Reviewerly/exHarmony huggingface dataset:
On the c-differential spectrum of power functions over finite fields

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. Inspired by this pioneering work, power functions with low c-differential uniformity were constructed. Wang et al. defined the c-differential spectrum of a power function [27]. In this paper, we present some properties of the c-differential spectrum of a power function. Then we apply these properties to investigate the c-differential spectra of some power functions. A new class of APcN function is also obtained.
Safe adaptation in multiagent competition

Achieving the capability of adapting to ever-changing environments is a critical step towards building fully autonomous robots that operate safely in complicated scenarios. In multiagent competitive scenarios, agents may have to adapt to new opponents with previously unseen behaviors by learning from the interaction experiences between the ego-agent and the opponent. However, this adaptation is susceptible to opponent exploitation. As the ego-agent updates its own behavior to exploit the opponent, its own behavior could become more exploitable as a result of overfitting to this specific opponent's behavior. To overcome this difficulty, we developed a safe adaptation approach in which the ego-agent is trained against a regularized opponent model, which effectively avoids overfitting and consequently improves the robustness of the ego-agent's policy. We evaluated our approach in the Mujoco domain with two competing agents. The experiment results suggest that our approach effectively achieves both adaptation to the specific opponent that the ego-agent is interacting with and maintaining low exploitability to other possible opponent exploitation.
Tamarix hispida

This datasheet on Tamarix hispida covers Identity, Distribution.
Optical Flow Estimation in Dense Foggy Scenes With Domain-Adaptive Networks

Estimating optical flow in dense foggy scenes is a challenging task. The basic assumptions for computing flow such as brightness and gradient constancy become invalid. To address the problem, we introduce a semisupervised deep learning method that can learn from real fog images without requiring the corresponding optical flow ground-truths. Our method is a multitask network, integrating the domain transformation and optical flow networks in one framework. The domain transformation is performed between foggy and clean domains. Under our semisupervised training strategy, we train our method in a supervised manner with a pair of synthetic fog images, their corresponding clean images and optical flow ground-truths. Subsequently, given a pair of real fog images and a pair of clean images that are not corresponding to each other (unpaired), in the next training batch, we train our network in an unsupervised manner. The supervised and unsupervised training processes are alternated iteratively. Since our method relies on unsupervised learning for real data, we show that it can be used for test-time training. We show in our experiments that performing test-time training improves the results further on our test data. Extensive experiments show that our method outperforms the state-of-the-art methods in estimating optical flow in dense foggy scenes.
A novel improved SLIC superpixel segmentation algorithm

Superpixel is a local compact homogeneous region formed by adjacent pixels with similar characteristics. Compared with pixel features, replacing original pixels with superpixels can reduce the complexity of image processing algorithm. However, there are few researches on extracting superpixel features by deep neural networks. Aiming at the problem that the existing superpixel segmentation algorithm is not differentiable, which makes it unable to be integrated into the deep learning network. Based on SLIC, this paper converts the non-differentiable pixel-superpixel correlation calculation into differentiable operation, and proposes a differentiable superpixel segmentation method, which can extract superpixel features by using full convolution network. The new proposed superpixel segmentation algorithm has been tested on the BSDS 500 public dataset. The simulation results show it not only has excellent performance and higher accuracy, but also can used to learn other task-specific superpixels.
Insights Into Incorporating Trustworthiness and Ethics in AI Systems With Explainable AI

Over the past seven decades since the advent of artificial intelligence (AI) technology, researchers have demonstrated and deployed systems incorporating AI in various domains. The absence of model explainability in critical systems such as medical AI and credit risk assessment among others has led to neglect of key ethical and professional principles which can cause considerable harm. With explainability methods, developers can check their models beyond mere performance and identify errors. This leads to increased efficiency in time and reduces development costs. The article summarizes that steering the traditional AI systems toward responsible AI engineering can address concerns raised in the deployment of AI systems and mitigate them by incorporating explainable AI methods. Finally, the article concludes with the societal benefits of the futuristic AI systems and the market shares for revenue generation possible through the deployment of trustworthy and ethical AI systems.
Introducing Transparent Data Encryption

In this chapter we begin to look at Transparent Data Encryption (TDE). TDE was introduced in SQL Server 2005 as a way to encrypt your data. It remains part of SQL Server and pretty much unchanged right up to the latest versions.
End-to-end training image-text matching network

