Reviewerly/exHarmony huggingface dataset:
NetCruiser: Localize Network Failures by Learning from Latency Data

In modern data center networks (DCNs), failures of network devices always occur and it is difficult to localize these failures. Our key observation is that latency data can reflect and profile network status. We can use this information to resolve issues like network failure localization. In this paper, we present NetCruiser, a system that is able to localize failures by learning from latency data. It can both measure and collect latency data to monitor the status of the whole network and pinpoint which switch or router encounters a failure. And we design a data structure to handle these latency data. With the construction of this data structure, we build a machine learning model to infer where issue occurs. Therefore, by the usage of this system, it answers the question about which switch encounters a failure in network. Our experimental evaluation has validated both the efficiency and effectiveness of our approach. Our system can be widely applied to both inter-DC network and intra-DC network.
Image Noise Declining Approaches by Adopting Effective Filters

Digital Image Processing is one of the techniques which has a constant requirement in the world due to its immense applications in MRI scans, retaining old literature and enhancing the quality of images acquired from underwater or from the space. People have been working on image processing for years and have successfully classified the information which would lead us to fruitful results. This paper discusses about all the categories that form a major key to image processing and the outcomes of the various techniques that come under Digital Image Processing. It gives a clear description of different formats that a grayscale picture or a color picture could be classified into i.e., PNG, JPEG, GIF, TIFF, BMP. This paper also discusses about the different types of noises (Gaussian noise, salt& pepper noise, speckle noise etc.,) that distort the original picture in a minimalistic or in a large way. Multiple filters that can be applied to a picture to correct one or more errors are also specified with reference to the codes written in MATLAB. Respective outputs for the applied noise and the final images after clearance of noise using the filters are also exhibited in this journal.
Augmented Reality Application Based on Information Barcoding

In this paper we present the augmented reality application based on information barcoding. Tricolor barcoding is used for creation of meaningful augmented reality markers. Barcode markers allow to achieve two purposes: defining location and orientation of virtual objects in an augmented reality scene and storing information about these objects. To enable storing larger amount of information, we propose to use a method of tricolor barcoding with data compression. The developed augmented reality application is focused on museum exhibition extension. It can also be useful for other augmented reality use cases where textual information about virtual objects is required along with the virtual objects appearance.
A SMC Sampler for Joint Tracking and Destination Estimation from Noisy Data

In this paper we describe a Sequential Monte Carlo (SMC) sampler that performs joint tracking and destination estimation of a target traveling along a known road network, as its journey progresses. The destination estimation is based on a simplistic model of driver intent, which assumes no prior knowledge of the history of visited destinations. The proposed algorithm is capable of refining the distribution of destinations that can be inferred from an incoming stream of position estimates. We compare the performance achieved by the proposed algorithm with a mainstay Particle Filter, demonstrating how the later suffers greatly from sample impoverishment, therefore necessitating an ever increasing number of particles as the number of possible destinations increases, while showcasing that the issue is significantly mitigated by the proposed SMC Sampler.
AM-I-BLUES: An Interactive Digital Music Instrument for Guiding Novice Pianist in the Improvisation of Jazz Melodies

In this paper, we discuss the design considerations and goals of an interactive digital musical instrument (DMI) for novice pianists. It aims to promote the practice of melodic improvisation by guiding pianists in the selection of notes. The DMI includes two major components: a generative jazz model (software) and an illuminating keyboard controller (hardware). Visual feedback in the form of illuminated keyboard controller keys guides notes selection. Illuminated keys correspond to scale degrees that comply with a harmonic structure generated on-the-fly. The generative nature of the engine allows high degrees of novelty while guaranteeing a structurally-coherent harmonic structure anchored in the blues/jazz idiom. Preliminary experimental results inform critical directions for future design iterations of the proposed system.
Deep learning for biomedical videos: perspective and recommendations

Medical videos capture dynamic information of motion, velocity, and perturbation, which can assist in the diagnosis and understanding of disease. Common examples of medical videos include cardiac ultrasound to assess cardiac motion, endoscopies to screen for gastrointestinal cancers, natural videos to track human behaviors in population health, and microscopy to understand cellular interactions. Deep learning for medical video analysis is rapidly progressing and holds tremendous potential to extract actionable insights from these rich complex data. Here we provide an overview of deep learning approaches to perform segmentation, object tracking, and motion analysis from medical videos. Using cardiac ultrasound and cellular microscopy as case studies, we highlight the unique challenges of working with videos compared to the more standard models used on still images. We further discuss available video datasets that may search as good training sets and benchmarks. We conclude by discussing the future directions for this field with recommendations to practitioners.
ActiveDeeper

