Reviewerly/exHarmony huggingface dataset:
Nonlinear relative dynamics

Covariance and correlation are two widespread tools in statistics and finance to measure how two entities vary together. Correlation measures the linear relationship between two variables and is not an adequate measure when the two exhibit nonlinear relationships. In this paper, we extend linear correlation to an α-grade monomial one; α values that maximize correlation indicate which type of nonlinear relationship data exhibit. Lagrange representation allows us to define a contro-correlation measure to represent how two entities are not related and a measure of relative variability. Finally, a simulation study and a real-world data application are performed to assess the performance of the proposed methodology.
Detecting the event of a single photon loss on quantum signals

Abstract We design a scheme for detecting a single photon loss from multi-modal quantum signals transmitted via a fiber or in free space. This consists of a special type of unitary coding transformation, the parity controlled-squeezing, applied prior to the transmission on the signal composed by information and ancilla modes. At the receiver, the inverse unitary transformation is applied—decoding, and the ancilla mode is measured via photon detection. The outcome reveals whether a photon loss has occurred. Distortion of the information part of the signal caused by an ancilla photon loss can be corrected via unitary transformation while loss of a photon from the information part of the signal can be detected with the probability exponentially close to unity but cannot be corrected. In contrast to the schemes of decoherence free subspaces and quantum error correction protocols, this method allows one to make use in principle of entire Hilbert space dimensionality. We discuss possible ways of synthesizing the required encoding–decoding transformation.
Automatic Detection for Reused Open Source Codes Based on Similarity Identification of Software Networks

Software plays an increasingly important role in today's world. With the advent of open-source software, an increasing number of developers begin to focus on and apply open-source software as a basic tool for their program development. However, at the same time, introducing open-source software into their own software introduces various types of defects and disadvantages. These unknown risks may cause incalculable economic loss aud credit crises if they were to be exploited in the future. Therefore, it is an important and urgent problem to detect the components of open-source software that may be reused when outsourcing software. To help detecting opensource software components in large-scale software projects, this paper proposes automatic identification technology for subnetworks with similar structural characteristics. This technology is based on node role classification, node similarity matching, and similar subnetwork search. This subject applies complex network technology to the comparison of software networks. In contrast to traditional code detection technology, this study does not constrain the text information of the software's source code. Considering that the basic skeleton structure of an application, in the processes of code reuse and features, remains the same, its network structure is used instead of its software structure to avoid problems such as poor detection results of similar codes as a result of text modification. This technology starts from the software features and the structure of software network.
An Application Model of GNN in Session Recommendation

Session-based recommendation is used to generate a session recommendation list for the current user according to the existing session information. Most of the existing session-based recommendation models with deep learning only focus on the session information of users, rarely consider the interaction recommendation between similar users, and ignore the user's interest orientation, so the accuracy of the session-based recommendation still has space to improve. To solve above problems, an application model of GNN in session recommendation was proposed. Firstly, take the item's number and order accessed by user session as the basis of clustering, k-means algorithm was used to cluster the sessions to generate k similar sessions clusters. Secondly, choose the appropriate atomic interest on the basis of session clustering to expand user's session length and strengthen user's interest orientation. Thirdly, train the enhanced user sessions by using graph neural network to obtain the latent vector of items, and predict the probability of item being clicked according to the items and the latent vector of the given session, and then generate session recommendation list. Finally, the proposed model is compared with other session recommendation models in YOOCHOOSE and DIGINETICA dataset. The experimental results show that the proposed model has higher recommendation accuracy.
Dual mean field annealing scheme for binary optimization under linear constraints

It is shown that certain general classes of constrained binary optimization tasks can be solved with increasing accuracy by a first order mean field approximation of the Boltzmann distribution of the associated Lagrangian as the instance size grows. The formalism is thoroughly analyzed for the quadratic and multidimensional knapsack models. In these cases analytical expressions for the convergence of the optimality gaps are given, which are experimentally verified.
Queuing Delay for Quantum Networks

Queuing delay is critical for quantum networks. This paper introduces a tractable model for analyzing the queuing delay of quantum data, referred to as quantum queuing delay (QQD). The model employs a dynamic programming formalism. Using this model, we develop a cognitive-memory-based policy for memory management and show that this policy can decrease the average queuing delay exponentially with respect to the memory size. Such a significant reduction can be traced back to the use of entanglement, a peculiar quantum phenomenon that has no classical counterpart. Numerical results validate the theoretical analysis and demonstrate the desirable performance of the developed policy.
Micro-Transmission Mode Analysis of Ideological and Political Education under Big Data Background

