Reviewerly/exHarmony huggingface dataset:
A Lightweight Network for Fast Semantic Segmentation

Semantic segmentation is a fundamental task in computer vision and is widely used in industry. However, current state-of-the-art architectures usually bring heavy computation complexity, making it hard to meet the demand for real-time, and can not be implemented in industry. In this paper, we propose a lightweight network to complete fast segmentation. Our network follows encoder-decoder style, which encodes rich spatial information at shallow layers and gains sufficient semantic information at deep layers. At the decoder part, we use attention mechanism to re-weight features and gradually fuse high-level features back to low-level features. We evaluate our network on Cityscapes dataset. Our method achieves an accuracy of 68.0 % mean intersection over union, and runs at 50.7 frames per second at full resolution (1024x2048) on one NVIDIA GeForce GTX 1080Ti card.
The research of source-load transaction mechanism based on blockchain

Abstract With the development of distributed renewable energy and the energy Internet, the cost of distributed energy participating in traditional power transactions continues to rise, and there is an urgent need for more reasonable transaction mechanisms and efficient information technology to improve the efficiency of distributed energy transactions. The characteristics of blockchain technology are in line with the transaction needs of distributed renewable energy, and it has development potential in energy transactions. Based on blockchain technology, this paper designs a source-load transaction mechanism in which the virtual energy medium is the equivalent of the intermediary. This mechanism can avoid the problem of credit asymmetry between buyers and sellers. We design from three aspects: transaction, payment, and settlement, and verify the actual application. The results show the feasibility and effectiveness of the transaction, provide a new way for multi-frequency and small transaction volume of new energy transactions, and provide new solutions for the consumption of new energy such as photovoltaics and wind power.
Attentive Inception Module based Convolutional Neural Network for Image Enhancement

In this paper, the problem of image enhancement in the form of single image superresolution and compression artifact reduction is addressed by proposing a convolutional neural network with an inception module containing an attention mechanism. The inception module in the network contains parallel branches of convolution layers employing filters with multiple receptive fields via filter dilation. The aggregated multi-scale features are subsequently filtered via an attention mechanism which allows learned feature map weighting in order to reduce redundancy. Additionally, a long skip attentive connection is also introduced in order to process the penultimate feature layer of the proposed network. Addition of the aforementioned attention modules introduce a dynamic nature to the model which would otherwise consist of static trained filters. Experiments are performed with multiple network depths and architectures in order to assess their contributions. The final network is evaluated on the benchmark datasets for the aforementioned tasks, and the results indicate a very good performance.
On Information Links

In a joint work with D. Bennequin, we suggested that the (negative) minima of the 3-way multivariate mutual information correspond to Borromean links, paving the way for providing probabilistic analogs of linking numbers. This short note generalizes the correspondence of the minima of k-multivariate interaction information with k Brunnian links in the binary variable case. Following Jakulin and Bratko, the negativity of the associated K-L divergence of the joint probability law with its Kirkwood approximation implies an obstruction to local decomposition into lower order interactions than k, defining a local decomposition inconsistency that reverses Abramsky's contextuality local-global relation. Those negative k-links provide a straightforward definition of collective emergence in complex k-body interacting systems or dataset.
Comparative study of select non parametric and ensemble machine learning classification techniques

Supervised classification is a machine learning approach where data is classified among various discrete classes which could be binary or multi-class. Decision Tree, random Forest, Rotation Forest, AdaBoost, Extra Tree, XGBoost are some of the ensembles that classify data on trained model under certain criterion. AUROC and in-sample out-sample error is used as performance measure of algorithms on benchmark datasets. Hyperparameter tuning is done to improve performance and avoid overfitting.
A hybrid approach for image denoising using explicit guided filter and collaborative wiener filter

In this paper, we propose an image denoising strategy which combines the edge-preserving property of an explicit filter, known as the guided filter, with the de-blurring property of a collaborative wiener filter. Removing noise from an image without losing the main image features such as object edges, corners, and other fine structures, while minimizing the blurring effects in the denoised image is the main objective of our proposed strategy. The guided filter is used in the preprocessing step and it takes into account the content from a reference image and transfers the structure of this image to the output, i.e. preprocessed image. The noisy input image is then processed by the wiener filter, which also involves the use of this preprocessed output image for collaborative filtering. The experimental results of our proposed approach show that not only the edges and other refined features in the input noisy image are preserved to a great extent, but also the extent of blurring is reduced to a satisfactory level. Our results, when compared to those of Guided filter, demonstrate that we are able to achieve a better denoising performance than this approach, regardless of the type of image used.
Structural Similarity of Boundary Conditions and an Efficient Local Search Algorithm for Goal Conflict Identification

