Reviewerly/exHarmony huggingface dataset:
Technical note: Determining the applicability of a clinical knowledge‐based learning model via prospective outlier detection

Knowledge-based planning (KBP) is increasingly implemented clinically because of its demonstrated ability to improve treatment planning efficiency and reduce plan quality variations. However, cases with large dose-volume histogram (DVH) prediction uncertainties may still need manual adjustments by the planner to achieve high plan quality.The purpose of this study is to develop a data-driven method to detect patients with high prediction uncertainties so that intentional effort is directed to these patients.We apply an anomaly detection method known as the local outlier factor (LOF) to a dataset consisting of the training set and each of the prospective patients considered, to evaluate their likelihood of being an anomaly when compared with the training cases. Features used in the LOF analysis include anatomical features and the model-generated DVH principal component scores. To test the efficacy of the proposed model, 142 prostate patients were retrieved from the clinical database and split into a training dataset of 100 patients and a test dataset of 42 patients. The outlier identification performance was quantified by the difference between the DVH prediction root-mean-squared errors (RMSE) of the identified outlier cases and that of the remaining inlier cases.With a predefined LOF threshold of 1.4, the inlier cases achieved average RMSEs of 5.0 and 6.7 for bladder and rectum, while the outlier cases have substantially higher RMSEs of 6.7 and 13.0 in comparison.We propose a method that can determine the prospective patient's outlier status. This method can be integrated into existing automated treatment planning workflows to reduce the risk of generating suboptimal treatment plans while providing an upfront alert to the treatment planner.
The Method of Flight Mission Formation for a Group Autonomous Flight of Unmanned Aerial Vehicles

The object of the research is the process of planning a group flight of unmanned aerial vehicles (UAVs). The aim is to reduce the time needed to form the flight trajectories of the UAV group and increase the quality of the mission by control the keeping of the distance and flight time on a given hardware platform. The following tools were used to organize an autonomous group flight: flight programming libraries for indoor UAVs, flight visualization libraries for outdoor UAVs. The existing tools for modeling and flight control are considered. The results obtained are: classification of UAV group flight based on the existing classification of UAV autonomy levels; a method of preparing a flight mission for a group autonomous synchronized flight and a flight along a predetermined trajectory of each member of the group. Scientific novelty: for the first time, a method for planning a group flight based on existing modeling tools has been proposed, which allows choosing a sufficient set of software for the group’s tasks.
Iterated nth order nonlinear quantum dynamics with mixed initial states

Nonlinear quantum transformations play an important role in quantum state distillation, aiding quantum communication schemes. The simplest of these involve quadratic rational maps for qubits, higher order versions have not been explored in detail. We consider such iterated nonlinear protocols for qubits, involving a generalized CNOT, a measurement, and a single-qubit Hadamard gate processing n inputs in identical quantum states. To characterize the global properties of these protocols, we determine the fixed points and relevant fixed cycles. We show that a repelling mixed fixed point exists for all orders, marking a phase transition related to the disappearance of the fractalness of borders of different basins of attraction.
Accurate policy detection and efficient knowledge reuse against multi-strategic opponents

In Markov games, how to respond quickly and optimally for an agent against opponents that follow changing policies is an open problem. Most state-of-the-art algorithms assume that players only change their policies at the end of an episode, and the agent can obtain the same optimal episodic rewards by accurately detecting the opponent policy. However, the opponent may change its policies within an episode, or switch to an unknown policy. Besides, the agent is more likely to achieve inconsistent optimal returns because of different opponent policies, which brings greater challenges to policy detection. In an effort to overcome these challenges, this paper proposes an algorithm to achieve accurate opponent policy detection and efficient knowledge reuse. Within an episode, an inter-episode belief and an intra-episode belief are jointly used to continuously infer the opponent’s identity taking into account the episodic rewards and opponent models. Then, the agent can reuse the best response policy directly. We also detect whether the opponent adopts an unknown policy based on performance models after each episode. For the detected unknown opponent type, we model the previously learned policies as corresponding options for indirect knowledge reuse. Moreover, an option-based knowledge reuse (OKR) network is introduced to guide new response policy learning by adaptively reusing useful knowledge from the existing learned policies. We demonstrate the advantages of the proposed algorithm over several state-of-the-art algorithms in three competitive scenarios. • An intra-episode belief continuously guides policy selection. • Episodic rewards and opponent models are used to infer the opponent policy. • Our approach can track the opponent who switches its policy within an episode. • Opponent policy switch frequencies do not degrade the agent’s performance. • Previously learned knowledge is used against an unknown opponent type.
GGADN: Guided generative adversarial dehazing network

Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the generative adversarial networks (GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image but also blurs the details of the image. For GAN network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a guided generative adversarial dehazing network (GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, the proposed method is better than the state-of-the-art dehazing methods.
Sharing Decoders: Network Fission for Multi-task Pixel Prediction

We examine the benefits of splitting encoder-decoders for multitask learning and showcase results on three tasks (semantics, surface normals, and depth) while adding very few FLOPS per task. Current hard parameter sharing methods for multi-task pixel-wise labeling use one shared encoder with separate decoders for each task. We generalize this notion and term the splitting of encoder-decoder architectures at different points as fission. Our ablation studies on fission show that sharing most of the decoder layers in multi-task encoder-decoder networks results in improvement while adding far fewer parameters per task. Our proposed method trains faster, uses less memory, results in better accuracy, and uses significantly fewer floating point operations (FLOPS) than conventional multi-task methods, with additional tasks only requiring 0.017% more FLOPS than the single-task network. We show results with a real-time model on a Pixel phone with released source code.
Negotiation Closer Bot: Negotiation Outcome Predictor based on Sentiment Analysis

On e-commerce platforms like OLX, Craigslist, etc. users can sell and buy a variety of products and also chat with the buyer for additional information such as pictures, description and more importantly for negotiating the prices. This process of negotiation is tedious for all users and some simply do not know how to negotiate. To tackle this, a system that assists negotiations for the user would solve the problem. Therefore, we have created a web application platform that allows users to list products to buy and sell products with an added feature of a negotiation support system that assists users in the negotiation process by predicting whether the negotiation is going to end in success or failure. Users can trigger it whenever they deem it necessary by clicking on the ‘Get Recommendation’ button. This system will read the chat using functions of Natural Language Processing Toolkit (NLTK) library followed by classification using classifier. After testing four classifiers namely, Naïve Bayes, Descisioon Tree, Support Vector Machine (SVM) and Random Forest, we selected SVM as it gave the highest accuracy.
A Blockchain Framework for secure Digital Identity transactions in Indian Agri-subsidy system: Issues, challenges and benefits

The Agriculture mechanization plays a critical role in increasing agricultural productivity and consequently rural prosperity. The Government of India (GOI) and in particular the Department of Agriculture and Farmers welfare (DAC&#x0026;FW), ministry of agriculture has taken up initiatives to help farmers by sanctioning subsidy to procure modern farming tools and equipment&#x0027;s using direct cash transfer scheme. The current technological solutions/implementations of this scheme in India, suffers from various issues such as data duplication and fragmentation, insecurity, verification, inefficacy to trace reliable subsidy provenance and subsidy distributions. Hence, amidst existing technologies blockchain blends as a multifaceted technology to provide secure traceability, immutability and trust creation between the parties involved. Blockchain enables more secure management and storage of digital identities using robust smart contracts solutions to fulfill the trust creation between the stakeholders. The stakeholders who are the part of this process are the Farmer, Government of India and Department of Survey and Land Records. In this article, we put forward a blockchain based smart-contracts on digital identity management system model to identify, authenticate and authorize the stakeholders to record and access services from ledger. We also provide an analysis of the current issues faced by these stakeholders and also highlight the key issues, challenges and benefits involved in deployment of an identity systems.
Blockchain Technologies and Factor of ERP Implementation in Making Infrastucture for Society 5.0

Blockchain Technologies and Enterprise Resource Planning (ERP) are familiar to companies in both developed and developing countries because it is focus on positioning human beings at the center of technology and innovation that is also known by society 5.0. ERP systems are essential for all types of businesses and supported by integrated blockchain technology where blockchain technology is an information transmission system that rely on transparency, decentralization, and security of transmitted data. The use of ERP can help to integrate all activities in the business process of a company where all data regarding activities and operations can be collected, managed, and reused for company needs and interests, while blockchain is an information transmission system, transparent and secure technology that records all the transactions between user who can also help to verify and validity the transmitted data. Systematic literature review is a set process of identifying, evaluating, and interpreting all available research relevant to specific research questions.
Developing a Hardware Approach to Simulating Quantum Computing Using an Optimization Algorithm