Deep Web (e.g., Yelp, IMDb) is an invaluable external data source for enriching a local database with new attributes. In this paper, we present ActiveDeeper, a novel model-driven data enrichment system powered by deep web. ActiveDeeper treats deep web as "a labeler" and uses it to train a data enrichment model. We show that this model-based approach significantly outperforms the state-of-the-art system in real-world scenarios. We implemented ActiveDeeper as a Google Sheets add-on and made a demo video at http://tiny.cc/activedeeper.
University Structural Units Rankings: Models and Solutions

Formation of strategic management tools in Russian universities, including mechanisms for ranking the activities of structural units is an important task of the innovative development of universities. The article discusses the rating models of structural units operating in Russian universities. Based on the analysis of real practices, the university-wide models for ranking educational and scientific units are distinguished. A complex-structured rating model of all structural divisions of the university is characterized by the example of one of the leading universities. The article highlights the general characteristics of ranking models used by domestic universities. To a large extent, they concentrate around the methods of assessing the activities of organizational units that have developed in the strategic management system. They consider the achievement of indicators of the Universities Effectiveness Monitoring as the target goals to increase their competitiveness.
Low Illumination Color Image Enhancement based on Dynamic Bistable Stochastic Resonance

To solve the problem of non-uniform low illuminantion color image enhancement, a dynamic bistable stochastic resonance (DBSR) algorithm is proposed. Based on the bistable stochastic resonance model, the algorithm can effectively enhance brightness of the low illumination image by transfer the noise energy in the image to the low illumination region. An iterative threshold of low illuminantion pixels is set to avoid over-enhancement in high brightness region. Through the contrast experiment, it is found that the DBSR has a good enhancement effect for the image of uniform low illumination and non-uniform low illumination. it can avoids the amplification noise energy and preserves the naturalness of original image.
Development of Robust Ridge Detection Method and Control System for Autonomous Navigation of Mobile Robot in Agricultural Farm

In this paper, we propose a robust ridge detection method for autonomous navigation of a mobile robot in agricultural fields using an inexpensive RGB-D camera system. In this system, the ridge centerline is estimated using the depth images from RGB-D sensor by using our proposed methods. We propose an autonomous driving system utilizing digital optimal control regulator for high-precision motion and robust recognition of ridge features. From the experimental evaluation, an accuracy level of $$\pm {1.5}$$ cm robot traversal along the farm ridge was recorded.
Generating Adversarial Examples with Image-To-Perturbation Network

Deep neural networks have been found to be easily misled by adversarial examples that are maliciously crafted by adding small perturbations. A variety of methods have been proposed to generate adversarial examples, but more efforts are needed to generate them with high perceptual quality and low computation costs. In this paper, we propose an adversarial attack method that uses a conditional encoder-decoder network named Image-To-Perturbation to generate adversarial perturbations in residual learning fashion. Image-To-Perturbation network can learn the mapping from clean images to according adversarial perturbations, once it is trained, it can generate perturbations for any examples efficiently. We test the proposed method on different target models using MNIST and CIFAR-10 datasets. The experimental results show that our model is easy to train and the generated adversarial examples are perceptually realistic and achieve high attack success rate.
Research on Book Recommendation System for People with Visual Impairment Based on Fusion of Preference and User Attention

With the development of the Internet, the information explosion problem comes into being and it is challenging for users to search for the information they needed from e-books. Although the book recommendation system can help users find their focuses, it is not applicable for visually impaired users when using ordinary visual reading methods for knowledge acquisition. Therefore, a book recommendation system that suits their behavior habits is required. In order to provide accurate and effective book sets for users, we propose an algorithm based on fusing their preferences. For intelligently ranking the candidate book sets and help users find the right book quickly, we propose a context-aware algorithm based on users’ attention. Meanwhile, we introduce an improved calculation method for users’ attention to solving the problem of inaccurate prediction on users’ current attention when their action history is cluttered. We use the self-attention to preserve the users’ reading tendencies during the reading process, analyze users’ personal features and book content features, and improve the accuracy of the recommendation by merging the feature space. Finally, the improved algorithm proposed and comparative experiments were employed on the dataset collecting from the China Blind Digital Library, and the effectiveness of the improvement is proved in each experimental comparison results.
Multi-agent Path Finding Modulo Theory with Continuous Movements and the Sum of Costs Objective

