, an AR-equipped socio-technical system is presented and used to discuss about the usefulness of our taxonomy.
Cruise Control for a Two-Wheeled Mobile Vehicle Using Its Mixed Logical Dynamical System Model

This paper proposes a cruise control for a two-wheeled mobile vehicle on a two-dimensional plane. The cruise control is executed using model predictive control (MPC), such that input and state constraints can be addressed explicitly. For real-time execution of MPC, we model the dynamics of the vehicle as a mixed logical dynamical system rather than an underlying nonlinear model. The cruise control is then reduced to a mixed integer quadratic programming problem, for which efficient solvers using the convexities are available. Simulations and experiments demonstrate the effectiveness of the proposed cruise control through a computation time comparison with the nonlinear MPC method.
Adaptive Masking: a Dynamic Trade-off between Energy Consumption and Hardware Security

As the Internet of Things (IoT) devices process and communicate an increasing amount of sensitive data, the confidentiality of these data is a growing concern. Hardware side-channel attacks pose a threat against the physical implementation of encryption, which is commonly used to ensure this confidentiality. Traditional hardware countermeasures against such side-channel attacks, like masking, usually introduce a high energy overhead. However, IoT devices are typically resource-constrained and have a small energy budget. Furthermore, the energy or security constraints may vary between devices or over time, depending on, e.g., the remaining battery level or data sensitivity. Therefore, a dynamic trade-off has to be found between security, power consumption and performance. In this paper, we introduce a new paradigm whereby hardware masking is applied on demand to increase the energy efficiency. We illustrate this concept by applying it to the stream cipher Trivium. We demonstrate that compared to a reference masked implementation, this solution ensures a high level of security when necessary while significantly reducing the power consumption (by up to 76.2%) the rest of the time, with a small area overhead (13.5%) and no impact on the performance.
Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors

To avoid the inherent defects of current airport surface surveillance systems, a distributed non-cooperative surface surveillance scheme based on geomagnetic sensor technology is proposed in this article. Furthermore, a surface target tracking algorithm based on improved interacting multiple model (WIMM) is presented for use when the target is perceptible. In this algorithm, the weighted sum of the mean values of the residual errors, which is used to reconstruct the model probabilistic likelihood function, and the Markov model transition probability are updated using posterior information. When a target is imperceptible, its trajectory can be predicted by the target identified motion model and the adaptive model transition probability. Simulation results show that the WIMM algorithm can be used efficiently together with an observed small sample of velocity information for target tracking and trajectory prediction. Compared with the interacting multiple model and residual-mean interacting multiple model algorithms, the frequency of model switching and the rate of model identification were increased during the imperceptible period, and target prediction error was greatly reduced.
Cost effective and reliable mobile solution for face recognition and authentication

Attendance marking is a critical and time-consuming process in schools and colleges. Manual attendance marking is time consuming, so as attendance recording using biometrics. Attendance marking using face recognition is time saving when compared to conventional methods. Most of the existing face recognition systems which uses static cameras are expensive and have portability issues too. To overcome this above mentioned time and portability constraints we propose an attendance marking system based on face recognition. The proposed system implemented as an android application takes input from the smart phone camera to mark the attendance. It uses Facenet Resnet V1 [10] convolutional neural network which was introduced by Google Inc, for face recognition. The attendance will be recorded in a learning management system(LMS) which serves as a back end application for the android application. After face recognition we are saving the attendance in our internal LMS system automatically. As per our analysis, we have noticed that the system works perfectly in a controlled scenario of 3-meter distance using a mobile camera device with a minimum face size of 108 × 108(Height × Width). In this controlled scenario our proposed methods achieves an accuracy over 90%.
Incremental Learning of SVM Using Backward Elimination and Forward Selection of Support Vectors

