Reviewerly/exHarmony huggingface dataset:
Optimal Transport Based Filtering with Nonlinear State Equality Constraints

In this work we propose a framework to address the issue of state dependent nonlinear equality-constrained state estimation using Bayesian filtering. This framework is constructed specifically for a linear approximation of Bayesian filtering that uses the theory of Optimal Transport. As a part of this framework, we present three traditionally-used nonlinear equality constraint-preserving algorithms coupled with the Optimal Transport based filter: the equality-constrained Optimal Transport filter, the projected Optimal Transport filter, and the measurement-augmented Optimal Transport filter. In cases where the nonlinear equality-constraints represent an arbitrary convex manifold, we show that the re-sampling step of Optimal Transport filter, can generate initial samples for filtering, from any probability distribution function defined on this manifold. We show numerical results using our proposed framework.
Design of Intelligent University Financial Management System Based on B/S Structure and Complex Data Mining

Design of modern intelligent university financial management system based on B/S structure and complex data mining is implemented in this paper. This article analyzes the data set with large sample size and high feature dimension. On the premise of ensuring the effectiveness of the algorithm as much as possible, the problem of computational efficiency caused by large-scale data is solved. The so-called general software system development cost minimization refers to finding an optimal reliability allocation plan based on the meeting system reliability target requirements, hence, we integrate the optimization model to construct the efficient methodologies. The numerical simulation compared with the latest models prove that the proposed framework is efficient. The B/S structure is applied to enhance the robustness of the whole system.
Computational Acceleration: Offense in Depth

As budgets tighten, customer demands increase, and the historic computational growth trends flatten, doing more with what you have will become the hallmark of an elite enterprise IT organization. Operating system, environment, application, and operations acceleration are emerging as the key enablers to span this compute gap.
Work-in-Progress-360-Degree Immersive Storytelling Video to Create Empathetic Response

Open days are organised by Universities to give potential students the opportunity to visit the University premises, talk to staff and student ambassadors and develop a sense of how it feels to study at a University a concept difficult to convey via prospectus. Visiting open days requires the investment of personal time, travel, and expense especially if from abroad. In recent years there has been an increasing demand for open days to be delivered online. Social distancing measures imposed by the COVID-19 pandemic enforced this mode of delivery of open days as the only viable option. Many Universities created VR campuses to help students experience their campuses but lack an empathetic response in the way they fail to capture the actual vibe of a university from the human perspective. New tools such as 360-degree immersive storytelling video (VR) and 3D interactive media present new opportunities for effectively delivering open days capturing not only a realistic representation of the place, but the actual feel of a place. This paper presents work-in-progress focusing on studying if 360-degree immersive storytelling video can invoke an empathetic response. It achieves this by submerging a user in a 360-degree immersive storytelling video that effectively and realistically captures student life. This paper presents the project motivation, discusses the proposed research methodology, presents the research instruments and finishes with expected contributions to knowledge and future work.
Deep Hash-based Relevance-aware Data Quality Assessment for Image Dark Data

Data mining can hardly solve but always faces a problem that there is little meaningful information within the dataset serving a given requirement. Faced with multiple unknown datasets, to allocate data mining resources to acquire more desired data, it is necessary to establish a data quality assessment framework based on the relevance between the dataset and requirements. This framework can help the user to judge the potential benefits in advance, so as to optimize the resource allocation to those candidates. However, the unstructured data (e.g., image data) often presents dark data states, which makes it tricky for the user to understand the relevance based on content of the dataset in real time. Even if all data have label descriptions, how to measure the relevance between data efficiently under semantic propagation remains an urgent problem. Based on this, we propose a Deep Hash-based Relevance-aware Data Quality Assessment framework, which contains off-line learning and relevance mining parts as well as an on-line assessing part. In the off-line part, we first design a Graph Convolution Network (GCN)-AutoEncoder hash (GAH) algorithm to recognize the data (i.e., lighten the dark data), then construct a graph with restricted Hamming distance, and finally design a Cluster PageRank (CPR) algorithm to calculate the importance score for each node (image) so as to obtain the relevance representation based on semantic propagation. In the on-line part, we first retrieve the importance score by hash codes and then quickly get the assessment conclusion in the importance list. On the one hand, the introduction of GCN and co-occurrence probability in the GAH promotes the perception ability for dark data. On the other hand, the design of CPR utilizes hash collision to reduce the scale of graph and iteration matrix, which greatly decreases the consumption of space and computing resources. We conduct extensive experiments on both single-label and multi-label datasets to assess the relevance between data and requirements as well as test the resources allocation. Experimental results show our framework can gain the most desired data with the same mining resources. Besides, the test results on Tencent1M dataset demonstrate the framework can complete the assessment with a stability for given different requirements.
Classification of White Blood Cell Abnormalities for Early Detection of Myeloproliferative Neoplasms Syndrome Using Backpropagation

