Reviewerly/exHarmony huggingface dataset:
On optimal cloning and incompatibility

We investigate the role of symmetric quantum cloning machines (QCMs) in quantifying the mutual incompatibility of quantum observables. Specifically, we identify a cloning-based incompatibility measure whereby the incompatibility of a set of observables maybe quantified in terms of how well a uniform ensemble of their eigenstates can be cloned via a symmetric QCM. We show that this new incompatibility measure $\mathcal{Q}_{c}$ is {\it faithful} since it vanishes only for commuting observables. We prove an upper bound for $\mathcal{Q}_{c}$ for any set of observables in a finite-dimensional system and show that the upper bound is attained if and only if the observables are mutually unbiased. Finally, we use our formalism to obtain the optimal quantum cloner for a pair of qubit observables. Our work marks an important step in formalising the connection between two fundamental concepts in quantum information theory, namely, the no-cloning principle and the existence of incompatible observables in quantum theory.
A Semantic Segmentation Method Based On Improved U-net Network

Semantic segmentation is the most important branch of image processing. Semantic segmentation based on deep learning occupies the mainstream method. CNN, FCN, U-NET and DEEPLAB series have achieved good results in the field of image semantic segmentation. The improvement methods of each network also emerge in an endless stream, but the emphasis of improving the network focuses on the feature transfer and feature fusion between network layers in each method. In this paper, based on u-net, we use deep convolution, residual autonomic force and dendrite network to improve the network model. Deep convolution can reduce the amount of calculation, deepen the depth of network, extract more abundant image information, and transmit information between network layers in a more refined way. Dendritic networks are different from ordinary neural networks. Ordinary neural networks process information as models of nerve cell bodies. The role of neural dendrites is also important in the process of information processing. Dendritic network is used as a model of neural dendrites to improve the ability of information processing. The improved network in this paper was tested on the VOC2012 dataset, and the experimental results showed that PA, mAP and IoU were 93.36%, 92.28% and 68.26% respectively. The experimental results showed that the improved network in this paper had improved in all indicators.
Gray Image Quantization Method Based on New Threshold Optimizing Technique

Images are one of the most important media information in our world it used as inputs to many functions like classification, content-based image retrieval, image feature extraction, and many images processing purposes. In these fields, the optimal image quantization and reduction of gray levels methods are considered a significant preprocess that increases the efficiency and performance of the applications and enhances the precision of its results. Image quantization has a big effect on the use of huge image datasets used in these applications whereas it reduces the size of images by retaining features and the general structure of the image. This paper proposes a new image quantization method depend on optimal selection for threshold values depending on the PSNR metric. This method uses one of the Greedy programming algorithms that is the Divide and Conquer strategy. In this strategy, the selection of the threshold value that represents an optimum division position in the image gray levels range depends on the heuristic function. The heuristic function decides whichever threshold value (new gray level) from the image gray levels range is an optimal PSNR if it is selected and added to the quantized image compared with the original image. The experimental results show that the proposed method enhances the quantized image quality compared with standard quantization methods as standard uniform quantization and k-mean clustering method and other methods. The proposed method gets efficient results in PSNR and SSIM quality measures with increasing average ranged in [1.2%-5.5%] and [2%-6.5%], respectively, compared with other methods considering different gray levels and different image types. The method time complexity analysis is explained. The experimental results show the time consuming of applying the proposed method on many images and two big datasets.
Debiasing Multilingual Word Embeddings: A Case Study of Three Indian Languages

In this paper, we advance the current state-of-the-art method for debiasing monolingual word embeddings so as to generalize well in a multilingual setting. We consider different methods to quantify bias and different debiasing approaches for monolingual as well as multilingual settings. We demonstrate the significance of our bias-mitigation approach on downstream NLP applications. Our proposed methods establish the state-of-the-art performance for debiasing multilingual embeddings for three Indian languages - Hindi, Bengali, and Telugu in addition to English. We believe that our work will open up new opportunities in building unbiased downstream NLP applications that are inherently dependent on the quality of the word embeddings used.
A classification of code changes and test types dependencies for improving machine learning based test selection

