Reviewerly/exHarmony huggingface dataset:
Improving word vector model with part‐of‐speech and dependency grammar information

Part-of-speech (POS) and dependency grammar (DG) are the basic components of natural language processing. However, current word vector models have not made full use of both POS information and DG information, and hence the models’ performances are limited to some extent. The authors first put forward the concept of POS vector, and then, based on continuous bag-of-words (CBOW), constructed four models: CBOW + P, CBOW + PW, CBOW + G, and CBOW + G + P to incorporate POS information and DG information into word vectors. The CBOW + P and CBOW + PW models are based on POS tagging, the CBOW + G model is based on DG parsing, and the CBOW + G + P model is based on POS tagging and DG parsing. POS information is integrated into the training process of word vectors through the POS vector to solve the problem of the POS similarity being difficult to measure. The POS vector correlation coefficient and distance weighting function are used to train the POS vector as well as the word vector. DG information is used to correct the information loss caused by fixed context windows. Dependency relations weight is used to measure the difference of dependency relations. Experiments demonstrated the superior performance of their models while the time complexity is still kept the same as the base model of CBOW.
WITHDRAWN: Blockchain technology for secure medical data sharing using consensus mechanism

Due to technological advancement, sharing of healthcare data among institutions is more challenging. Heterogeneous data modelling may prevent compatibility, while distinctive use of healthcare terminologies restricts data comprehension. Even though, semantics and structure of data are approved, both data security and consistency are resourcefully considered. Authorized providers and centralized data storage are attractive targets of numerous attacks. Establishment of constant perspective of patient record over data sharing networks are more problematic. In this work, Block chain based encryption of patient data among shared network is analyzed and investigated. Here, a consensus mechanism was used to validate Proof of Word and Inter-operability for data discovery and access. This approach offers a centralized trust based source for network consensus and compute consensus proof of semantic and structural interoperability. This consensus mechanism shows better trade off in contrary to other network modelling.
Reconnecting with Food through Dining Play

While the food production and processing are known to cause major environmental pollution, we as consumers have little awareness of the underlying processes that bring food from farm to plate. This lack of awareness influences our food related decisions and we end up following non-sustainable food practices. To create awareness on food related issues, we present two playful dining experiences: (1) Caesar on a Skewer that presents the hidden data about water consumption levels for different foods through a Caesar salad, and (2) Chopstick Parfait that visualizes the hidden data related to the food distribution channels through the Parfait dessert. We simulated the dining environment to evaluate these dining experiences. We present findings related to both dining play interactions and edible visualizations. To guide future technological development around food related issues, we discuss the implications of our work around three themes: food arrangement, playful discomforts and delayed feedback. Through this work, we aim to advocate and inspire the use of food for designing multi-sensorial serious play.
Matrix Capsule Convolutional Projection for Deep Feature Learning

Capsule projection network (CapProNet) has shown its ability to obtain semantic information, and spatial structural information from the raw images. However, the vector capsule of CapProNet has limitations in representing semantic information due to ignoring local information. Besides, the number of trainable parameters also increases greatly with the dimension of the feature vector. To that end, we propose a matrix capsule convolution projection (MCCP) module by replacing the feature vector with a feature matrix, of which each column represents a local feature. The feature matrix is then convoluted by columns into capsule subspaces to decrease the number of trainable parameters effectively. Furthermore, the CapDetNet is designed to explore the structural information encoding of the MCCP module based on object detection task. Experimental results demonstrate that the proposed MCCP outperforms the baselines in image classification, and CapDetNet achieves the 2.3% performance gain in object detection.
Calibration-Free Structure-from-Motion with Calibrated Radial Trifocal Tensors

In this paper we consider the problem of Structure-from-Motion from images with unknown intrinsic calibration. Instead of estimating the internal camera parameters through some self-calibration procedure, we propose to use a subset of the reprojection constraints that is invariant to radial displacement. This allows us to recover metric 3D reconstructions without explicitly estimating the cameras' focal length or radial distortion parameters. The weaker projection model makes initializing the reconstruction especially difficult. To handle this additional challenge we propose two novel minimal solvers for radial trifocal tensor estimation. We evaluate our approach on real images and show that even for extreme optical systems, such as fisheye or catadioptric, we are able to get accurate reconstructions without performing any calibration.
Bypassing Tor Exit Blocking with Exit Bridge Onion Services