In goal-oriented requirements engineering, goal conflict identification is of fundamental importance for requirements analysis. The task aims to find the feasible situations which make the goals diverge within the domain, called boundary conditions (BCs). However, the existing approaches for goal conflict identification fail to find sufficient BCs and general BCs which cover more combinations of circumstances. From the BCs found by these existing approaches, we have observed an interesting phenomenon that there are some pairs of BCs are similar in formula structure, which occurs frequently in the experimental cases. In other words, once a BC is found, a new BC may be discovered quickly by slightly changing the former. It inspires us to develop a local search algorithm named LOGION to find BCs, in which the structural similarity is captured by the neighborhood relation of formulae. Based on structural similarity, LOGION can find a lot of BCs in a short time. Moreover, due to the large number of BCs identified, it potentially selects more general BCs from them. By taking experiments on a set of cases, we show that LOG I ON effectively exploits the structural similarity of BCs. We also compare our algorithm against the two state-of-the-art approaches. The experimental results show that LOGION produces one order of magnitude more BCs than the state-of-the-art approaches and confirm that LOGION finds out more general BCs thanks to a large number of BCs.
Computational based on GUI MATLAB for back propagation method in detecting climate change: case study of mataram city

Abstract Climate change is a global phenomenon in which its impact can be perceived locally. In the face of this phenomenon, it is necessary to prepare and overcome this threat by developing various mitigation and adaptation strategies for climate change. To support this strategy, climate data such as temperature (maximum and minimum), rainfall, number of rainy days, as well as necessary information in the future need to be detected to prepare good and directional policies. The study aims to determine the climate parameters in Mataram city using forecasting by constructing the algorithm the Back Propagation method. In using the Matlab GUI application by analyzing climate change in Mataram city by using monthly data for the last 10 years. In the simulation stage, the data taken are the maximum temperature, minimum temperature, rainfall, and the number of rainy days in Mataram city. Based on the results of the training and testing process for climate data in the city of Mataram with a prediction of maximum temperature occurred in November at 32,58 0 C and maximum rainfall occurred in December as much as 399.2931 mm, while for the maximum number of rainy days occurred in January with a total of 21.99 days. These results can be used as an alternative policy in the handling and preparation of extreme climate change.
Non-classicality in an open two-mode parametric amplifier cavity containing a Λ-qutrit system

Abstract In the presented paper, we introduce an analytical description for a dissipative two-mode parametric amplifier coherent cavity containing a three-level system (qutrit). Based on normalized correlation function, Q-function and its associated Wehrl entropy, the dynamics of the quantum phenomena: two-mode cavity non-classically, qutrit phase space information, and quantum coherence are investigated under the physical parameters: qutrit-cavity interactions, initial coherent intensity, and the dissipation. It is found that the generated quantum phenomena, due to the qutrit-cavity interaction, depend on the physical parameters of the initial states and the dissipation. The robustness of the quantum phenomena against the dissipation can be enhanced by decreasing the initial coherent intensity cavity. The stability and strength of the generated bunching/anti-bunching behaviour can be controlled by the cavity dissipation.
Design of medical image enhancement algorithm based on Python

The accuracy of medical diagnosis depends on the accuracy of image recognition, so the details of the preprocessing of medical images are more stringent, especially the edges of images that contain a lot of information need to be enhanced. Therefore, this article chooses to combine various enhancement methods in the spatial domain and apply python software to medical image enhancement. The experimental results show that the algorithm not only effectively retains the texture and details in the medical image, but also enhances the contour and edge of the medical image, so that some details that cannot be observed by the naked eye appear. At the same time, the use of python makes the whole algorithm more realized. Fast and more conducive to medical staff to observe the focus of the medical image.
Information Acquisition Chatbot System using LUIS

The college information chatbot system is enhanced using the Naïve Bayes classification algorithm that analyzes user's queries and messages. This system responds appropriately to the queries that are posed by the user using in-built Artificial Intelligence (AI) with an effective Graphical User Interface (GUI) tool. The queries posed by the user were analyzed by the chatbot using a cognitive service named Language Understanding Intelligent System (LUIS), which is designed by Microsoft. It is integrated with the Skype application which can be downloaded and installed from the play store on the user's smartphone.
Real-time Face Tracking in Surveillance Videos on Chips for Valuable Face Capturing