One of the diseases of white blood cells is myeloproliferative neoplasms syndrome where this disease is an abnormality in the bone marrow in excessive blood cell counts. Full Blood Count (FBC) is a type of examination that shows the patient’s health status, blood cell abnormalities, and the presence of infection in the patient’s body. However, the determination of cell abnormalities is still done manually based on the knowledge and experience of clinical pathology so that the determination of these abnormalities is subjective. Therefore we need a system that is able to classify white blood cell abnormalities automatically, objectively and accurately. This study uses digital image processing on peripheral blood smear images then feature extraction will be obtained which will be the input of the classification system. The features used are area, perimeter, metric and compactness, while the classification method used is the backpropagation method. The best backpropagation network architecture used is 4, 6, 8, 5, 2 with a variation of learning rates of 0.05 and 0.3 producing the best accuracy rate of 91.82% with the amount of training data that is 516 and testing data is 159.
E-Medic – Autonomous Drone for Healthcare System

This paper presents the implementation of a platform for the delivery of medicine using an autonomous drone. The platform consists of a Healthcare platform that connects doctors and patients and an autonomous drone that handles the delivery of medicine to the patients. This platform mainly contains several functionalities for the E-prescribing and delivery management of a drone. For patient management, the E-medic system uses a mobile application with facial recognition-based Authentication. Also, this platform is developed with separate web-applications to handle prescriptions, orders, and delivery management. Since this system uses an autonomous drone for deliveries, this platform also has functionalities to operate the delivery drone using a web application. the system uses REST APIs to operate the drone regardless of the distance to the ground station. The delivery drone can discover the shortest path to the destination and fly to that destination autonomously with the help of the computer vision-based obstacle avoidance system.
Image Data Preservation with Fractional Sine Transform and Dual Chaotic Sequence

The security issue of visual data is prevalent in today's era because of the fast growth of communication technology. The cutting edge technologies like machine learning, deep learning, and cloud computing has given new heights to data communication. And in this virtual age the preservation of digital images are of prime importance because it carries much personal and sensitive information from an individual's personal data to the any nation's defence data. So, the growth in communication technology not only given heights to mankind, but also attracts the attackers for their mischievous activities of data theft. Therefore, to safeguard this, various image security methods are already proposed. So, in the chain, a hybrid method combining discrete fractional sine transform with logistic and Arnold cat map is proposed here. This hybrid combination is noteworthy in the sense that it has higher PSNR and least error in the reconstruction as compared to their ancestor algorithm.
Faculty Facial Recognition Using Convolutional Neural Network a Tool for Smart Academic Monitoring

Faculty Academic Monitoring is one thing a university has to perform to ensure quality delivery of instructions. That includes proper attendance monitoring of classes, faculty consultations, and others. To address attendance monitoring in CCIS - College of Computing and Information Sciences, a facial detection and recognition was developed to identify a faculty face's important feature from a real-time captured image. The study uses a pre-trained model called facenet but acquires new training data sets to generate the desired classifier for a localized recognition system. The data are cleaned by eliminating duplicates, resizing, cropping into the desired dimension, and labeling each image data. The system used image pre-processing techniques like face alignment algorithm, landmarks, face detection, and local binary pattern. The study achieved a validation rate of 96.45% using the test validation function and an accuracy rate of 90% in the actual testing.
Blockchain and Internet of Things: Applications and practices

In the course of recent years, a broad surge of innovations are observed in business-characterization domain. One such domain is blockchain, at its center there is a conveyed record innovation that gives guarantees and exchange straight forwardness and realness. There is a case to be made for actualizing blockchain crosswise over ventures like stock exchanging, supply chains to track any part from assembling, retail, open area, car producing enterprises and land libraries et cetera. Yet, just like the case with all spearheading advancements, blockchain, as well, is loaded with complexities that associations need to assess before submitting. Millions of Internet of Things devices are associated with ordinarily to trade information or data over the web. Notwithstanding, Internet of Things protection and security are significant difficulties because of expanding the quantity of IoT gadgets that the data are gathered from IoT gadgets. Blockchain has stood out to address protection and security worry in Internet of Things gadgets because of its trademark highlight which incorporates permanence, review capacity, and decentralization. Regular blockchain is costly in computational and has restricted versatility and devours more memory and transfer speed which causes the postponement for IoT biological systems. A confident approach is used by the group heads to endlessly reduce the handling overhead for confirm new squares. Blockchain is progressively being utilized to give a circulated, secure, trusted, and private structure for the energy exchange. Be that as it may, existing arrangements experience the ill effects of the absence of protection. The Lightweight adaptable accomplishes decentralization by shaping an overlie network where high asset gadgets together deal with the blockchain.
MIRA:Leveraging Multi-Intention Co-click Information in Web-scale Document Retrieval using Deep Neural Networks

