Reviewerly/exHarmony huggingface dataset:
A matrix decomposition based adaptive control scheme for a class of MIMO non-canonical approximation systems

This paper presents a unified study on adaptive control of multi-input multi-output (MIMO) uncertain approximation systems in non-canonical forms. For adaptive control of such systems, this paper develops a new state feedback parametrization-based adaptive control scheme, by specifying relative degrees and establishing a normal form, deriving new system reparametrizations needed for adaptive control designs, and constructing adaptive state feedback parametrized controllers for the controlled plant with different vector relative degrees, where the uncertain control gain matrices are handled using a matrix decomposition technique. The main features of such a new control framework are demonstrated by new results for non-canonical MIMO recurrent high-order neural network systems with closed-loop stability and output tracking performance analyzed. An illustrative example with simulation results is presented to show the control design procedure and desired system performance.
LDPart: Effective Location-Record Data Publication via Local Differential Privacy

Driven by the advance of positioning technology and the tremendous popularity of location-based services, location-record data have become unprecedentedly available. Publishing such data is of vital importance to the advancement of a wide spectrum of applications, such as marketing analysis, targeted advertising, and urban planning. However, the data collection may pose considerable threats to the individuals privacy. Local differential privacy (LDP) has recently emerged as a strong privacy standard for collecting sensitive information from users. Due to the inherent high dimensionality, it is particularly challenging to publish the location-record data under LDP. In this paper, we propose LDPart, a probabilistic top-down partitioning algorithm to effectively generate a sanitized location-record data. Our approach employs a carefully designed partition tree model to extract essential information in terms of location records. Furthermore, it also makes use of a novel adaptive user allocation scheme and a series of optimization techniques to improve the accuracy of the released data. The extensive experiments conducted on real-world datasets demonstrate that the proposed approach maintains high utility while providing privacy guarantees.
Central Bank Digital Currencies: Preliminary Legal Observations

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Spatio-temporal attention mechanisms based model for collective activity recognition

Collective activity recognition involving multiple people active and interactive in a collective scenario is a widely-used but challenging domain in computer vision. The key to this end task is how to efficiently explore the spatial and temporal evolutions of the collective activities. In this paper we propose a spatio-temporal attention mechanisms based model to exploit spatial configurations and temporal dynamics in collective scenes. We present ingenious spatio-temporal attention mechanisms built from both deep RGB features and human articulated poses to capture spatio-temporal evolutions of individuals’ actions and the collective activity. Benefited from these attention mechanisms, our model learns to spatially capture unbalanced person–group interactions for each person while updating each individual state based on these interactions, and temporally assess reliabilities of different video frames to predict the final label of the collective activity. Furthermore, the long-range temporal variability and consistency are handled by a two-stage Gated Recurrent Units (GRUs) network. Finally, to ensure effective training of our model, we jointly optimize the losses at both person and group levels to drive the model learning process. Experimental results indicate that our method outperforms the state-of-the-art on Volleyball dataset. More check experiments and visual results demonstrate the effectiveness and practicability of the proposed model.
Large-scale Coherent Ising Machine

Today, world-wide efforts are being undertaken to explore non-von Neumann computation methods to overcome "the end of Moore's law". A promising approach is the coherent Ising machine (CIM), which utilizes a network of degenerate optical parametric oscillators for solving ground-state-search problems of the Ising model. Since its proposal in 2013 and the first proof-of-principle experiment in 2014, research on experimental CIMs has made rapid progress and now reached the level where we can find solutions to large-scale combinatorial optimization problems using a CIM system. In this paper, we review recent progress on CIMs, mainly focusing on the experimental side.
Pulsar Detection for Wavelets SODA and Regularized Fuzzy Neural Networks Based on Andneuron and Robust Activation Function

The use of intelligent models may be slow because of the number of samples involved in the problem. The identification of pulsars (stars that emit Earth-catchable signals) involves collecting thousands of signals by professionals of astronomy and their identification may be hampered by the nature of the problem, which requires many dimensions and samples to be analyzed. This paper proposes the use of hybrid models based on concepts of regularized fuzzy neural networks that use the representativeness of input data to define the groupings that make up the neurons of the initial layers of the model. The andneurons are used to aggregate the neurons of the first layer and can create fuzzy rules. The training uses fast extreme learning machine concepts to generate the weights of neurons that use robust activation functions to perform pattern classification. To solve large-scale problems involving the nature of pulsar detection problems, the model proposes a fast and highly accurate approach to address complex issues. In the execution of the tests with the proposed model, experiments were conducted explanation in two databases of pulsars, and the results prove the viability of the fast and interpretable approach in identifying such involved stars.
Hyperspectral image compression using hybrid transform with different wavelet-based transform coding