Face capturing is a task to capture and store the "best" face of each person passing by the monitor. To some extent, it is similar to face tracking, but uses a different criterion and requires a valuable (i.e., high-quality and recognizable) face selection procedure. Face capturing systems play a critical role in public security. When deployed on edge devices, it is capable of reducing redundant storage in data center and speeding up retrieval of a certain person. However, high computation complexity and high repetition rate caused by ID switch errors are major challenges. In this paper, we propose a novel solution to constructing a real-time low-repetition face capturing system on chips. First, we propose a two-stage association algorithm for memory-efficient and accurate face tracking. Second, we propose a fast and reliable face quality estimation algorithm for valuable face selection. Our pipeline runs at over 20fps on Hisiv 3559A SoC with a single NNIE device for neural network inference, while achieving over 95% recall and less than 0.4 repetition rate in real world surveillance videos.
Minimum Error Entropy Based EKF for GPS Code Tracking Loop

This paper investigates the minimum error entropy based extended Kalman filter (MEEKF) for multipath parameter estimation of the Global Positioning System (GPS).The extended Kalman filter (EKF) is designed to give a preliminary estimation of the state.The scheme is designed by introducing an additional term, which is tuned according to the higher order moment of the estimation error.The minimum error entropy criterion is introduced for updating the entropy of the innovation at each time step.According to the stochastic information gradient method, an optimal filer gain matrix is obtained.The mean square error criterion is limited to the assumption of linearity and Gaussianity.However, non-Gaussian noise is often encountered in many practical environments and their performances degrade dramatically in non-Gaussian cases.Most of the existing multipath estimation algorithms are usually designed for Gaussian noise.The I (in-phase) and Q (quadrature) accumulator outputs from the GPS correlators are used as the observational measurements of the EKF to estimate the multipath parameters such as amplitude, code delay, phase, and carrier Doppler.One reasonable way to obtain an optimal estimation is based on the minimum error entropy criterion.The MEEKF algorithm provides better estimation accuracy since the error entropy involved can characterize all the randomness of the residual.Performance assessment is presented to evaluate the effectivity of the system designs for GPS code tracking loop with multipath parameter estimation using the minimum error entropy based extended Kalman filter.
A Deep Learning Approach for Reconstruction of Color Images in Different Lighting Conditions Based on Autoencoder Technique

Object detection problem in terms of different lighting conditions has been a challenging issue. Existing algorithms can only detect the objects with their shapes. However, when the color of an object changes in different times of a day for its various lighting conditions, these models fail to detect the shape of those color changing objects. As a result, the reliability of those models decreases. This model proposes an Autoencoder technique which can transfer an image of an object to its exact color. A new dataset is created where the image of an object is taken in two different lighting conditions to represent change in color of the same object. Then an autoencoder technique is applied on this dataset. The main function of an Autoencoder is to reconstruct its input image which is given in output through a neural network. Once the object is reconstructed to its exact color, understanding the object for any model becomes much more efficient in comparison with existing object detection models.
Cluster-aware Semantic Vector Learning using BERT in Natural Language Understanding

Natural language understanding (NLU) is a core technology for implementing natural interfaces. Recently, embedding sentences and correspondence between texts as extracted semantic knowledge, called semantic frame, has shown that semantic vector representation is key for implementing or supporting robust NLU systems. However, existing studies pertain to only the relations between sentences or only the correspondence between sentences and semantic frames, and do not consider the many-to-l relationship of text-to-semantic frames and semantic clusters. Herein, we propose a novel framework that learns semantic cluster-aware vector representations using bidirectional encoder representations from transformers(BERT). A key technique is cohesion modeling for pulling paraphrase texts to semantic centroids. Another technique is separation modeling for pushing different clusters away by employing a triplet margin loss. Additionally, we propose a novel semantic frame-encoding method using bidirectional encoder representations from trans-formers(BERT). Using the proposed framework, we demonstrate that the proposed model can learn meaningful semantic vector representations.
Unsupervised Image and Text Fusion for Travel Information Enhancement

With the explosive growth of the shared information on social media platforms, people are increasingly interested in sharing and making their travel plans by referring to others’ travel experiences. However, different social media sources render the heterogeneity of these valuable data, bringing difficulties for data collection and fusion. Thus, facing massive information online, one of the biggest challenges to enhance travel information is how to integrate and match these multi-source data without clear labels. In this paper, we propose an unsupervised method to fuse and match images and travelogues. We first use the three textual components (title, tag, and description) of the descriptive texts of images as three criteria to embed travelogues and the descriptive texts of images, and further introduce images into our method by joint embedding texts and images. Finally, a multiple kernel clustering approach is adopted for matching travelogues and images. Extensive experiments conducted on the real dataset crawled from two websites (Flickr and TripAdvisor) demonstrate the effectiveness and robustness of our proposed method.
Using Graph Neural Networks to Reconstruct Ancient Documents

