Reviewerly/exHarmony huggingface dataset:
Robust Fuzzy Clustering Algorithm Based on Adaptive Neighbors

Abstract In pattern recognition, fuzzy logic is widely used in unsupervised classification or clustering methods. Fuzzy c-means (FCM) clustering algorithm is a typical dynamic clustering algorithm of the fuzzy c-means algorithm based on the error square sum criterion. It introduces fuzzy membership and optimizes the objective function to obtain each sample point for all classes. The membership degree of the center is used to automatically classify the data sample. However, the FCM algorithm is susceptible to the noise points and outliers, and the unbalanced data structure reduces the generalization performance of the algorithm. In this paper, we propose a fuzzy c-means clustering algorithm with adaptive neighbors weight learning. Through adaptive neighborhood robust weight learning, an adaptive weight vector with robustness and sparsity is obtained. During the optimization, we only activate the k samples with the shortest distance to the cluster center and eliminate extreme noise samples to improve the global robustness and sparsity of the algorithm. Finally, empirical analysis verifies the superiority and effectiveness of the proposed clustering method.
Method of Distinguishing Styles by Fractal and Statistical Indicators of the Text as a Sequence of the Number of Letters in Its Words

The method of the analysis of different texts styles is developed in the paper. Integer numerical sequences are used as models. The elements of the sequence are the number of letters in the words of the text. The algorithm for calculating the exact value of the fractal dimension is developed. It provides the determination of the exact value of the Hurst index. The value of the power dependence constant is calculated. The obtained indicators in the aspect of fractality completely describe the objects of research.
Behavioural Analysis For Prospects In Crowd Emotion Sensing: A Survey

Crowd behavioural analysis is an interesting and emerging domain in research, with incomplete set of activities, tasks and lack of intermediate cub-processes which are mandated for productive analysis. Since the domain is untapped to a major extent, the research carried out in the domain needs proper stages of operations. A proper taxonomy will direct the futuristic domains in the right track of processes and organization of intermediate tasks. This review paper intends to document the list of stages and processes, data collection, pipelining the sub-tasks, pre-emptive identification of supposed problems during the later stages in detection of crowd emotions and behavioural analysis. Deep learning techniques have been widely deployed to investigate the models of crowd analysis, anomaly detection, and look for meaningful insights and patterns from datasets. The Different models are investigated thoroughly for their respective understanding about the emotional aspects considered in the studies. Emotional characteristics when powered with crowd behavioural analysis and real world entities will deliver a promising solution for crime detections, anomaly detection and ensure a safer environment for nations. Video surveillance tools, datasets from crime datasets and various other factors contributed to the previous research works, models are now being designed to incorporate the best features of these models into one and thus achieve one fruitful model for continuous video analytics.
Querying Medical Imaging Datasets Using Spatial Logics (Position Paper)

Nowadays a plethora of health data is available for clinical and research usage. Such existing datasets can be augmented through artificial-intelligence-based methods by automatic, personalised annotations and recommendations. This huge amount of data lends itself to new usage scenarios outside the boundaries where it was created; just to give some examples: to aggregate data sources in order to make research work more relevant; to incorporate a diversity of datasets in training of Machine Learning algorithms; to support expert decisions in telemedicine. In such a context, there is a growing need for a paradigm shift towards means to interrogate medical databases in a semantically meaningful way, fulfilling privacy and legal requirements, and transparently with respect to ethical concerns. In the specific domain of Medical Imaging, in this paper we sketch a research plan devoted to the definition and implementation of query languages that can unambiguously express semantically rich queries on possibly multi-dimensional images, in a human-readable, expert-friendly and concise way. Our approach is based on querying images using Topological Spatial Logics, building upon a novel spatial model checker called VoxLogicA, to execute such queries in a fully automated way.
Describing Semantics of Data Metamodels: A Case Study of Association-Oriented Metamodel

In this paper, we describe the method for expressing the semantics of data metamodels using a concept system. The method abstract from metamodels’ syntax and deems to enable the modeler to compare different data metamodels, and express semantics-aware translations between different metamodels. The method is based on the Semantics Of Business Vocabulary And Rules standard. Moreover, the paper describes the Association-Oriented Metamodel as a case study for the method.
Broad Learning System-based Automatic Speed Tracking Control of Marine Surface Vessel

The speed control of ships occupies an important position in the fields of ship motion control. Considering the speed control problem of hydrodynamic parameter variation and unknown disturbances, this paper proposes an automatic speed tracking control method of ships based on broad learning system (BLS). The derivation results show that the controller can ensure the asymptotic stability of the error of the speed control system. The simulation results show that this method has stronger adaptive and learning ability and better performance than other methods. It can adaptively adjust the parameters of PID controller according to the current disturbances and system output, and can effectively suppress the disturbances caused by the change of hydrodynamic parameters and the external unknown disturbances.
Quantum Algorithms for Variants of Average-Case Lattice Problems via Filtering