We study the problem of deep recall model in industrial web search, which is, given a user query, retrieve hundreds of most relevant documents from billions of candidates. The common framework is to encoding queries and documents separately into distributed representations and match them in latent semantic space. However, all the exiting deep encoding models only leverage the information of the document itself, which is often not sufficient in practice when matching with query terms, especially for the hard tail queries. In this work we aim to leverage the additional information for documents from their co-click neighbours to help document retrieval. The challenges include how to effectively extract information and eliminate noise when involving co-click information while meet the demands of industrial scalability for real time online serving.
Parameters adaptive identification of Bouc-Wen hysteresis

The adaptive identification method is designed for estimation of system parameters with the Bouc-Wen hysteresis. It is based on the use of adaptive observers and eliminates the problems inherent the identification procedures of such systems. Adaptive algorithms for identifying processes in an adaptive system are developed. The limitation of processes in the adaptive system is proved. The exponential dissipativity of the adaptive system is proved.
On reduction and normalization in the computational core

Abstract We study the reduction in a $\lambda$ -calculus derived from Moggi’s computational one, which we call the computational core. The reduction relation consists of rules obtained by orienting three monadic laws. Such laws, in particular associativity and identity, introduce intricacies in the operational analysis. We investigate the central notions of returning a value versus having a normal form and address the question of normalizing strategies. Our analysis relies on factorization results.
Real-Time Detection of Inter-Frame Video Forgeries in Surveillance Videos

Detection of video forgery is a critical requirement to ensure integrity of video data and with an increase in the use of cameras and dependability of surveillance systems, it becomes more critical. In this paper, video forgery detection is proposed based on the concept of exponential weighted moving average of optical flow variation factors. This mechanism relies on the fact that optical flow variation factor is almost continuous in an original video. However, discontinuity points in the optical flow variation factor are introduced in forged video which are not visible with the naked eye. The proposed mechanism detects inter-frame forgery process, i.e., frame deletion, insertion, and duplication in surveillance videos. The proposed detection mechanism is deployed on cloud server where a live video stream is received from different ATM vestibules. Upon detection, an alert message is sent to the concerned bank for immediate action. Experiments are performed on the videos captured from ATM vestibules of a bank. The accuracy and latency of proposed mechanism manifest pragmatism of the proposed idea.
Attention Based LSTM CNN Framework for Sentiment Extraction from Bengali Texts

Sentiment analysis is a field of immense possibilities and application despite being an age old topic. Variants of neural networks with attention mechanism is a well known tool in this field. As many people now use Bengali to express their opinion in online platforms, it becomes inevitable to analyze emotions in social networks and product reviews in online commerce sites. But unfortunately very few of these techniques have been applied for Bengali sentences. In this paper an attention based LSTM-CNN model is being proposed to be used to solve the problem which outperforms all the latest neural network based approaches in terms of accuracy and model complexity.
Aspect Based Sentiment Analysis of catering field reviews via RoBERTa-AOA model

Abstract The emotional information included in catering field reviews has a great influence on the ordering behaviour of users. The traditional Aspect Sentiment Analysis Model based on Neural Network often has the problem of neglecting Aspect Words. This paper proposes an aspect sentiment analysis model that combines RoBERTa and Attention-over-Attention (AOA). Firstly, the model uses RoBERTa to model sentences and aspects respectively. Then, the deep semantic features of sentence and aspect were extracted by Bi-LSTM. Finally, AOA is adopted to learn the weight of aspect to sentence and sentence to aspect respectively, and the results are input into SoftMax layer for classified output. Experimental results on real Meituan catering data sets show that the accuracy of RoBERTa-AOA is about 1.7%-3.7% higher than that of other models.
Explicit Metric-Based Multiconcept Multi-Instance Learning With Triplet and Superbag