Based on the theory of micro propagation path of ideological and political education in colleges and universities, this paper excavates the frequent patterns in the information dissemination tree represented by micro-blog, and analyzes the mode in the information theme dimension. Based on the formal description of attribute graph and information communication graph of social network, the application of MltreeMiner algorithm in ideological and political education communication mode is discussed. In the theme dimension, the frequent communication mode is analyzed to explore the relationship between the theme and the communication mode. Finally, mining the information dissemination mode of social network on the real data set, and analyzing the relationship between the theme and the information dissemination mode, we acquire the distribution of the theme under each dissemination mode, and provide a useful reference for analyzing the actual effect of Ideological and political education under big data background.
Developing an Android-Based Test on Colligative Properties of Solution

Abstract Android-based tests in chemistry are still limited and few in development. This research has been carried out to describe the result of developing an Android-based test on the colligative properties of the solution. The method used in this research was descriptive qualitative. The product/application was created using the Android Studio application (in the.apk format) and can be used on smartphones. Moreover, its details are described, including every visual appearance. The application is interesting, easy to use, can be accessed offline and visualizes animated videos so that it can be used to measure student’s ability to explain the colligative properties.
Feature Selection Algorithm Based on Sparse Score and Correlation Analysis

Aiming at the high computational complexity of classification prediction algorithms for high-dimensional data with large scale and high dimensionality, an effective solution is to select a small number of feature subsets with high correlations among the many candidate features of high-dimensional data, and remove the irrelevant and redundant features. In this paper, based on the correlation of sparse scores and category features, the feature selection (ISSFS) algorithm based on sparse score and correlation analysis is studied to select the input features of the learning algorithm. The algorithm calculates the optimal feature subset by comprehensively analyzing the sparse score of each feature in the dataset and the degree of correlation between the feature and the category, so as to achieve the purpose of dimension reduction of high-dimensional data features. Simulation experiments show that the algorithm achieves better feature selection on UCI dataset and ice hockey game dataset, and the classification effect is good.
Fast Semantic Feature Extraction Using Superpixels for Soft Segmentation

In this work, we address the problem of extracting high dimensional, soft semantic feature descriptors for every pixel in an image using a deep learning framework. Existing methods rely on a metric learning objective called multi-class N-pair loss, which requires pairwise comparison of positive examples (same class pixels) to all negative examples (different class pixels). Computing this loss for all possible pixel pairs in an image leads to a high computational bottleneck. We show that this huge computational overhead can be reduced by learning this metric based on superpixels. This also conserves the global semantic context of the image, which is lost in pixel-wise computation because of the sampling to reduce comparisons. We design an end-to-end trainable network with a loss function and give a detailed comparison of two feature extraction methods: pixel-based and superpixel-based. We also investigate hard semantic labeling of these soft semantic feature descriptors.
The Human Visual System Based Color QR Codes

QR codes have been used in consumer advertising fields widely and often possesses a significant visual area. In general, QR codes is small, black square block which is generally visually unpleasant. Therefore, visual QR codes could improve the exterior appearance and social acceptability. In this study, a content based encoding method for creating visual color QR codes is proposed. The proposed approach adopts the Human Visual System consideration to achieve high quality colorful QR codes. Several measurements to evaluate the robustness of the proposed scheme is performed which includes Gaussian noise, Gaussian blurring, tilted angle attacks. The visual metric comparison of the encoded image quality is also studied. In summary, the proposed technique outperforms the previous researches and it is a promising technique that can be implemented in real applications.
Using Noisy Word-Level Labels to Train a Phoneme Recognizer based on Neural Networks by Expectation Maximization

The Connectionist Temporal Classification (CTC) technique can be used to train a neural-network based speech recognizer. When the technique is used to train a phoneme recognizer, it is required that training data should be annotated with phoneme-level labels. This is not feasible if large speech databases are used. One approach to make use of such speech data is to convert the word-level transcriptions into phoneme-level labels, followed by a CTC training. The problem of this approach is that the converted phonemelevel labels may mismatch the audio content of the speech data. This paper uses a probabilistic model to describe the probability of observing the noisy phoneme-level labels given an utterance. The model consists of a neural network which predicts the probability of any phoneme sequence, and another so-called mismatch model to describe the probability of disturbing a phoneme sequence to another. Based on the Expectation-Maximization (EM) framework, we propose a training algorithm which can simultaneously learn parameters of the neural-network and the mismatch model. Effectiveness of our method is verified by comparing recognition performance of our method with a conventional training method on TIMIT corpus.
Towards accurate camera geopositioning by image matching