Traditional pattern matching and classification algorithms in machine learning field build models by extracting patterns from whole data at once. It means once a model is learned about the data, the parameters of the model will never be updated and the model remains unchanged forever. However, in the industrial applications, a large volume of data is accumulated incrementally and the dynamics of the data may change over time. The traditional approach of learning the model using all the data at once may not be feasible because it may require a huge storage for data and take a very long time to build a model from a huge volume of data, and the one-time built model may not be able to learn the changed patterns automatically over time. To overcome this limitation of the traditional approach, we propose two novel algorithms of pattern matching and classification, based on Incremental Learning Support Vector Machine (ILSVM), which learn and update models as new data arrives. Our proposed algorithms work based on selection and elimination of support vectors while learning. By performing experiments on 13 publicly available data sets, we found that our proposed methods obtain better accuracy, are computationally faster and require lower memory than the traditional batch learning and ILSVM.
Identification of Click Fraud and Review of Existing Detection Algorithms

Ad Tech is a growing business, that has already taken over the traditional way of showing ads. It has made the ad-industry better with better reach to audience where the impact of the ads can be measured and acted upon to optimize the performance of the ads. But exposure to internet comes with its own pros and cons. Out of the three ways of paying out money, RPC payout method leads to click-fraud (spam-clicks or clicks with less intent), to increase the count of clicks (eg. Bot clicks) of generate more revenue, eventually affecting the quality of traffic. The paper discusses various ways being used to detect how ad-serving works, ways of detecting spam clicks and monitoring activity of users, and a proposed method. In this proposed method, visitors on the website and their identification details are analysed and each parameter is assigned a weight according to significance. Then each click is assigned a rank which is compared to a threshold value to determine whether the click is suspicious or not. The future scope of this method along with optimisation techniques is also discussed in this paper.
Fine-grained k-anonymity for privacy preserving in cloud

Data sensitive information is a crucial concern of every individual. Hospitals lag their trust in privacy to take up the newest technologies of cloud like Information-as-a-service, storage-as-a-service, to deploy their patient’s data for better health management. Intensive study is being undertaken to run-over the shortcomings of data privacy for the published information as well as the publisher, One amongst the methods is privacy by statistics using data mining techniques such as k-anonymity. The fundamental technique of k-anonymity is to anonymize sensitive information of an individual person published that could not be determined from at least (k-1) instances. The best way to attain k-anonymity is by grouping similar records into a cluster by choosing the best seed value to balance utility and privacy in the published data. This paper proposes a Fine-grained k-anonymity algorithm which uses a systematic procedure of seed selection. The proposed method exhibits a minimum information loss than existing clustering algorithms.
The Emergence of Agile Maintenance: A Preliminary Study

Agile maintenance is an approach adopting agile method in maintenance activities. Since software maintenance process requires fast and flexible way, adoption of agile method in software maintenance is promising. However, there is still lack of information on what are the challenges faced by maintainers when they adopted agile method in their software maintenance activities. This paper attempts to identify the issues and challenges during the adoption of agile method in software maintenance project. Document analysis is employed in this study considering from various literature. The results indicates several issues and challenges for maintainers such as interruption in sprint planning, unstable velocity and burndown chart inconsistency. The emergence of agile maintenance in software industry has given significant improvement in software maintenance process. This study may provide insight understanding for bigger picture of agile maintenance processes in software development projects.
Multi-graph Convolution Network with Jump Connection for Event Detection

Event detection is an important information extraction task in nature language processing. Recently, the method based on syntactic information and graph convolution network has been wildly used in event detection task and achieved good performance. For event detection, graph convolution network (GCN) based on dependency arcs can capture the sentence syntactic representations and the syntactic information, which is from candidate triggers to arguments. However, existing methods based on GCN with dependency arcs suffer from imbalance and redundant information in graph. To capture important and refined information in graph, we propose Multi-graph Convolution Network with Jump Connection (MGJ-ED). The multi-graph convolution network module adds a core subgraph splitted from dependency graph which selects important one-hop neighbors' syntactic information in breadth via GCN. Also the jump connection architecture aggregate GCN layers' representation with different attention score, which learns the importance of neighbors' syntactic information of different hops away in depth. The experimental results on the widely used ACE 2005 dataset shows the superiority of the other state-of-the-art methods.
Research on Autonomous Grasping Control of Underwater Manipulator Based on Visual Servo