Multi-agent path finding with continuous movements and time (denoted MAPF $$^\mathcal {R}$$ ) is addressed. The task is to navigate agents that move smoothly between predefined positions to their individual goals so that they do not collide. Recently a novel solving approach for obtaining makespan optimal solutions called SMT-CBS $$^\mathcal {R}$$ based on satisfiability modulo theories (SMT) has been introduced. We extend the approach further towards the sum-of-costs objective which is a more challenging case in the yes/no SMT environment due to more complex calculation of the objective. The new algorithm combines collision resolution known from conflict-based search (CBS) with previous generation of incomplete SAT encodings on top of a novel scheme for selecting decision variables in a potentially uncountable search space. We experimentally compare SMT-CBS $$^\mathcal {R}$$ and previous CCBS (continuous conflict-based search) algorithm for MAPF $$^\mathcal {R}$$ .
Guaranteed Cost Robust Centralized Fusion Kalman Estimators with Uncertain Noise Variances and Missing Measurements

The guaranteed cost centralized fusion Kalman estimation problem for discrete-time systems with uncertain noise variances and missing measurements. Missing measurement is described as Bernoulli random variables. The system under consideration can be converted into one only with uncertain noise variances by the method of derandomness, then two classes of guaranteed cost robust centralized fusion Kalman estimators (filter, predictor and smoother) are presented by Lyapunov equation approach based on minimax robust estimation principle and parameterisation representation of uncertain noise variances. The proposed guaranteed cost estimators can concurrently give minimal upper and maximal lower bound of accuracy deviations. A unified approach of designing the robust centralized fusion Kalman estimators is presented based on the robust Kalman predictor. The proof of the guaranteed cost robustness is presented by the Lyapunov equation approach. A numerical simulation example shows the correctness and effectiveness of the proposed results.
Accuracy Analysis on Images Retrieval System using Radial Basis Function Algorithm and Coefficient Correlation

The image retrieval system is a system used for the process of retrieval of images based on information contained in the image files. Radial Basis Function (RBF) is one of the Neural Network methods used in the image retrieval system, is known for the capability to produce image information search properly. In determining the initial centroid value, the RBF method uses K-Means Clustering. This algorithm has a weakness in determining the right initial centroid value to get proper classification results in image retrieval. In this paper, the Coefficient Correlation (CC) method is used in determining the initial centroid value of the input data following the similarity of the data. Data with the highest degree of similarity compared to other data used as the initial centroid value. Data used in this study are leaf image data of 500 images with 10 categories of leaf types, and each sample contained 50 images. Based on the testing results, an increase in image retrieval accuracy with an average of 90.92% using the RBF and CC methods compared the image retrieval results using the RBF and K-Means Clustering methods gained an average accuracy of 85.96%.
Towards a formal comparison of uncertainty handling

This paper explores the use of the Uncertainty Representation and Reasoning Evaluation Framework (URREF), a framework intended to change that state of affairs, in evaluating potential uncertainty representation approaches for a maritime Decision Support System. We revisit some comparison aspects discussed along the years and map them to the URREF. We illustrate the comparison on a simple maritime use case involving basic reasoning about threat assessment, with observations from partially reliable sources. The same fusion problem is modeled with the two uncertainty theories of Bayesian probability theory and evidence theory. Within the same framework, we consider two different reasoning schemes, Causal and Evidential, complemented with a source model of partial reliability. Comparison items are mapped to URREF ontology criteria of (Representation) Expressiveness and (Reasoning) Correctness. We highlight the criteria that can be useful in supporting systems developers in their choice of how to represent and manage uncertainty in information fusion processes, and propose some refinement to the URREF to capture handling of inconsistency.
SDTCNs: A Symmetric Double Temporal Convolutional Network for Chinese NER

Chinese NER is a basic task of Chinese natural language processing. Most current models for Chinese NER can be roughly divided into two categories: character-based models and word-based models. Character-based models cannot effectively utilize the inherent information of a word. Word-based models cannot effectively disambiguate words under different word segmentation norms. In this paper, we propose a symmetric double temporal convolutional network for Chinese NER: SDTCNs. SDTCNs is built on the BERT model and is composed of two symmetric temporal convolutional networks, where one is used to learn the location features of named entities and the other is used to learn the class features of named entities. Finally, a fusion algorithm proposed in this paper is used to fuse location features and class features to obtain the final named entity. Experiments on various datasets show that SDTCNs outperforms multiple state-of-the-art models for Chinese NER, achieving the best results.
Development of TARS Mobile App with Deep Fingertip Detector for the Visually Impaired