The task of image-text matching involves the information of both image and text, and the foremost challenge is how to find the correspondence between image and text. The existing work is based on the trained visual features to train the matching network or uses the complex Transformer network architecture to extract image features and text features to complete the matching. In this paper, we design a simple image-text matching network that can be trained end-to-end. We have conducted comparative experiments on Flickr30K and MSCOCO datasets, and the results show that our network performance is better than the latest methods on Flickr30K dataset, and we have further discussed our network performance in the VQAv2 dataset.
Feature Selection using Multi-Objective Clustering based Gray Wolf Optimization for Big Data Analytics

Although numerous efforts have been made to develop feature selection framework which is efficient in Big Data technology, complexity of processing big data remains a significant barrier. As a result, the computational complexity and intricacy of big data may block the data mining process. The feature selection method means, a required pre-processing approach to minimize dataset dimensionality for great advanced features and classifier performance optimization. In order to increase performance, feature selection are regarded to constitute the core of big data technologies. In recent years, many academics have moved their focus to data science and analytics for application scenarios leveraging integrating tools of big data. People take quite some time to engage, when it comes to big data. As a consequence, in a decentralized system with a high workload, it is crucial in making feature selection dynamic and adaptable. Multi objective optimal strategies for feature selection are provided in this work. This research adds to the creation of a strategy for enhancing feature selection efficiency in large, complex data sets. In this paper, a multi-objective clustering-based gray-wolf optimization algorithm (MOCGWO) is proposed for classification problems. Five datasets were used to show the robustness of proposed algorithm. The result analysis was compared with other optimization methodology such as GWO and PSO. This shows efficacy of MOCGWO algorithm.
A possibilistic Fuzzy c-means algorithm based on improved Cuckoo search for data clustering

Possibilistic Fuzzy c-means (PFCM) algorithm is a powerful clustering algorithm. It is a combination of two algorithms Fuzzy c-means (FCM) and Possibilistic c-means (PCM). PFCM algorithm deals with the weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in the case of coincidence clusters. However, PFCM still has a common weakness of clustering algorithms that is easy to fall into local optimization. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be stable and high-efficiency. In this study, we propose a hybrid method encompassing PFCM and improved Cuckoo search to form the proposed PFCM-ICS. The proposed method has been evaluated on 4 data sets issued from the UCI Machine Learning Repository and compared with recent clustering algorithms such as FCM, PFCM, PFCM based on particle swarm optimization (PSO), PFCM based on CS. Experimental results show that the proposed method gives better clustering quality and higher accuracy than other algorithms.
Deutsch Algorithm

TheDeutsch algorithm Deutsch algorithmAlgorithmsquantum Deutsch algorithm illustrates, using a very special example, that a quantum computer, in principle, is more efficient than a classical computer.
Quantum Gates and Circuits

A quantum circuit, like the classical case, represents qubits by lines and gates by various symbols. At the hardware level, a quantum circuit, like a classical computer, is a physical object—composed of tiny capacitors with Josephson junctions creating superconducting qubits that are manipulated using resonators, magnetic and electric fields and other interactions. The information transmitted along the circuit is no longer high and low voltages of a classical bit but the quantum mechanical state (wave) function.
Simon’s Algorithm

At the outset of quantum algorithmsAlgorithmsquantum in the early nineties, there were not many practical algorithms that show significant advantage of a quantum computer to a classical computer.
The Current State of Research on The Burlingame Mission -Focusing on a Global Historical Perspective-

Anson Burlingame was the first American minister accredited to Beijing and the first official envoy of the Qing government to Western countries in 1868. The Burlingame mission visited 11 Western countries, and relevant records of their activities have remained to this day. Since its wide scope of activity had a negligible effect on the history of many countries, the Burlingame Mission has been studied in various fields of international academic circles. The article deals with the studies and trends of research on the Burlingame mission from a global historical perspective and organizes them by field. The article divides the research fields of the Burlingame mission into three categories. The first field is the history of politics and diplomacy. Anson Burlingame arrived in Washington, D.C., and as a representative of the Chinese side, signed The Burlingame Treaty with the United States, which greatly affected the politics and diplomacy of both countries. The research trend of the treaty reflects changes in political and diplomatic relations between China and the United States, which shows the varying tendency of Sino-American relations. The second one is cultural history. Zhigang(志剛) and Zhang Deyi(張德彝), members of the Qing’s first diplomatic corps, wrote journals that provide a rich description of Western countries in the nineteenth century, which made the journals attracted as one of the areas of the cultural history research. It is remarkable that the journals have been studied from the maritime history, one of the global historical perspectives. Last but not least, the third one is comparative history. Some scholars have been interested in studying comparing the Burlingame mission with the Iwakura mission of Japan in that both of them were sent to the West around the same time. In particular, analyzing English-language newspapers, the studies, which read the viewpoint of Western countries regarding the missions of both countries at that time and compared both missions from the maritime history as well, opened a new horizon for the research of the East Asian missions. It can be evaluated that these studies revealed the value of the Burlingame Mission in global historical research and offered the possibility of interdisciplinary studies on this subject.
Convolutional Redistribution Network for Multi-view Medical Image Diagnosis