Machine learning has been increasingly used to solve various software engineering tasks. One example of their usage is in regression testing, where a classifier is built using historical code commits to predict which test cases require execution. In this paper, we address the problem of how to link specific code commits to test types to improve the predictive performance of learning models in improving regression testing. We design a dependency taxonomy of the content of committed code and the type of a test case. The taxonomy focuses on two types of code commits: changing memory management and algorithm complexity. We reviewed the literature, surveyed experienced testers from three Swedish-based software companies, and conducted a workshop to develop the taxonomy. The derived taxonomy shows that memory management code should be tested with tests related to performance, load, soak, stress, volume, and capacity; the complexity changes should be tested with the same dedicated tests and maintainability tests. We conclude that this taxonomy can improve the effectiveness of building learning models for regression testing.
A Transformation-Based Quantum Physical Synthesis Approach for Nearest-Neighbor Architectures

The physical synthesis concept for quantum circuits, the interaction between synthesis and physical design processes, was first introduced in our previous work. This concept inspires us to propose some techniques that can minimize the number of extra inserted SWAP operations required to run a circuit on a nearest-neighbor architecture. Minimizing the number of SWAP operations potentially decreases the latency and error probability of a quantum circuit. Focusing on this concept, we present a physical synthesis technique based on transformation rules to decrease the number of SWAP operations in nearest-neighbor architectures. After the qubits of a circuit are mapped onto the physical qubits provided by the target architecture, our procedure is fed by this mapping information. Our method uses the obtained placement and scheduling information to apply some transformation rules to the original netlist to decrease the number of extra SWAP gates required for running the circuit on the architecture. We follow two policies in applying a transformation rule, greedy and simulated-annealing-based policies. Simulation results show that the proposed technique decreases the average number of extra SWAP operations by about 20.6% and 24.1% based on greedy and simulated-annealing-based policies, respectively, compared with the best in the literature.
Low-Cost Image-Based Occupancy Sensor Using Deep Learning

An occupancy sensor is going to be an integral part of smart buildings for energy efficiency as well as for providing human-centric lighting. This paper presents a low-cost image-based alternative for conventional occupancy sensors using deep learning. The developed system works as a standalone unit and can integrate with heating, ventilation and air conditioning (HVAC) and lighting control schemes. Here, a Raspberry Pi 3B + is utilized as the hub for occupancy detection. Single-shot multi-box detection (SSD) is used as the primary architecture and is compared with you only look once (YOLO), the test results are computed for several test rooms, and an evaluation of the practical requirements in terms of camera and images captured for accurate detections is measured based on positives obtained. Here, a low-cost system is designed which avoids the use of multiple sensors and is most suitable for offices and libraries, and classrooms.
A theoretical justification and framework for scenario planning in SMEs

This article provides a theoretical framework and justification for scenario planning, a practice that enables small businesses to be more agile and resilient. Agile and resilient companies are better positioned to succeed in an unknown future. Scenario planning offers a way for companies to think creatively about opportunities, threats, and solutions while still operating successfully in the known present. Compared to strategic planning, scenario planning is less resource intensive and therefore offers small businesses a more realistic way to build for the future. This article contrasts scenario planning with strategic planning while providing recommendations for adapting the former into a small business environment.
Explore Data, Enjoy Yourself - KUbism, A Playful Approach to Data Exploration

The increasing relevance of information in today’s world has grown the challenge of accessible data exploration. Most existing approaches are primarily utilitarian, where the motivation for exploration remains mostly extrinsic, correlated to the value of the outcome. In light of our natural drives for exploration, we argue that data exploration can be made more rewarding and enjoyable via interfaces that leverage the intrinsic motivations of users. To that end, we present KUbism, a prototype emphasizing the hedonic value of data exploration through a playful interface built with elements of Minecraft gameplay. Implemented as a Terasology module, KUbism invites users to explore data in voxel worlds populated with data blocks. We conducted a user study (n = 41) to validate our approach in both hedonic and utilitarian dimensions. Our results demonstrate that KUbism allows effective data exploration while arousing curiosity and joy, thereby reinforcing the extrinsic value of data exploration with intrinsic rewards.
Research on Recognition of Financial Fraud Based on Data Mining

This paper builds a mathematical model based on data mining to study the related issues of fraud. This paper constructs a recognition model from two aspects of traditional recognition and intelligent algorithms, uses Logistic model, BP neural network model and decision tree model to identify fraudulent data in manufacturing, and uses confusion matrix to use classification accuracy, recall rate, and F value And accuracy and other indicators to evaluate the accuracy of recognition. Therefore, substitute manufacturing-related data into the recognition model, compare the recognition results of the three models, and select the model with the highest recognition accuracy to predict the sixth year of the manufacturing industry.
Stereophotogrammetry Camera Pose Optimization