In this work, we present a camera geopositioning system based on matching a query image against a database with panoramic images. For matching, our system uses memory vectors aggregated from global image descriptors based on convolutional features to facilitate fast searching in the database. To speed up searching, a clustering algorithm is used to balance geographical positioning and computation time. We refine the obtained position from the query image using a new outlier removal algorithm. The matching of the query image is obtained with a recall@5 larger than 90% for panorama-to-panorama matching. We cluster available panoramas from geographically adjacent locations into a single compact representation and observe computational gains of approximately 50% at the cost of only a small (approximately 3%) recall loss. Finally, we present a coordinate estimation algorithm that reduces the median geopositioning error by up to 20%.
Applications of Deep Learning Using Quaternary Hash Codes for Image Retrieval

Image retrieval technology has made great breakthroughs in the development of computer vision. Feature extraction is crucial to image retrieval as a good method not only brings convenience to image recognition, but also improves the performance of image retrieval system. Many traditional methods only extract shallow features like the statistics of brightness, color components and texture measures, which miss the contextual semantic information and cannot behave well in image retrieval. However, feature extraction based on deep learning is able to obtain better features with semantic information. Therefore, much work has shown that integrating neural network with hash codes performs better in retrieval tasks. Encouraged by this, we propose an image hash retrieval algorithm by optimizing structures in deep layer aggregation network (DlaNet). This model is mainly composed of an improved net called DlaNet-V and a four-valued hash code scheme. The DlaNet-V is optimized on the basis of DlaNet, improving the efficiency. Meanwhile, the binary hash codes are switched into the quaternary hash codes to make the model more robust and efficient. Experiments are conducted on the CIFAR-10 data set and a medical device image data set collected by authors. Results show that the image retrieval based on DlaNet-V and quaternary hash code is more accurate and stable.
A Video Semantic Segmentation Method Based on FCN and Data Argumentation

Video semantic segmentation is an important and fundamental problem in computer vision. It has broad application prospects in the fields of mobile robot, drone, intelligent driving and monitoring. With the development of neural networks, the models commonly adopted are all based on full convolutional network (FCN). However, current methods are limited by a small training set, which makes it difficult to improve the segmentation accuracy. In this paper, we propose a robust method that uses different data argumentation methods to increase the data set according to different characteristics of the scene. On the basis of analyzing different video features, targeted data argumentation techniques are selected to increase training samples. Experimental results show that data argumentation techniques can significantly improve the accuracy of video semantic segmentation compared with traditional training methods that ignore video features.
Virtual Conversation with Real-Time Prediction of Body Moments/Gestures on Video Streaming Data

The exisitng conversation system where the user interacts with the virtual system with voice and virtual system replies to the user based on what user speaks. In this context whenever user makes some gestures to communicate with the virtual system, the virtual system will miss out those communications. For example, user instead of speaking, may nod head for “yes” or “no” and user can also use hand signals to respond to the virtual system. If these events are not addressed then the conversation is not very interactive and natural human-like interaction will start losing important information. The paper describes how the user body moments/gestures will help effective conversation with the virtual system and virtual conversation system can understand the user misspelled conversation, missed conversation effectively with user gesture/body movements.
Room-area Adhoc G roup Management for Timely Coordination of Voice-enabled Consumer Devices

While more consumer devices become smarter with voice-based natural interfaces, this trend poses such a problem that a user might frequently experience undesirable responses from nearby devices, i.e., seemingly uncoordinated and often redundant responses. In this paper, we present a lightweight adhoc group management leveraging inaudible sound and RF local-connectivity in room area, thereby enabling to efficiently suppress the uncoordinated interactions of devices upon a user’s voice command via instant group communication.
Comparison and Visualization of Code Clone Detection Results

Many techniques for code clone detection have been proposed and implemented as clone detectors in the past. These studies show that a result of code clone detection changes drastically for different tools and/or their detection parameters. Therefore, it is important to apply different clone detectors or different parameters and to identify the different or common parts of the obtained detection results. In this paper, we propose a method for comparison and visualization of detection results based on the correspondence of clone pairs. It enables developers to compare detection results by different tools and/or their detection parameters. Using this method, we will show the comparison results of an OSS using two code clone detectors, CCFinderX and NiCad.
Applying a 3D range sensor to enhance children's experience of art