Medical data such as Computed Tomography (CT), X-ray, and Magnetic Resonance Imaging (MRI) are integral elements of medical diagnosis. Deep learning has become common in computer-aided diagnosis, however, most of these models use only single-modal data as input and cannot take full advantage of data from different modalities for diagnosis. In addition, most of the existing multi-view models only fuse the results on a single view, without fully exploring the relationships between multi-view data. To better explore the correlation between data from different modalities, we propose a generic multi-view classification model for computer-aided diagnosis on multi-view medical images. With attention mechanism, the proposed model automatically extracts essential information from multi-view data to generate a series of “good and diverse” pseudo views for integration. The experiment results show that proposed model achieves good performance on pancreatic tumor classification task as well as the OrganMNIST3D classification task of the MedMNIST public datasets.
An Improved Person Re-Identification Method based on AlignedReID ++ algorithm

Person re-identification (ReID) is a popular research topic in computer vision. It focuses on matching a given person from an image dataset captured by many non-overlapping cameras. It remains challenging duo to the influences of pose, illumination, occlusion, and background confusion. In this paper, an improved ReID approach based on the AlignedReID ++ algorithm is proposed. Three effective training tricks are introduced to improve the effectiveness of the AlignedReID ++ algorithm. Training loss, accuracy, and mean average precision (mAP) are used as measure metrics. Extensive experiments are implemented on the ResNet50 and DenseNet121 backbone networks. Our implementation gains the Rank-1 accuracy and mAP of 93.7% and 91.2%, respectively. The source code of the improved AlignReID ++ method is available on request.
BLOCOVID: A blockchain-based COVID-19 digital vaccination certificate verification system

The global impact of the COVID-19 pandemic has been felt in diverse ways. Although the death rate in Africa has not been as devastating as predicted by the World Health Organization (WHO), its economic and social impact has been fully felt by the African continent. As the world goes through the vaccination process to achieve herd immunity, Africa has not only faced problems like the inability to produce and procure vaccines, but some countries in the west are doubting the authenticity of the vaccination process and even vaccine certificates coming from various countries on the continent. The approach of using centralized systems to validate COVID-19 vaccine certificates makes these systems susceptible to Denial of Service (DoS), modification, and Man-in-the-Middle (MiTM) attacks. To curb this problem, we proposed a blockchain-based digital COVID-19 vaccination certificate verification system called BLOCOVID. The proposed system uses the decentralized approach of distributed ledgers to ensure that vaccine certificates are secured, immutable, and verifiable. Our proposed system stores vaccine serial numbers and their corresponding certificates as hash values. These hash values are stored on the blockchain network as transaction values. The authenticity of a vaccine certificate is determined by the availability of the hash values of the certificate and its corresponding vaccine serial number on the blockchain network. The proposed system was simulated using the BlockSim simulator. To begin with, the simulation results show that the proposed system can ensure system availability, thereby minimizing DoS attacks. Secondly, the proposed system can ensure the integrity of vaccine certificates by allowing third parties to verify the authenticity of these certificates. The simulation results show that even with 10240 nodes, the average transaction time was 137.2ms, with a total transaction rate of 9911.034 transactions per second.
Anomaly Detection of Filter Mesh Based on DifferNet and SSIM Algorithm

The power supply equipment of electric traction provides electricity for subway operation and produces a large amount of heat during operation, and the filter mesh on it plays a vital role in heat dissipation. However, in the long-term operation of the train, the filter mesh is easily blocked and entangled by foreign objects, causing heat accumulation and reducing the heat dissipation effect of the equipment. Therefore, it is very significant to realize the anomaly detection of the filter mesh of traction box. In this paper, We propose a computer vision-based anomaly detection method, which firstly adopts DifferNet based on normalizing flow to realize the qualitative analysis of anomaly detection, then combines with SSIM algorithm to achieve the localization of the anomaly area, and finally marks the anomaly area on the original image to accomplish the whole anomaly detection process.
Low-Latency Sliding Window Algorithms for Formal Languages