To address the problem associated with the low efficiency and intelligence level of underwater master-slave operation method, a grasping control method of underwater manipulator, on the basis of monocular vision servo, is proposed to improve the autonomous capability of underwater operation. Typical underwater objects can be grasped accurately and quickly. Firstly, ArUco marker is used to obtain accurate position information at a high rate of speed, attitude information is also calculated based on the shape and color of target in key frames from video stream at the same time. Grasping method is adopted automatically according to specific position and attitude of the target. Experiments are carried out with the 7-function underwater hydraulic manipulator developed by Shenyang Institute of Automation, Chinese Academy of Sciences. The manipulator is programmed to grasp the object automatically based on visual servo and place it to the appointed position. Both land and underwater experiments validated that the adopted control method of manipulator in this paper meets the actual needs of underwater operation.
Deep Multi-label Hashing for Image Retrieval

Due to its low storage cost and fast query speed, hashing has been widely applied to approximate nearest neighbor search for large-scale image retrieval, while deep hashing further improves the retrieval quality by learning a good image representation. However, existing deep hash methods simplify multi-label images into single-label processing, so the rich semantic information from multi-label is ignored. Meanwhile, the imbalance of similarity information leads to the wrong sample weight in the loss function, which makes unsatisfactory training performance and lower recall rate. In this paper, we propose Deep Multi-Label Hashing (DMLH) model that generates binary hash codes which retain the semantic relationship of multi-label of the image. The contributions of this new model mainly include the following two aspects: (1) A novel sample weight calculation model adaptively adjusts the weight of the sample pair by calculating the semantic similarity of the multi-label image pairs. (2) The sample weight cross-entropy loss function, which is designed according to the similarity of the image, adjusts the balance of similar image pairs and dissimilar image pairs. Extensive experiments demonstrate that the proposed method can generate hash codes which achieve better retrieval performance on two benchmark datasets, NUS-WIDE and MS-COCO.
Question Answering System in Bengali Using Semantic Search

A Semantic Question Answering system (QA) is a computer program that takes question as input and retrieves answer using Information Retrieval (IR) rather than keyword based searching mechanism. Currently, there exists several Question Answering approaches and most of them are lexical similarity based approach. This work presents the design, implementation and evaluation of QA system based on semantic analysis using Bengali WordNet. The data sets are prepared from the Bengali text corpus which is developed under the TDIL (Technology Development for Indian Languages) project of the Government of India. The proposed model is tested on 40 questions from different subject domains and the overall accuracy is achieved as 80%. The challenges and the pitfalls of this work are explained in detail at the end of this paper.
3D Dual Path Networks and Multi-scale Feature Fusion for Human Motion Recognition

In this paper, a novel 3D dual path networks (3D-DPN) with 3D convolutional block attention module (3D-CBAM) and multi-scale feature fusion (MFF) was established. The architecture converted all 2D operations in the DPN structure into 3D and combined with 3D-CBAM to make the network have attention mechanisms for channel and spatiotemporal information. In order to keep the network sensitive to spatiotemporal input on different scales, we integrate the output of each 3D-DPN+CBAM block at the top of the network. This multi-scale feature fusion operation also allows the network to expand flexibly to accommodate different input frame lengths without significantly increasing the number of parameters and computation. We utilize this structure for human motion recognition based on the depth-sensing video. The experimental results indicate that our method higher-quality than the state-of-the-art model on multiple datasets.
Motion Coordination of Multiple Robots Based on Deep Reinforcement Learning

Multi-robot motion coordination is a sequential decision problem that controls each robot's action through a coordination mechanism to avoid collisions and reach their respective destinations. This problem can be viewed as a Markov Decision Process. Therefore, under determinate conditions of the motion path, we make the attempt of applying deep reinforcement learning (DRL) to solve the motion coordination problems. In this paper, in order to make this problem solvable through RL methods, we design a Path Checkerboard Diagram (PCD) to represent the collision relationship among different paths and determine whether collisions occur when the agent takes action. Technically, we design the states, actions and reward function. However, the traditional Deep Q-Networks (DQN) algorithm has the problem of overestimation of state-action values and high approximation error variance of the target values. To fit the motion coordination problem scenario, we further propose a new algorithm Multi-Loss Double DQN (MLDDQN) based on the Double DQN (DDQN), which minimizes the weighted sum of loss of different target networks we have learned before. We evaluate the performance of our algorithm through the different motion coordination tasks. Results suggest that our MLDDQN method yields remarkable improvements in terms of the success rate, success steps and the stability of training process compared with the original DDQN algorithm.
Real-Time Age-Invariant Face Recognition in Videos Using the ScatterNet Inception Hybrid Network (SIHN)