We propose TARS mobile applications that uses a smartphone with a camera and deep learning fingertip detector for easier implementation than using a PC or a touch panel. The app was designed to recognize the user’s hand touching the images with the rear camera and provide voice guidance with the information on the images that the index finger is touching as a trigger. When performing gestures with either the index finger or thumb, the app was able to detect and output the fingertip point without delay, and it was effective as a trigger for reading. Thumb gestures are assumed to have reduced detection variances of 68% in the lateral direction because they rarely move the other four fingers compared to index finger gestures. By performing multiple detections in the application and outputting the median, the variances of detection can be reduced to 73% in the lateral direction and 70% in the longitudinal direction, which shows the effectiveness of multiple detections. These techniques are effective in reducing the variance of fingertip detection. We also confirmed that if the tilt of the device is between −3.4 mm and 4 mm, the current app could identify a 12 mm difference with an accuracy of 85.5% as an average in both of the lateral and longitudinal directions. Finally, we developed a basic model of TARS mobile app that allows easier installation and more portability by using a smart phone camera rather than a PC or a touch panel.
Modelling and Reasoning in Biomedical Applications with Qualitative Conditional Logic

Different approaches have been investigated for the modelling of real-world situations, especially in the medical field, many of which are based on probabilities or other numerical parameters. In this paper, we show how real world situations from the biomedical domain can be conveniently modelled with qualitative conditionals by presenting three case studies: modelling the classification of certain mammals, modelling infections with the malaria pathogen, and predicting the outcome of chronic myeloid leukaemia. We demonstrate that the knowledge to be modelled can be expressed directly and declaratively using qualitative conditional logic. For instance, it is straightforward to handle exceptions to a general rule as conditionals support nonmonotonic reasoning. Each of the knowledge bases is evaluated with example queries and with respect to different inference mechanisms that have been proposed for conditional knowledge, including p-entailment, system Z, and various inference relations based on c-representations. Comparing the obtained inference results with the answers expected from human experts demonstrates the feasibility of the modelling approach and also provides an empirical evaluation of the employed nonmonotonic inference relations in realistic application scenarios.
It Is Time to Seriously Consider Personal Blockchains

Much has been written about the benefits of using blockchains for securing transactions in finance, healthcare, supply chain management and more, and some of these benefits are already being realized. But not nearly as much consideration has been given to use blockchains for records of personal transactions. That is, there is great potential for blockchains in certifying and securing the validity, time, and order of various life events, legal transactions, records of achievement, personal contracts, healthcare records, etc. In this article, the motivation for creating personal blockchains is presented. Then, various kinds of personal blockchain implementation possibilities, use cases, challenges, and solutions are briefly discussed. Personal blockchains offer many benefits but pose some challenges and create some concerns. The purpose of this article is to stimulate more discussion and research into these issues.
An Improved Encoding Scheme for Distributed Video Coding Based on Wavelet Transform

Some improvement ideas have been made for distributed video coding scheme based on the wavelet transform domain. First, a simplified algorithm is introduced for integer wavelet transform coding to improve the computational efficiency of the encoding side. Secondly, in order to save the channel bandwidth, this paper proposed a direct filling method of the side information for the high-frequency part. At last, this article uses the idea of hierarchical transmission in traditional video coding for different transmission channel bandwidth. The simulation results show that the program greatly reduces the transmission bit rate with a good video transmission quality.
8th international workshop on human activity sensing corpus and applications (HASCA)

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpus and much improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition. This year HASCA will welcome papers from participants to the Third Sussex-Huawei Locomotion and Transportation Recognition Challenge and Second Nursing Activity Recognition Challenge in special sessions.
Perceptual hashing-based novel security framework for medical images

The security of medical images carrying important patient information and is a challenging issue when images are transmitted across public channels. These images are often faced with various kinds of intentional and unintentional attacks during the transmission process. To enhance the security of medical information systems and protect medical data, a novel security framework is presented in this chapter using a combination of zero watermarking and perceptual hashing. First, robust invariant features are extracted from the medical image using scale invariant feature transform and discrete cosine transform followed by a quantization process to generate the hash sequence. Finally, an encrypted watermark is used in the construction of a digital signature corresponding to the original medical images. Simulation results demonstrate that the proposed technique has greater influence on robustness against conventional attacks as well as geometric attacks.