Tor exit blocking, in which websites disallow clients arriving from Tor, is a growing and potentially existential threat to the anonymity network. This paper introduces HebTor, a new and robust architecture for exit bridges---short-lived proxies that serve as alternative egress points for Tor. A key insight of HebTor is that exit bridges can operate as Tor onion services, allowing any device that can create outbound TCP connections to serve as an exit bridge, regardless of the presence of NATs and/or firewalls. HebTor employs a micropayment system that compensates exit bridge operators for their services, and a privacy-preserving reputation scheme that prevents freeloading. We show that HebTor effectively thwarts server-side blocking of Tor, and we describe the security, privacy, and legal implications of our design.
Real-Time Human Intrusion Detection for Home Surveillance Based on IOT

Considering home security being more prominent nowadays, its proper surveillance and alerts at the right time warrant utmost importance. Our project focuses on an enhanced home security system that integrates the surveillance systems with powerful machine learning tools that guarantee a flawless responsible home safeguard system. To implement a real-time home security system for human intrusion detection, a remote monitoring video surveillance system based on Wi-Fi was developed. The video captured from the camera is further segmented and pre-processing techniques like Histogram of Oriented Gradients (HOG) and HAAR cascade algorithm are used for smartly eliminating false alarm due to animals. The former extract features from the input image whereas the latter is a cascade classifier that is used for identifying the objects in an image as propounded by Viola-Jones. The combination of these features is fed to SVM for multi-stage classification. The system then identifies the intruder automatically alerts the home resident and sends an alert message to the user's mobile using IoT. In this chapter, the proposed work is implemented using Arduino, PIR sensor, wi-fi camera and evaluated in Python and Matlab2019. The power of this video surveillance system is efficiently enhanced with the use of proximity sensors. Experiment results show that the integration of hardware with effective machine learning techniques can attain remote surveillance with high reliability and accuracy.
Research on the Application of Computer-aided Translation Software in English Translation Teaching

Abstract With the continuous innovation of computer information technology in our country, its application in our life has attracted a lot of people’s attention and love. As we all know, English learning is a process of gradual accumulation. For English majors and non-English majors, the teaching of English translation is very important. The translation software can help them understand the specific meaning of English words or sentences. The application of computer assisted translation software in English translation teaching is the first topic put forward by foreign scientists. It has been proved that the advantages of its application are great. This paper studies and discusses the application of computer assisted translation in English translation teaching, and finally comes to the corresponding conclusion.
Noise Resistant Focal Loss for Object Detection

Noise robustness and hard example mining are two important aspects in object detection. A common view is that the two techniques are contradictory and they cannot be combined. In this paper, we show that there is a possibility to combine the best of two techniques. We find that, even using the hard example mining technique, recent deep neural network-based object detectors themselves have abilities to distinguish correct annotations and wrong annotations during the early stage of training. Based on this observation, we design a simple strategy to separate the wrong annotations from training data, reducing their loss weights and correcting their labels during training. The proposed method is simple, it doesn't add any computational overhead during model inference. Moreover, the proposed method combines the hard example mining and noise resistance property in one model. Experiments on PASCAL VOC and DOTA datasets show that the proposed method not only archieves competitive performances on clean dataset, but also outperforms the baseline by a large margin when data contain severe noise.
Genetic Algorithm with Updated Multipoint Crossover Technique and its Application to TSP

Genetic Algorithm (GA) is a promising method for optimizing the NP-hard problem especially the Travelling Salesman Problem (TSP). The reason of its popularity is for the ability to gain an ideal approximation in time. GA is usually based on the three artisans namely selection, reproduction and metamorphosis. The principal target of using GA is to determine the lowest total cost to travel all the nodes optimally. Consequently, this study introduces a novel crossover operator which optimizes the solution to the TSP. The suggested method started with two randomly selected parents and new offsprings have been generated by comparing cost. The overall methods, as well as the experimental outcomes, have also depicted here. The paper concludes that the newly introduced crossover operator outperforms various cross-over operators. It produced better result while experimenting on a set of instances from TSPLIB dataset.
Multiperson interaction recognition in images: A body keypoint based feature image analysis