Face recognition has become a vital component of various safety and security systems with applications in safety and security systems, law enforcement applications, access control etc. Ageing makes face recognition challenging as the facial features evolve over time. In this paper, we propose a ScatterNet Inception Hybrid Network (SIHN) network that learns deep features for age-invariant face recognition. The trained system is evaluated on a separate dataset of 200 videos corresponding to 100 celebrities collected from public sources. These videos contain faces recorded at different locations, scales, rotations, illumination and ages. Experimental results evaluated over 27000 frames show that the proposed method can achieve state-of-the-art performance on both our video dataset as well as the other widely used datasets for age-invariant face datasets such as CACD and FG-NET. The system finds the individuals of interest from the videos in real-time at 18 fps. This research also introduces the Celebrities Video Aging (CVA) dataset used for evaluating the deep network which hopefully may encourage researchers interested in using deep learning for age-invariant face recognition.
Sentiment Analysis and Contrastive Experiments of Long News Texts

In view of the current patterns and characteristics of information dissemination, this paper makes a sentiment analysis of network news. It aims to effectively control the spread of negative and false information, maintain social stability, and provide support for relevant departments to master the right of disseminate discourse. The "Belt and Road" related corpus obtained through web crawler is used as the experimental corpus. We use the dictionary method, machine learning method and deep learning method to analyze the preprocessed corpus respectively. Among them, machine learning algorithms include Logistic Regression, Naive Bayes and Support Vector Machine(SVM). The method of deep learning is Long Short-term Memory(LSTM) network. The experimental results show that the accuracy of the dictionary-based method is lower than the other two methods. The accuracy of the machine learning-based method depends on the selection of the feature vector dimension. In contrast, deep learning-based method have higher accuracy.
Multi-Resident Activity Recognition using Multi-Label Classification in Ambient Sensing Smart Homes

Activity recognition in smart home environment using wireless ambient sensing is a well-known problem that is being researched very actively. Rapid development in the sensing technologies has made human activity recognition very important for various fields such as health care, home monitoring, surveillance, etc. In this paper, we describe the use of Classifier Chain method of the Multi-Label Classification approach to tackle the task of multi-resident activity recognition. We evaluate the developed model of Classifier Chain with K-Nearest Neighbor as base classifier on real world ARAS dataset which consists of two smart homes with evaluation metrics such as accuracy, precision and hamming loss. Through results, it can be inferred that Classifier Chain method successfully caters the problem of multi-resident activity recognition taking into consideration underlying label dependencies.
Leveraging Data-Analysis Session Logs for Efficient, Personalized, Interactive View Recommendation

View recommendation has been recently adopted to assist data analysts in better understanding the data. In order to recommend useful views, existing view recommendation approaches propose a variety of utility functions, each suitable for a different usage scenario. However, as the "interestingness" of a recommended view is user-dependent, no single utility function can represent users' preferences and intentions in all cases. With the richly available choices for utility functions, identifying the most appropriate ones along with their tunable parameters remains a challenge even for expert users. To help identify the most appropriate utility function, existing works have made attempts in two different directions, 1) providing generic recommendations of utility functions by learning offline from historical logs, and 2) providing personalized recommendations of utility functions by learning from the interactions with each particular user. Both proposed approaches exhibit clear advantages and disadvantages. In this work, to benefit from both approaches, we device a novel hybrid interactive view recommendation solution, namely HolisticViewSeeker (HVS), that effectively combines the offline learning with the online interactive learning to provide personalized view recommendation. Our experimental evaluations conducted on real-world data show that HVS outperforms both state-of-the-art online and offline approaches by a significant margin in multiple respects.