In recent years, machine learning and deep learning approaches such as artificial neural networks have gained in popularity for the resolution of automatic puzzle resolution problems. Indeed, these methods are able to extract high-level representations from images, and then can be trained to separate matching image pieces from non-matching ones. These applications have many similarities to the problem of ancient document reconstruction from partially recovered fragments. In this work we present a solution based on a Graph Neural Network, using pairwise patch information to assign labels to edges representing the spatial relationships between pairs. This network classifies the relationship between a source and a target patch as being one of Up, Down, Left, Right or None. By doing so for all edges, our model outputs a new graph representing a reconstruction proposal. Finally, we show that our model is not only able to provide correct classifications at the edge-level, but also to generate partial or full reconstruction graphs from a set of patches.
SARG04 and AK15 Protocols Based on the Run-Time Execution and QBER

Quantum computer has become a concern since the scientists have approved the ability of breaking the classical cryptographic methods in a potential Low-Run- Time Execution. The Run-Time could mean a risk for most of the classical algorithms; if not all. While the classical system depends on the sequential order of bits, quantum system will utilize simultaneous multi-channel submissions. These submissions can be used in several aspects of the quantum system, which include hardware, software, and applications. Also, the power of cryptographic algorithms is based on the processing machine or the Run-Time Execution (RTE). In cryptography, the preference usually is given to simple and fast processing algorithms, where in this paper will compare between two Quantum Key Distribution (QKD) Protocol algorithms. The comparison will discuss and demonstrate the Run-Time Execution between the SARG04 and AK15 Protocols, and both algorithms will be applied to the same number of qubits as well as the measurement mechanism. In addition, both protocols will be experienced to investigate the Quantum Bit Error Rate (QBER), and the possibility of the QBER occurs during qubits transmissions.
Design and Implementation of Primary School Students’ Oral Arithmetic System Based on Android Technology

Abstract This paper introduces the importance of cultivating pupils’oral arithmetic ability. This paper designs and realizes students’ score management system based on Java technology. The system can automatically give oral arithmetic questions. When users answer questions, it can automatically judge whether the answers are correct or not, and put the questions they have done into the combo box for users to review.
Recommendation of cyber attack method based on knowledge graph

The lack of correlation between the new cyber attack intelligence and the knowledge database makes it difficult for the recommendation system to recommend effective cyber attack knowledge in terms of the new security intelligence. This paper proposes an cyber attack method intelligence recommendation algorithm based on knowledge graph. Firstly, it proposes a cyber attack knowledge graph construction scheme based on four kinds of open security databases. Then, based on the idea of collaborative filtering recommendation, the meta-path is introduced to describe different relations between nodes. At last, the recommendation list could be generated by calculating the correlation score of each path with node vector. Compared with the content-based recommendation method, the effectiveness of this method is proved.
Comparison of Sentiment Analysis and Domain Adaptation Techniques with Research Scopes

Sentiment analysis is a classification problem that automatically estimates the stance of written text as positive, neutral, or negative. Nowadays, the social platform is extensively used by users to represent any comment, feedback, or opinion on any items or persons. It would be helpful if all feedbacks and opinions are summarized and the analysis is presented in form of positive, negative, or neutral ones. To prepare this analysis accurately, a sufficient amount of annotated data are required. However, such data are not always available, and manually constructing enough training data is a very costly and time-consuming task. Therefore technique is required to train the model on one domain and with transfer learning logic, it becomes possible to classify data from other domains. This technique is called domain adaptation. In this article, many states of art sentiment analysis and domain adaptation techniques are presented, compared, and investigated briefly with the research scopes in the field of sentiment analysis.
Regularities of element migration based on rock spectral features: a case study of the Liwu copper deposit

This study used portable X-ray fluorescence (pXRF) spectrometry and hyperspectral imaging for analysing regularities of element migration.
Confusable Learning for Large-Class Few-Shot Classification

Few-shot image classification is challenging due to the lack of ample samples in each class. Such a challenge becomes even tougher when the number of classes is very large, i.e., the large-class few-shot scenario. In this novel scenario, existing approaches do not perform well because they ignore confusable classes, namely similar classes that are difficult to distinguish from each other. These classes carry more information. In this paper, we propose a biased learning paradigm called Confusable Learning, which focuses more on confusable classes. Our method can be applied to mainstream meta-learning algorithms. Specifically, our method maintains a dynamically updating confusion matrix, which analyzes confusable classes in the dataset. Such a confusion matrix helps meta learners to emphasize on confusable classes. Comprehensive experiments on Omniglot, Fungi, and ImageNet demonstrate the efficacy of our method over state-of-the-art baselines.