We show polynomial-time quantum algorithms for the following problems: The SIS, LWE, and EDCP problems in their standard forms are as hard as solving lattice problems in the worst case. However, the variants that we can solve are not in the parameter regimes known to be as hard as solving worst-case lattice problems. Still, no classical or quantum polynomial-time algorithms were known for the variants of SIS and LWE we consider. For EDCP, our quantum algorithm slightly extends the result of Ivanyos et al. (2018). Our algorithms for variants of SIS and EDCP use the existing quantum reductions from those problems to LWE, or more precisely, to the problem of solving LWE given LWE-like quantum states. Our main contribution is solving LWE given LWE-like quantum states with interesting parameters using a filtering technique.
Sensory Effect Extraction for 360° Media Content

The presentation of sensory effects in sync with 360° content has the potential to increase user immersion. However, the authoring process for such effects is laborious and slow. It requires the author to specify in time and space the presentation characteristics of effects. This paper contributes to address this need with a generic architecture for inferring the presentation characteristics of sensory effects in 360° content. The proposed architecture decouples and eases the adaptation of multimedia content analysis techniques for recognizing sensory effects. We highlight the original contribution of this work of presenting the automatic recognition of sensory effects location in 360° content.
SAU: Smooth Activation Function Using Convolution with Approximate Identities

Well-known activation functions like ReLU or Leaky ReLU are non-differentiable at the origin. Over the years, many smooth approximations of ReLU have been proposed using various smoothing techniques. We propose new smooth approximations of a non-differentiable activation function by convolving it with approximate identities. In particular, we present smooth approximations of Leaky ReLU and show that they outperform several well-known activation functions in various datasets and models. We call this function Smooth Activation Unit (SAU). Replacing ReLU by SAU, we get 5.63%, 2.95%, and 2.50% improvement with ShuffleNet V2 (2.0x), PreActResNet 50 and ResNet 50 models respectively on the CIFAR100 dataset and 2.31% improvement with ShuffleNet V2 (1.0x) model on ImageNet-1k dataset.
Research on Information Sharing System of Digital Library in Cloud Computing Environment

Using the cloud computing method and applying the cloud computing system, great changes have been realized in China’s IT industry. Cloud computing not only promotes technological innovation, but also realizes the integration of problems from content construction to service. The new method of cloud computing strengthens the application and exploration of cloud computing in different fields. Based on the application, service mode and main characteristics of cloud computing, this paper analyzes the new opportunities brought by cloud computing to library information resource management, constructs digital library information resource sharing system based on cloud computing technology, and establishes and realizes the operation process and mechanism of digital library information resource sharing system under cloud computing environment.
Dynamic time distribution system monitoring on traffic light using image processing and convolutional neural network method

Abstract The number of vehicles that continues to increase every time can cause congestion and increase the potential for accidents. One type of violation that often results in accidents is motorists who break through traffic lights when the red light is on. One factor that encourage motorists to not obey traffic light signs is due to the distribution of green time which tends to be fast on relatively dense tracks. This final project aims to get dynamic green light on traffic light according to the volume of the vehicle for each lane using convolutional neural network method and can detect traffic light violations in the form of breaking the stop line markings on zebra cross. The sensor used is camera. There are three cameras used in each line. The number of cars detected will be sent to microcontroller via laptop. The data on the number of cars is used as input for the convolutional neural network to produce output data that serves as reference for obtaining the length of time for the green light. If the car stops above or exceeds the stop line on zebra cross when the red light is on, the car will be detected as violation.
Pose Estimation and 3D Model Overlay in Real Time for Applications in Augmented Reality

Pose estimation in real time is a challenging research area with numerous applications. Obtaining detected key points in each pose presents us with useful applications, one such application being in the augmented reality domain. This paper presents an approach to 3D model overlay on an image with pose key point information. The model used in this work is based on the paper titled Multi-Person Pose Estimation. An FCN is employed for extracting key point information from the data. The method used in this work involves intersecting the image plane and the desired 3D model in real time and orienting the model correctly in accordance with the key point information. The performance will greatly depend on the GPU specifications.
Continuous-Time Deep Glioma Growth Models