This study proposed a system that uses children's imagination to boost their appreciation of art. Since pretend play improves children's imagination, we propose a system using pretended play to increase art appreciation in a way children find interesting. With the proposed system, a player can interact with projections of animal footprints that respond to human movement. The system, which we have developed and explain in this paper, measures the position of the player using a Kinect sensor and the footprints change based on the position of the player. In this manuscript, we describe the outline of the under developing system. In addition, we demonstrate using experiments that the Kinect sensor can be applied to accurately recognize the body positions of users. This is the first step towards realizing the proposed art system for children.
A Relation-Algebraic Treatment of the Dedekind Recursion Theorem

The recursion theorem of Richard Dedekind is fundamental for the recursive definition of mappings on natural numbers since it guarantees that the mapping in mind exists and is uniquely determined. Usual set-theoretic proofs are partly intricate and become lengthy when carried out in full detail. We present a simple new proof that is based on a relation-algebraic specification of the notions in question and combines relation-algebraic laws and equational reasoning with Scott induction. It is very formal and most parts of it consist of relation-algebraic calculations. This opens up the possibility for mechanised verification. As an application we prove a relation-algebraic version of the Dedekind isomorphism theorem. Finally, we consider two variants of the recursion theorem to deal with situations which frequently appear in practice but where the original recursion theorem is not applicable.
ABEONA: An Architecture for Energy-Aware Task Migrations from the Edge to the Cloud

This paper presents our preliminary results with ABEONA, an edge-to-cloud architecture that allows migrating tasks from low-energy, resource-constrained devices on the edge up to the cloud. Our preliminary results on artificial and real-world datasets show that it is possible to execute workloads in a more efficient manner energy-wise by scaling horizontally at the edge, without negatively affecting the execution runtime.
Design and Development of 4-Bit Gray Code Counter Circuit Using Reversible Logic Gate

Aim: This paper proposed the design and development of 4-Bit Gray Code Counter Circuit Using Reversible Logic Gate. Methods: The 4-Bit Gray Code Counter Circuit can be design using SAM gate, Feynman gate (FG), double Feynman gate (DFG) and NOT gate. The proposed circuit is the combined application of 4-bit binary asynchronous counter and 4-bit binary to gray code converter circuit. Results: The proposed gray code counter is designed using four no. of SAM gate, three no. of DFG, one FG and seven reversible NOT gate. The QC, GO and CI of proposed circuit are correspondingly 30, 4 and 7. Conclusion: The novel reversible gray code counter have been designed using existing reversible logic gate. The proposed gray code counter is designed using four no. of SAM gate, three no. of DFG, one FG and seven reversible NOT gate. The QC, GO and CI of proposed circuit are correspondingly 30, 4 and 7.
OCR-friendly Image Synthesis using Generative Adversarial Networks

With quality being a deciding factor in the accuracy of OCR predictions for a given source image, there comes the need of pre-processing methods to improve the quality of an image before undergoing this process. Towards that, we present a GAN-based method targeted at improving the quality of source image in the fields of image resolution, blur and noise. The model uses an encoder trained to generate latent image representations for a low-quality image, the representations corresponding to blur and noise types present in the image. These representations act as inputs to the constructed conditional GAN. Besides these labels, the generator takes a low-quality image as input and is trained to generate a high-quality image as represented by the target images.
Robustness and Imperceptibility Enhancement in Watermarked Images by Color Transformation

One of the effective methods for the preservation of copyright ownership of digital media is watermarking. Different watermarking techniques try to set a tradeoff between robustness and transparency of the process. In this research work, we have used color space conversion and frequency transform to achieve high robustness and transparency. Due to the distribution of image information in the RGB domain, we use YUV color space, which concentrates the visual information in the Y part. Embedding of the watermark is performed in the DCT coefficients of the specific wavelet subbands. Experimental results show high transparency and robustness of the proposed method.
Simplifying Electronic Testing Environment with SLAM Based Handheld Augmented Reality System

Current electronic testing measurements during embedded software development lack simultaneous multiple real-time measurements with location-specific information. To address this problem with a focus on electronic subsystem testing in the space industry an Augmented Reality (AR) solution is presented in form of a technology demonstrator. This Simultaneous Localization And Mapping (SLAM) system based on the Handheld Augmented Reality (HAR) concept provides an affordable zero-time installation augmented reality system that blends simultaneously multiple real-time measurements displayed exactly where they are measured. The system is composed of a handheld device and so-called Smart Probes, or miniature Bluetooth Low Energy (BLE) devices attached electronically to the measured specimen. This system may, with further development, become an alternative to a multimeter, an oscilloscope, and a logic analyser.