In the past few decades, there has been an acute problem of creating a quantum computer that uses quantum mechanical effects such as quantum parallelism and quantum entanglement for computations. Using these mechanisms, the quantum computer is able to solve some of the NP-class problems in polynomial time. A hardware approach to modeling quantum computing is considered, and a general mathematical model of the operation of a quantum computer is described, and a technique for mathematical modeling of quantum computing is presented. At the stage of consideration of the method of mathematical modeling, the most resource-intensive parts of the model are indicated. Also, issues related to data parallelization on a hardware accelerator, which are planned to be used to simulate quantum computing, were considered, and algorithms for the operation of this type of accelerator were given. A more compact data format is proposed, which is recommended to be used when implementing the accelerator. Using the proposed format, it is possible to reduce the resources spent on elementary arithmetic operations. The possibility of introducing an optimization algorithm to minimize the time spent in computations, as well as to speed up the execution of quantum algorithms in simulators of a quantum computer at the stage of the effect of quantum gates on the quantum register model, is proposed. The results of the optimization algorithm and its comparison with the classical mathematical approach using a software model are presented.
Research on Small Target Detection Method of Traffic Signs Improved Based on YOLOv3

Aiming at the problem of low accuracy and high missed detection rate of traffic signs in small target detection, this paper proposed an improved multi-branch parallel fusion feature algorithm based on YOLOv3. In order to alleviate the shortcomings of feature reduction for small targets after multiple convolutions. MPB- YOLOv3 collected and retained features in the initial stage of network transmission, pulled out three parallel branches from them, and respectively transmitted them to the last three feature extraction modules of the original YOLOv3 for dimensional splicing processing, thereby enhancing the dimensional information of detecting small and medium targets. In addition, in order to enhance the detection accuracy of small targets, this paper also designed a TD (Tiny-Detection) module, which is responsible for the feature collection and extraction of small targets in the parallel branch structure. In the experimental results, mAP0.5 was 5.35% higher than the traditional YOLOv3 and 5.00% higher than the SPP-improved YOLO algorithm. The small target detection index <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mAP}_{\text{small}}$</tex> was 5.38% higher than the traditional YOLOv3 and higher than the SPP-improved YOLO algorithm Out of 2.03%. After a series of optimizations, the index ofmAP0.5 could reach up to 0.9485, and the index of mAPsmall could reach up to 0.3822.
Prioritized Load Balancer for Minimization of VM and Data Transfer Cost in Cloud Computing

Cloud computing is one of the rapid growing technology in IT industry. Load balancing is a huge challenge in cloud computing as the incoming requests onto cloud console varies from time to time and so a load balancer need to balance these requests by assigning a new VM. The main challenge in load balancing is that whenever a new request arrives at cloud console and if the load is high at cloud console based on the newly arrived request, i.e., length of task and processing capacity of the task. In the existing work, many of the authors discussed about many of the load balancing algorithms, i.e., round robin, equally spread execution load, and throttled algorithms, but none of the authors mentioned about the priority of the tasks. In this work, we are proposing a prioritized load balancer based on incoming priority of tasks and assign these tasks onto corresponding VMs. This simulation is carried out by using cloud analyst simulator and evaluated the metrics response time, data transfer cost and virtual machine cost. These metrics are evaluated against existing algorithms named as RR and throttled algorithms, and the proposed prioritized load balancer is greatly minimizes response time, virtual machine, and data transfer cost.
Self-attention networks for motion posture recognition based on data fusion

Aiming at the problems of error and poor recognition effect in the data collected by sensors in current motion posture recognition, this paper proposes a self-attention network for motion posture recognition based on data fusion to solve these problems. This method uses the attitude angle information output by the gyroscope to correct the attitude angle obtained by the acceleration sensor using kalman filtering, which effectively improves the accuracy of the attitude angle; at the same time, the attitude angle and acceleration sensor data are used to construct an attention convolutional neural long short term memory artificial neural network (CNN-LSTM) of the attention mechanism to recognize the motion state. The experimental results show that the use of data fusion method can correspond to the accuracy of physical signs, and compared with the traditional network, the accuracy of the network frame recognition proposed in this paper is improved
Analysis on Resource Allocation for parallel processing and Scheduling in Cloud Computing