The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy. Recent work has approached the glioma growth modeling problem via deep learning and variational inference, thus learning growth dynamics entirely from a real patient data distribution. So far, this approach was constrained to predefined image acquisition intervals and sequences of fixed length, which limits its applicability in more realistic scenarios. We overcome these limitations by extending Neural Processes, a class of conditional generative models for stochastic time series, with a hierarchical multi-scale representation encoding including a spatio-temporal attention mechanism. The result is a learned growth model that can be conditioned on an arbitrary number of observations, and that can produce a distribution of temporally consistent growth trajectories on a continuous time axis. On a dataset of 379 patients, the approach successfully captures both global and finer-grained variations in the images, exhibiting superior performance compared to other learned growth models.
TagRec: Automated Tagging of Questions with Hierarchical Learning Taxonomy

Online educational platforms organize academic questions based on a hierarchical learning taxonomy (subject-chapter-topic). Automatically tagging new questions with existing taxonomy will help organize these questions into different classes of hierarchical taxonomy so that they can be searched based on the facets like chapter, topic. This task can be formulated as a flat multi-class classification problem. Usually, flat classification based methods ignore the semantic relatedness between the terms in the hierarchical taxonomy and the questions. Some traditional methods also suffer from the class imbalance issues as they consider only the leaf nodes ignoring the hierarchy. Hence, we formulate the problem as a similarity-based retrieval task where we optimize the semantic relatedness between the taxonomy and the questions. We demonstrate that our method helps to handle the unseen labels and hence can be used for taxonomy tagging in the wild, like the question-answer forums. In this method, we augment the question with its corresponding answer to capture more semantic information and then align the question-answer pair’s contextualized embedding with the corresponding label (taxonomy) vector representations. The representations are aligned by fine-tuning a transformer based model with a loss function that is a combination of the cosine similarity and hinge rank loss. The loss function maximizes the similarity between the question-answer pair and the correct label representations and minimizes the similarity to unrelated labels. Finally, we perform extensive experiments on two real-world datasets. We empirically show that the proposed learning method outperforms representations learned using the multi-class classification method and other state of the art methods by 6% as measured by Recall@k. We also demonstrate the performance of the proposed method on unseen but related learning content like the learning objectives without re-training the network.
Family metro maps

We present family metro maps as a new approach to visualize the relations between multiple related families. A family is represented by a metro line, where the parents are the end nodes and the children the intermediate nodes. We introduce family trees as rooted binary trees and consider the problem of automatically drawing a family metro map for a given family tree without crossing line segments, where the successive nodes of a metro line are placed at equal distances. The inter-node distance may be chosen to depend on the generation of the family, such that metro lines of earlier generations are shown more prominently than metro lines of later generations. Family metro maps can be used to visualize the family relations of the descendants of multiple couples that have common descendants.
Research and Optimization of Conflict Search Algorithm for Multi-Agent Path Planning Based on Incremental Heuristic

Abstract Conflict Based Search(CBS) is used for multi-agent Pathfinding (MAPF) to enable each Agent to reach the target node. The CBS algorithm uses the heuristic algorithm A* search to calculate the MAPF solution, and the path planning uses forward search, which cannot explore the path of the unknown region. On this basis, this paper proposes that, in the case of unknown map and changing environment at any time, when encountering new obstacles, the information obtained from previous search should be used without completely replanning the path. Because of the idea of incremental programming, the number of reprogramming times and the number of affected nodes can be reduced. The optimized algorithm consumes less time and memory, and improves the efficiency of path planning.
Development and Analysis of Multilingual Phone Recognition System

This chapter describes the development of Multi-PRS for four Indian languages. Detailed description on multilingual speech corpora and experimental setup is provided. The development and evaluation of Multi-PRS is presented. The results of Multi-PRSs and Mono-PRSs are compared and analysed with respect to Dravidian and Indo-Aryan language families. The performance improvement of Multi-PRS using tandem phone posterior features is also discussed.
Predicting Symptoms from Multiphasic MRI via Multi-instance Attention Learning for Hepatocellular Carcinoma Grading

Liver cancer is the third leading cause of cancer death in the world, where the hepatocellular carcinoma (HCC) is the most common case in primary liver cancer. In general diagnosis, accurate prediction of HCC grades is of great help to the subsequent treatment to improve the survival rate. Rather than to straightly predict HCC grades from images, it will be more interpretable in clinic to first predict the symptoms and then obtain the HCC grades from the Liver Imaging Reporting and Data System (LI-RADS). Accordingly, we propose a two-stage method for automatically predicting HCC grades according to multiphasic magnetic resonance imaging (MRI). The first stage uses multi-instance learning (MIL) to classify the LI-RADS symptoms while the second stage resorts LI-RADS to grade from the predicted symptoms. Since our method provides more diagnostic basis besides the grading results, it is more interpretable and closer to the clinical process. Experimental results on a dataset with 439 patients indicate that our two-stage method is more accurate than the straight HCC grading approach.
Digital chronological data reuse in archaeology: Three case studies with varying purposes and perspectives