Abstract Most interaction recognition approaches have been limited to single‐person action classification in videos. However, for still images where motion information is not available, the task becomes more complex. Aiming to this point, we propose an approach for multiperson human interaction recognition in images with keypoint‐based feature image analysis. Proposed method is a three‐stage framework. In the first stage, we propose feature‐based neural network (FCNN) for action recognition trained with feature images. Feature images are body features, that is, effective distances between a set of body part pairs and angular relation between body part triplets, rearranged in 2D gray‐scale image to learn effective representation of complex actions. In the later stage, we propose a voting‐based method for direction encoding to anticipate probable motion in steady images. Finally, our multiperson interaction recognition algorithm identifies which human pairs are interacting with each other using an interaction parameter. We evaluate our approach on two real‐world data sets, that is, UT‐interaction and SBU kinect interaction. The empirical experiments show that results are better than the state‐of‐the‐art methods with recognition accuracy of 95.83% on UT‐I set 1, 92.5% on UT‐I set 2, and 94.28% on SBU clean data set.
Coupling Deep Textural and Shape Features for Sketch Recognition

Recognizing freehand sketches with high arbitrariness is such a great challenge that the automatic recognition rate has reached a ceiling in recent years. In this paper, we explicitly explore the shape properties of sketches, which has almost been neglected before in the context of deep learning, and propose a sequential dual learning strategy that combines both shape and texture features. We devise a two-stage recurrent neural network to balance these two types of features. Our architecture also considers stroke orders of sketches to reduce the intra-class variations of input features. Extensive experiments on the TU-Berlin benchmark set show that our method achieves over 90% recognition rate for the first time on this task, outperforming both humans and state-of-the-art algorithms by over 19 and 7.5 percentage points, respectively. Especially, our approach can distinguish the sketches with similar textures but different shapes more effectively than recent deep networks. Based on the proposed method, we develop an on-line sketch retrieval and imitation application to teach children or adults to draw. The application is available as Sketch.Draw.
PTZ camera calibration based on improved DLT transformation model and vanishing Point constraints

High-precision camera calibration is the key to obtaining accurate 3D information of the target from the image. Due to the shortcomings of both the classical simplified linear model and the complete distortion model in the algorithm, a PTZ camera autocalibration method is proposed, it is an improved DLT method based on three vanishing point constraints and virtual identification point constraints. This method uses the two vanishing points obtained by cascading Hough transform of vehicle trajectory line and car body edge line and the third vanishing point obtained by geometric relationship of three vanishing points to complete the initial parameter calibration of the camera, and calculate the intrinsic parameters, rotation matrix and translation matrix in order. Then artificially filtered spatial virtual identification points are used to establish a camera parameter constraint with the actual scene non-virtual reference points, and the DLT direct linear transformation method is used for final correction. This study is tested under different rotation angles and pitch angles of traffic scene, and it uses the differences between the actual physical distance of lane line in the scene and its tested distance as the measure of the calibration error. The error of proposed method at different conditions is less than 2.5%. The test results of multiple sets of highway scenes show that the method proposed in this paper can meet the application requirements for the physical measurement error of the highway, and it has greater accuracy and better stability compared with the reference method. The obtained camera parameters can be used to accurately calculate vehicle speed and estimate spatial position.
Corpus Callosum Segmentation from Brain MRI Images Based on Level Set Method

Corpus callosum integrates left and right hemispheres of human brain. There are several methods for segmenting corpus callosum, but the existing algorithms need several steps to segment images. Therefore, we propose a simple method using level set method to segment corpus callosum. We use level set method as it can handle the structure of the brain easily. This method provides a numerical solution for processing changes in topological contours by representing a curve or surface as a zero level to a higher hyper-dimensional surface. This experiment shows that by implementing level set method to segment the corpus callosum produces Dice Similarity Coefficient (DSC) value of 85.14%.
Indigenous Women Managing Pregnancy Complications in Rural Ecuador

Previous research has explored the potential use of digital health to support maternal health in the Global South highlighting the importance of understanding the socio-cultural context to inform system design. However, the experiences of indigenous women managing pregnancy complications in Latin America remain underexplored in HCI. We present a qualitative study with 25 indigenous pregnant women in an Ecuadorian rural community looking at their experiences during complications, their antenatal care visits and their access and use of technologies. Our findings highlight key barriers that hinder the use of antenatal care services and influence women's experiences managing complications. Based on the findings, we present opportunities for digital health centered on indigenous women to enhance antenatal care in rural Ecuador.
Gender Recognition from Facial Images using Hybrid Classical-Quantum Neural Network

Gender recognition is an important function in numerous software applications that analyze facial images. This paper presents a quantum machine learning based solution towards automatic classification of gender from facial images using the hybrid classical-quantum neural network. Here we obtain a binary classifier using the knowledge of a pre-trained off-the-shelf deep neural network (DNN) combined with the transfer learning of a quantum variational circuit. The binary classifier is a hybrid network consisting of convolutional base of the DNN truncated with a dressed quantum circuit. We obtain performance better than previously reported in classifying publicly available dataset of facial images.
Surface vs Motion Gestures for Mobile Augmented Reality