Stereophotogrammetry makes use of calibrated camera pairs to obtain three-dimensional information from two-dimensional images. The accuracy of the extracted measurements is extremely dependent on the selection and setup of the camera system. For a given test object and desired viewing orientation, there is no one “correct” stereo camera setup, but rather a range of potential setups with some approaching an optimal system with respect to maximizing the measurement resolution. The open-ended nature of this test design exercise is compounded by equipment availability and the fact that many of the setup parameters have dependent characteristics, e.g., changing focal distance will affect stand-off distance, field of view, and image projection, among others. This work describes a planning tool that utilizes projective and Euclidian geometry to iteratively estimate optimal camera poses for available equipment, determines the most efficient image size, and also performs checks for lens diffraction, minimum focal distance, and adequate depth of field. Integrating a finite element model with these calculations further extends planning capabilities by allowing (1) an accurate definition of the volume to be imaged and (2) the ability to estimate response displacements in pixels due to an arbitrary excitation applied to the test object. This latter capability is critical for pre-test determination of the chosen camera setup’s ability to successfully extract three-dimensional measurements. The theory and workflow are presented along with an experimental demonstration.
An Efficient Method for Automated Breast Mass Segmentation and Classification in Digital Mammograms

Background: Automatic detection and classification of breast masses in mammograms are still challenging tasks. Today, computer-aided diagnosis (CAD) systems are being developed to assist radiologists in interpreting mammograms. Objectives: This study aimed to provide a novel method for automatic segmentation and classification of masses in mammograms to help radiologists make an accurate diagnosis. Materials and Methods: For an efficient mass diagnosis in mammograms, we proposed an automatic scheme to perform both mass detection and classification. First, a combination of several image enhancement algorithms, including contrast-limited adaptive histogram equalization (CLAHE), guided imaging, and median ﬁltering, was investigated to enhance the visual features of breast area and increase the accuracy of segmentation outcomes. Second, the density of discrete wavelet coefficient density (DDWCs), based on the quincunx lifting scheme (QLS), was proposed to find suspicious mass regions or regions of interest (ROIs). Finally, mass lesions that appeared in the mammogram were classiﬁed into four categories of benign, probably benign, malignant, and probably malignant, based on the morphological shape. The proposed method was evaluated among 1593 images from the Curated Breast Imaging Subset-Digital Database for Screening Mammography (CBIS-DDSM) dataset. Results: The experimental results revealed that the suspected region localization had 100% sensitivity, with a mean of 6.4 ± 4.5 false positive (FP) detections per image. Moreover, the results showed an overall accuracy of 85.9% and an area under the curve (AUC) of 0.901 for the mass classification algorithm. Conclusion: The present results showed the comparable performance of our proposed method to that of the state-of-the-art methods.
Demystifying Democracy: Incentivizing Blockchain Voting Technology for an enriched Electoral System

Online voting is one of the new, upcoming alternatives to the traditional systems like Electronic Voting Machine (EVM) and age-old ballot paper. Centralized online voting systems are vulnerable to a plethora of problems. Blockchain, with its characteristics, solves many of the problems faced in online voting and traditional systems. Blockchain is immutable i.e. after a vote is cast, it cannot be undone, and it is decentralized and distributed and therefore there is no central authority over the voting system. These characteristics of Blockchain ensure transparency in the election. The paper involves the concept of Blockchain technology and its application in the field of Electronic Voting. The paper proposes a system that implements online voting with an underlying Blockchain system. The proposed system can be implemented to provide services to medium-scale and large-scale corporations. The system has been tested and deployed on the Rinkeby test network, using Ethereum as a framework to develop smart contracts using Solidity. This system can conduct multiple elections simultaneously. This paper also contains a detailed analysis of the operating expenditure in the form of graphs which were tested in several elections. This system has been tested for scalability, efficiency, and usability according to the requirements.
Adaptive Learning Image Tracking Algorithm Based on Characteristic Fusion

Abstract To solve the problem of model free real-time image tracking, this paper proposes an image tracking algorithm based on characteristic fusion to adaptively learn the historical characteristics of the target(CFAL). In this algorithm, the color feature is redesigned by using the idea of hog to achieve the feature consistency; the particle filter is combined with the reality constraint to improve the matching efficiency. Finally, the algorithm is programmed and tested in reality, animation and video scenes. The experimental results show that adaptive feature learning and weight adjustment can improve the tracking effect and CFAL can track the target stably and reliably under the environment of attitude change and partial occlusion.
Classification of observations into von Mises-Fisher populations with unknown parameters