• Digital archaeological data are often not archived in a reusable format. • There is insufficient data archived describing the process of building chronological models. • There needs to be explicit standards for ensuring digital archaeological data are reusable. • Full ADS archive search and site specific data are investigated. A wealth of digital data are produced during an archaeological excavation and because so much of the fieldwork is unrepeatable, once the site is fully excavated, the digital records must be archived in a manner that best facilitates reuse. This paper presents three case studies of users wishing to reuse digital archaeological data from online repositories, with a specific focus on absolute and relative dating evidence. We discuss the problems encountered and how they reflect the wider issues of the reuse of digital archaeological data. Additionally, we provide recommendations specific to chronological data that seek to address the problems.
A Comparison of Similarity Measures for Neighbourhood Based Collaborative Filtering Recommender Systems

As data continues to grow rapidly at an exponential rate, the importance of data processing systems to extract information is also increasing continuously. Recommender Systems are information processing software that seek to aid decision making by providing relevant suggestions from a given pool of items. In this domain, earlier studies have presented a comparison of similarity measures mostly using Root Mean Squared Error and Mean Absolute Error and to the best of our knowledge, they lacked analysis based on the computation time. Therefore, in this paper, we present a comparison of three similarity measures, namely Pearson Correlation Similarity, Cosine Vector Similarity, and Mean Square Difference on two different datasets known as MovieLens 100k and MovieLens 1M and compared the results using three accuracy metrics namely, Root Mean Square Error, Mean Absolute Error, and Fraction of Concordant Pairs. Our results show that in terms of accuracy, Pearson Correlation Similarity is the best performing similarity measure. Moreover, we also consider the time of simulations for each measure to add another dimension to performance evaluation. As a result, Mean Squared Difference was found to be the fastest among all the three similarity measures
Characterising a handheld quantum key distribution system with emulated beam steering

We present a miniaturised free-space quantum key distribution (QKD) system which allows key exchange between a handheld transmitter and a fixed terminal. The QKD system requires to be optically aligned emphasising the need of a beamsteering unit for later applications. To maintain within the size, weight and power restrictions, the active beamsteering hardware is exclusively located inside the receiver. Our target is consumer use so we present rigorous characterisation against a range of background light levels to show anticipated performance outside of a laboratory environment. Experimental results show a reduction in the raw count rate commensurate with the transmission of the added components (74.5%) and a small degradation of the error rate (0.5 percentage points) due to the worse signal-to-noise ratio. These combine to a 50% reduction in estimated secret key rate of the system with the additional components for beam steering.
High-Capacity Reversible Data Hiding using Deep Learning

In today's time, it has become imperative to lay more focus on the security problems such as privacy, copyright protection, and tamper detection which become relevant with the transfer of digital data. A strategy that has recently gained popularity is reversible data hiding (RDH), which helps the recipient on the other end to retrieve confidential information that the sender had concealed in digital data such as photographs or videos. This is done with the help of image encryption and Deep Neural Network (DNN). Through this study we have tried to dive deeper into the domain of reversible data hiding to try and gauge its effect when used with deep learning techniques on the decryption end. Images containing secret data were encrypted and then a deep learning-based model was utilized at the receiver end to classify which of the images had been decrypted accurately along with extraction of the hidden data bits. We have achieved higher embedding capacity with DNN when compared to the existing Support Vector Machine (SVM) scheme.
A (k, n) secret document sharing with meaningful shares

A (k, n) Secret document sharing (SDS) was first proposed by Tu and Hsu. A secret document is shared into n noise-like shares with a public meaningful cover document. In Tu and Hsu's (k, n)-SDS, any k or more shares with the public document are collaboratively used for recovering the secret document. Recently, Liu et al. enhanced Tu and Hsu's (k, n)-SDS with by reducing the share size and the time complexity of generation and recovery. Consider an application scenario that a participant involved in various (k, n)-SDSs. A participant may hold several noise-like shares. These noise-like shares are visually identical and indistinguishable, such that they are and not easily managed. Both previous (k, n)-SDSs have noise-like shares. In this paper, a (k, n)-SDS with meaningful shares is investigated. Differing from existing (k, n)-SDS methods, our SDS provides easy management for shares, and meantime can deliver additional information to respective participants based on these meaningful shares. Moreover, experiments and comparisons are demonstrated to show the merits of the proposed scheme.