Surface gestures have been the dominant form of interaction for mobile devices with touchscreen. From our survey of current consumer mobile Augmented Reality (AR) applications, we found that these applications also adopted the surface gestures metaphor as their interaction technique. However, we believe that designers should be able to utilize the affordance of the three-dimensional interaction space that AR provides. We compared two interaction techniques of surface and motion gestures in a Pokémon GO-liked mobile AR game that we developed. Ten participants experienced both techniques to capture three sizes of Pokémon. The comparison examined the two types of gestures in terms of accuracy, game experience, and subjective ratings on goodness, ease of use, and engagement. It was found that the motion gesture provided better engagement and game experience, while the surface gesture was more accurate and easier to use.
Loki: A Lightweight LWE Method with Rogue Bits for Quantum Security in IoT Devices

Internet of Things (IoT) is a platform that connects various devices through the Internet. Their abilities are limited in providing intelligent services. IoT devices are heterogeneous in nature and are able to connect sensors to simple devices that are constrained. Software and hardware attacks are a major concern in this domain. These attacks may lead to a breach of privacy, confidentiality, and malware attacks. To resolve this issue, a novel security algorithm is proposed in this paper. The proposed algorithm uses lattice-based cryptography method for encrypting gateways and cloud services and asymmetric key cryptography method for securing devices that protect the service gateways and low-energy IoT devices such as sensors. The proposed architecture uses asymmetric key cryptosystem that has nodes with a shared session key, and this session key is then further used to transfer messages. This protects the system from quantum attacks, eavesdropping, and public key attacks.
Multimedia Traffic Prediction Based on Wavelet-and Spline-extrapolation

The task of predicting self-similar traffic of IoT network objects with a significant number of pulsations and the property of long-term dependence is considered, which makes it difficult to predict in practice. Using the methods of wavelet- and spline-extrapolation based on Haar-wavelet, quadratic and B-spline function, the results of prediction of multimedia traffic are obtained. We compare the results of traffic prediction based on the Haar-wavelet and the quadratic and B-spline function using wavelet- and spline-extrapolation. This will allow the choice of one or another extrapolation method to improve the accuracy of the prediction, while ensuring scalability and the ability to use it for various IoT applications to prevent network overloads.
Detection and Segmentation of Unlearned Objects in Unknown Environment

Detecting and segmenting unlearned objects in unknown environment is a very important visual perception ability to enhance industrial intelligence. In this article, we present a novel conditional random field model integrating unimodal and cross-modal terms for detecting and segmenting object instances without knowing their categories and without sampling extra proposals. This model takes a paired image and point cloud as input, from which we first develop a set of novel category-independent features to distinguish objects. Then, a set of unary, pairwise, and higher order potentials are designed according to these category-independent features, and the cross-modal potential is introduced as a novel global constraints to keep the spatial consistency in both 2-D and 3-D modalities. In this novel model, the unlearned object detection and segmentation is treated as the process of pixel labeling. Thus, adjacent or occlusion object instances can also be separated efficiently from a labeled map. By comparison with the baseline methods, experimental results on a public RGB+D dataset show that the proposed model can obtain better performance with improved precision and recall rate. Moreover, we use the proposed method in a real industrial scene and achieve satisfactory performance.
KnowlyBERT - Hybrid Query Answering over Language Models and Knowledge Graphs

Providing a plethora of entity-centric information, Knowledge Graphs have become a vital building block for a variety of intelligent applications. Indeed, modern knowledge graphs like Wikidata already capture several billions of RDF triples, yet they still lack a good coverage for most relations. On the other hand, recent developments in NLP research show that neural language models can easily be queried for relational knowledge without requiring massive amounts of training data. In this work, we leverage this idea by creating a hybrid query answering system on top of knowledge graphs in combination with the masked language model BERT to complete query results. We thus incorporate valuable structural and semantic information from knowledge graphs with textual knowledge from language models to achieve high precision query results. Standard techniques for dealing with incomplete knowledge graphs are either (1) relation extraction which requires massive amounts of training data or (2) knowledge graph embeddings which have problems to succeed beyond simple baseline datasets. Our hybrid system KnowlyBERT requires only small amounts of training data, while outperforming state-of-the-art techniques by boosting their precision by over 30% in our large Wikidata experiment.