Low-latency sliding window algorithms for regular and context-free languages are studied, where latency refers to the worst-case time spent for a single window update or query. For every regular language $L$ it is shown that there exists a constant-latency solution that supports adding and removing symbols independently on both ends of the window (the so-called two-way variable-size model). We prove that this result extends to all visibly pushdown languages. For deterministic 1-counter languages we present a $\mathcal{O}(\log n)$ latency sliding window algorithm for the two-way variable-size model where $n$ refers to the window size. We complement these results with a conditional lower bound: there exists a fixed real-time deterministic context-free language $L$ such that, assuming the OMV (online matrix vector multiplication) conjecture, there is no sliding window algorithm for $L$ with latency $n^{1/2-\epsilon}$ for any $\epsilon>0$, even in the most restricted sliding window model (one-way fixed-size model). The above mentioned results all refer to the unit-cost RAM model with logarithmic word size. For regular languages we also present a refined picture using word sizes $\mathcal{O}(1)$, $\mathcal{O}(\log\log n)$, and $\mathcal{O}(\log n)$.
Analysis of Early Warning Tsunami Prediction System using the Arduino Uno R3 and DNN

An early warning tsunami prediction module is a device that predicts the tsunami within a day, the warning system improves the survival rate of the shoreline people and the destruction rate is also reduced. Present system only detect the tsunami and it doesn't forecast any of the warning signals and they are not equipped with the predicting models and they follow the older flow of warning forecasting (i.e.) the data collected from the source is sent to the data centers and they will be deciding the decision which leads to the lesser efficiency in the forecasting. This research study has utilized the DNN architecture to automate every process and boost the performance. It is an AI automated process of detecting the tsunami and broadcasting the distress signal around the area. It records the seismic activity continuously if the threshold is reached according to the position and the winds peed data, the direction of the tsunami is predicted These modules are deployed far away from the shoreline and the maintenance of the module is less. The main goal of this module is to provide warning as earlier as possible so that people can be ready for the impact of tsunami and the survival rate can also increase, the data to be uploaded in the database and additionally the telecast contains the humidity data, temperature data, time series, depth data, global position data, seismic data, turbidity data, this device has been developed with the Atmel microcontroller and equipped with the neural network, which is capable of predicting the Tsunami.
Research on Stereo 3D Display for Augmented Reality Surgical Navigation

Limitations exist in the application of surgical robots in clinical practice such as doctors' limited field of vision, lack of depth information, and the need to constantly switch the field of vision in the process of surgery, which affects the efficiency of surgery. In this paper, research on stereo 3D display for augmented reality (AR) surgical navigation is carried out to address the issues, which combines the technology of AR to overlay the virtual 3D model with the actual image of the operation area, providing doctors and surgical assistants with a more intuitive display method of the lesion location of patients during the operation. The stereo 3D display is presented in detail, including the theoretical research, the algorithm model, the AR effect test, and the positioning error analysis. The simulation and experiments are conducted to evaluate the performance of the stereo 3D display, which verified the practicability and effectiveness of the system and showed that the system can correctly realize pose estimation, virtual model projection, and presentation of AR effect.
Smart Home Surveillance System and Intruder Detection Using Local Binary Pattern Histogram

In today’s society, there has been an increase in the number of suspicious and offensive behaviours. Many public areas, such as shopping malls, banks, and hotels, have CCTV cameras installed to safeguard the safety of their customers and visitors. CCTV cameras are now installed in many homes, particularly in those with single individuals and the elderly. Because human observation of these security cameras 24 hours a day, seven days a week is nearly impossible. It necessitates a workforce and their undivided attention in order to determine whether the collected activities are unusual or suspicious. As a result, this flaw necessitates the necessity to automate this procedure with high precision, as well as the ability to determine which frames and areas of the recording contain suspicious activity, allowing for faster judgment. As a result, we are using deep learning models for Intelligent Threat Recognition System to save time and labour waste. Its purpose is to detect signals of enmity in real time, allowing abnormalities to be distinguished from typical patterns. This method reports the technologies available in Open Computer Vision libraries and the methodologies to implement them using python. To provide the user friendly interface tkinter GUI interface is used. It uses haar cascade to detect the face and Local Binary pattern Algorithm to recognize the face. It also send a alert mail if it finds any uncommon activities.
On the differential spectrum of a differentially 3-uniform power function

In this paper, we investigate the cardinality of the intersection of four particular cyclotomic classes of order two over the finite field with characteristic 5 by making most use of the cyclotomic numbers of orders two and four. As a consequence, a class of power functions over F5n is shown to be differentially 3-uniform and its differential spectrum is also completely determined.