The problem of classification of an observation into von Mises-Fisher populations is considered when the concentration parameters and the mean directions are unknown. For two von Mises-Fisher distr...
A Literature Review of Various Techniques to Perform Encryption and Decryption of Data

Network is a collection of nodes. The basic objective of network is to transfer information from one place to another. Obviously, this information is required to be secured from third party access. The concept of cryptography was born from the need to secure critical information exchanged over insecure network. The sender crypts or encodes the information with a secret key while practicing cryptography so that it can only be understood by the tender recipient. On the other hand, cryptanalysis means unwanted access without the secret key to information. The cryptography makes use of different techniques like Diffie Hellman, AES, RSA, DES, IDEA, BLOWFISH, x.509, PKI based infrastructure and digital signatures for converting plain text in to corresponding chipper text. All these algorithms have their importance in different circumstances. The working of these algorithms is well explained in this paper. This paper contains an investigation of various cryptographic algorithms. This investigation is performed by review of some research papers.
ArchivesChain:Distributed PKI Archives System

Electronic archives are electronic files with very high security requirements. We use block chain technology to implement a distributed PKI system. On this basis, we build an archives system ArchivesChain based on block chain to solve the drawbacks of the current centralized storage of archives and the security problems caused by the excessive centralization of traditional PKI.We have implemented multi-point authentication of archive data through block chain technology, including certificate registration and verification confirmation when archives are generated, ensuring data security during the process of receiving and migrating archives, and generating archive certificates on the chain, similar to handling identity cards for each electronic file.Compared with CA authentication systems in traditional PKI, the block chain-based PKI in our system is provided by all members of the Federation to verify and authenticate the data, thus ensuring that archives information can not be tampered with and providing authentic authentication with credibility.
Recommendation system using machine learning

Due to increase of data on internet, there is an increased dependency on internet by people Thus, recommendation systems help people by suggesting products where is overload of information on ecommerce websites.There are various methods for recommendation.This paper study about various techniques used in designing of recommendation system with machine learning algorithm.
Low-Rank Regularized Joint Sparsity for Image Denoising

Nonlocal sparse representation models such as group sparse representation (GSR), low-rankness and joint sparsity (JS) have shown great potentials in image denoising studies, by effectively exploiting image nonlocal self-similarity (NSS) property. Popular dictionary-based JS algorithms apply convex JS penalties in their objective functions, which avoid NP-hard sparse coding step, but lead to only approximately sparse representation. Such approximated JS models fail to impose low-rankness of the underlying image data, resulting in degraded quality in image restoration. To simultaneously exploit the low-rank and JS priors, we propose a novel low-rank regularized joint sparsity model, dubbed LRJS, to enhance the dependency (i. e., low-rankness) of similar patches, thus better suppress independent noise. Moreover, to make the optimization tractable and robust, an alternating minimization algorithm with an adaptive parameter adjustment strategy is developed to solve the proposed LRJS-based image denoising problem. Experimental results demonstrate that the proposed LRJS outperforms many popular or state-of-the-art denoising algorithms in terms of both objective and visual perception met-
News-Comment Relevance Classification Algorithm Based on Feature Extraction

In recent years, with the rapid development of the mobile Internet, it has become easier for users to read news and corresponding comments. Most people get used to reading news on-line. However, sociologists have shown that up to 50 percent of the comments in a news article are irrelevant to the news content. In this paper, we investigate the news-comments relevance analysis problem. We formalize such problem as a classification problem and proposed a news-comment relevance classification algorithm, based on BERT feature extraction. This algorithm uses a model trained in the extensive semantic database to extract the feature vectors of news and its comments and inputs the extracted feature vectors into a full-connection layer network to complete the classification task. Several experiments are carried out on five public datasets, and the experimental results show that the model I proposed has good performance.
AWGN-Based Image Denoiser using Convolutional Vision Transformer

This paper presents a new method for denoising an image corrupted with Additive White Gaussian Noise (AWGN). We adopt the effectiveness of Convolutional Vision Transformer (CvT) to suppress the occurred noise on an image. The proposed method exploits the residual learning approach in order to estimate and reduce the noise on a noisy image. Herein, the model is trained in the end-to-end manner to capture the relation between the noisy image and its noise map. The experimental results show the effectiveness of the proposed method in terms of subjective and objective measurements.