Today's era is technological era with cloud computing at the top level. It offers on demand service to the users. Additionally, it also allows for dynamic flexible resource allocation and works on the rule of pay as-you-use manner. In these many users can demand services at a same time so there is a provision that users are efficiently provided with the resources. Based on topology and dynamic resource allocation different optimal resource allocation are being reviewed in this paper which can be used for linear strategy of scheduling and parallel processing for [2] resource allocation. Discussion about the maximum profit to the cloud service providers by user satisfaction, what should be avoid having optimal resource strategy, and advantages disadvantages caused by resource allocation usage in cloud computing.
Proof-of-principle demonstration of sequential 3 → 1 quantum random access code via cascaded measurements

Quantum random access code (QRAC) serves the communication task to encode a long message into a quantum system and allow the receiver to decode the initial information with a higher success probability than classical random access code (RAC). Here, we present an experimental demonstration of sequential 3 → 1 QRAC in the prepare-transform-measure scenario with one sender and three independent receivers. The experimental results show that, in the 3 → 1 QRAC scenario, three receivers can independently decode the initial information with an average success probability higher than the classical RAC.
Machine Learning Approach to English-Afaan Oromo Text-Text Translation: Using Attention based Neural Machine Translation

In this paper, we present a Neural Machine Translation (NMT) approach for English-Afaan Oromo text translation. NMT is a machine translation technique that applies an artificial neural network to predict the probability of a sequence of words. It is enhanced by Recurrent Neural Networks (RNN), also called the encoder-decoder networks. There have been some noticeable challenges with English-Afaan Oromo machine translation using the conventional rule-based and Statistical Machine Translation (SMT) systems. Text alignment was the main challenge since there are differences in the structure between the two languages. Afaan Oromo is a morphologically rich language, which makes it difficult to develop rules for the syntactic and semantic elements. Our proposal is to develop a machine translation model for English-Afaan Oromo text, using the attention-based NMT technique. This technique overcomes most of the text alignment and rule tagging limitations. The train/test split technique is used to train and test our model. Adam algorithm is adopted for our training optimizations. We used the BLEU score and human rating Likert scale evaluation methods for evaluation. Experiment shows that our model is significantly important in translation with an average BLEU point of 41.62 on the test sets. This result outperforms the previous baseline systems experimented on English-Afaan Oromo.
A Benchmark Database for the Comparison of Face Morphing Detection Methods

Aim to provide a public and fair comparison platform for evaluating the performance of face morphing detection, a benchmark database HNU-FM is built in this paper. It contains 100 genuine subjects. All morphed face images are automatically generated following the guidance of complete morphing and splicing morphing, and different morphing factors are taken into account. The morphed images are all validated by Face++. Different from the existing databases for face morphing detection, it contains balanced samples between the real face images and the morphed face images, and four evaluation protocols are developed for evaluating different application scenarios. With Utrecht_M and HNU-FM, experiments were conducted on some existing face morphing detection methods for performance comparison. Experimental results and analysis show that the proposed HNU-FM database can further effectively evaluate the stability of various face morphing detection methods on the variation of morphing factors, which are impossible to be evaluated in Utrecht_M. It also shows that HNU-FM has great potential to be a public evaluation benchmark database for face morphing detection.
Unravelling the Idiosyncrasy and Commonality in L2 Developmental Processes: a Time-Series Clustering Methodology

Abstract Learners’ self-initiated language learning with mobile technology occurring outside the classroom is often contextualized, heterogeneous, and idiosyncratic. In this study, we propose a time-series clustering methodology for researching informal mobile language learners’ learning and development of another language, with a view to unravelling the essential uniquenesses and commonalities in learners’ developmental processes. Intensive longitudinal writing samples from nine English learners in China were collected and analysed with the methodology proposed, which first depicted individual-level developmental trajectories of writing complexity that were often idiographic or individual-specific, and then distilled salient developmental patterns that transcended the individual heterogeneity. These typically occurring patterns across individuals demonstrated a more predictable and interpretable manifestation of informal language learners’ developmental processes. Methodological and pedagogical implications of adopting the time-series clustering methodology are discussed.