Multi-instance learning (MIL) has garnered considerable attention in recent years due to its favorable performance in various scenarios. Nonetheless, most previous studies have implicitly expressed the correlation between instances and bags. Moreover, the importance of negative instances has been largely overlooked. Hence, we seek to present an explicit and intuitively understandable method that can compensate for these deficiencies. In this article, we creatively introduce a metric-based multiconcept MIL approach based on two aspects. First, the triplet-based bag embedding method identifies instance categories and builds attention weights for every instance explicitly. Accordingly, bag embedding is accomplished under the limitation of weak supervision. Second, the developed instance correlation metric approach in the superbag considers the multiconcept issue to boost the model generalization performance. We have designed a rich variety of experiments to demonstrate the performance of our algorithm. The artificial data experiment reveals the interpretability of the proposed network. The results of the comparison experiment confirm that our method shows favorable performance in multiple tasks. Finally, we illustrate the motivation of the presented method by the ablation experiments.
Knowledge Graph Completion-based Question Selection for Acquiring Domain Knowledge through Dialogues

Building a perfect knowledge base in a certain domain is practically impossible, so it is effective for dialogue systems to acquire knowledge for enhancing an imperfect knowledge base through natural language dialogues with users. This paper proposes a framework for selecting questions for such knowledge acquisition when a knowledge graph is used as the knowledge base. The framework uses knowledge graph completion (KGC) for predicting new links that are likely to be correct and selects questions on the basis of the KGC scores. One of the problems with this framework is that questions with incorrect content might be selected, which often occurs when the link prediction performance is low, and this would reduce the users’ willingness to engage in dialogues. To alleviate this problem, this paper presents two modifications to the KGC training: 1) creating pseudo entities having substrings of the names of the entities in the graph so that the entities whose names share substrings are connected and 2) limiting the range of negative sampling. Cross validation-based experiments we conducted showed that these modifications improved KGC performance. We also conducted a user study with crowdsourcing to investigate the subjective perception of the correctness of the predicted links. The results suggest that the model trained with the modifications is capable of avoiding questions with incorrect content.
Unrestricted Face Recognition Algorithm Based on Transfer Learning on Self-Pickup Cabinet

In the contactless delivery scenario, the self-pickup cabinet is an important terminal delivery device, and face recognition is one of the efficient ways to achieve contactless access express delivery. In order to effectively recognize face images under unrestricted environments, an unrestricted face recognition algorithm based on transfer learning is proposed in this study. First, the region extraction network of the faster RCNN algorithm is improved to improve the recognition speed of the algorithm. Then, the first transfer learning is applied between the large ImageNet dataset and the face image dataset under restricted conditions. The second transfer learning is applied between face image under restricted conditions and unrestricted face image datasets. Finally, the unrestricted face image is processed by the image enhancement algorithm to increase its similarity with the restricted face image, so that the second transfer learning can be carried out effectively. Experimental results show that the proposed algorithm has better recognition rate and recognition speed on the CASIA-WebFace dataset, FLW dataset, and MegaFace dataset.
Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis

While black-box-based machine learning algorithms have high analytical consistency in manufacturing big data analysis, those algorithms experience difficulties in interpreting the results based on the manufacturing process principle. To overcome this limitation, we present a Self-Adaptive Genetic Programming (SAGP) for manufacturing big data analysis. In Genetic Programming (GP), the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. These advantages enable intuitive interpretation on manufacturing mechanisms and derive manufacturing principles based on the variables represented by formulas. However, GP occasionally has trouble adjusting the balance between high accuracy and detailed interpretation due to an incommensurable symmetry of the solutions. In order to effectively handle this drawback, we apply the self-adaptive mechanism into GP for managing crossover and mutation probabilities regarding the complexity of tree structure solutions in each generation. Our proposed algorithm showed equal or superior performance compared to other machine learning algorithms. We believe our proposed method can be applied in diverse manufacturing big data analytics in the future.
Virtual Logical Qubits: A Compact Architecture for Fault-Tolerant Quantum Computing

Fault-tolerant quantum computing is required to execute many of the most promising quantum applications. In recent years, numerous error correcting codes, such as the surface code, have emerged which are well suited for current and future limited connectivity 2-D devices. We find quantum memory, particularly resonant cavities with transmon qubits arranged in a 2.5-D architecture, can efficiently implement surface codes with around 20× fewer transmons via this work. We virtualize 2-D memory addresses by storing the code in layers of qubit memories connected to each transmon. Distributing logical qubits across many memories has minimal impact on fault tolerance and results in substantially more efficient logical operations. Virtualized logical qubit (VLQ) systems can achieve fault tolerance comparable to conventional 2-D transmon-only architectures while putting within reach a proof-of-concept experimental demonstration of around ten logical qubits, requiring only 11 transmons and 9 attached cavities.