Hyperspectral image resolution offers limited spectral bands within a continual spectral spectrum, creating one of the spectra of most pixels inside the sequence which contains huge volume of data. Data transmission and storage is a challenging task. Compression of hyperspectral images are inevitable. This work proposes a Hyperspectral Image (HSI) compression using Hybrid Transform. First the HSI is decomposed into 1D and it is clustered and tiled. Each cluster is applied with Integer Karhunen–Loeve Transform (IKLT) and as such it is applied for whole image to get IKLT bands in spectral dimension. Then IKLT bands are applied with Integer Wavelet Transform (IDWT) to decorrelate the spatial data in spatial dimension. The combination of IKLT and IDWT is known as Hybrid transform. Second, the decorrelated wavelet coefficients are applied to Spatial-oriention Tree Wavelet (STW), Wavelet Difference Reduction (WDR) and Adaptively Scanned Wavelet Difference Reduction (ASWDR). The experimental result shows STW algorithm using Hybrid Transform gives better PSNR (db) and bits per pixel per band (bpppb) for hyperspectral images. The comparison between STW, WDR and ASWDR with Hybrid Transform for Indian Pines, Salinas, Botswana, Botswana and KSC images is experimented.
PV Tracking Design Methodology Based on an Orientation Efficiency Chart

This work describes a new photovoltaic (PV) sun tracker design methodology that utilizes the advantages that the orientation and efficiency of the PV panel offer due to the latitude of the installation zone. Furthermore, the proposed design methodology is validated experimentally via the implementation of a solar tracker with dual axes at a specific location (27.5° latitude). In this case, the methodology enables the incorporation of a high-availability, low-accuracy, and low-cost tracking mechanism. Based on the results, the feasibility of this type of solar tracker for latitudes close to 30° is demonstrated, as this tracking system costs 27% less than the traditional commercial systems that use slew drives. This system increases the collection efficiency by 24% with respect to a fixed device. The proposed methodology, which is based on an orientation efficiency chart, can be applied to the construction or control of other types of solar tracker systems.
Identifying Redundancies in Fork-based Development

Fork-based development is popular and easy to use, but makes it difficult to maintain an overview of the whole community when the number of forks increases. This may lead to redundant development where multiple developers are solving the same problem in parallel without being aware of each other. Redundant development wastes effort for both maintainers and developers. In this paper, we designed an approach to identify redundant code changes in forks as early as possible by extracting clues indicating similarities between code changes, and building a machine learning model to predict redundancies. We evaluated the effectiveness from both the maintainer's and the developer's perspectives. The result shows that we achieve 57-83% precision for detecting duplicate code changes from maintainer's perspective, and we could save developers' effort of 1.9-3.0 commits on average. Also, we show that our approach significantly outperforms existing state-of-art.
lsemantica: A command for text similarity based on latent semantic analysis

In this article, I present the lsemantica command, which implements latent semantic analysis in Stata. Latent semantic analysis is a machine learning algorithm for word and text similarity comparison and uses truncated singular value decomposition to derive the hidden semantic relationships between words and texts. lsemantica provides a simple command for latent semantic analysis as well as complementary commands for text similarity comparison.
Improving transparency of deep neural inference process

Deep learning techniques are rapidly advanced recently and becoming a necessity component for widespread systems. However, the inference process of deep learning is black box and is not very suitable to safety-critical systems which must exhibit high transparency. In this paper, to address this black-box limitation, we develop a simple analysis method which consists of (1) structural feature analysis: lists of the features contributing to inference process, (2) linguistic feature analysis: lists of the natural language labels describing the visual attributes for each feature contributing to inference process, and (3) consistency analysis: measuring consistency among input data, inference (label), and the result of our structural and linguistic feature analysis. Our analysis is simplified to reflect the actual inference process for high transparency, whereas it does not include any additional black-box mechanisms such as LSTM for highly human readable results. We conduct experiments and discuss the results of our analysis qualitatively and quantitatively and come to believe that our work improves the transparency of neural networks. Evaluated through 12,800 human tasks, 75% workers answer that input data and result of our feature analysis are consistent, and 70% workers answer that inference (label) and result of our feature analysis are consistent. In addition to the evaluation of the proposed analysis, we find that our analysis also provides suggestions, or possible next actions such as expanding neural network complexity or collecting training data to improve a neural network.
Theme and sentiment analysis model of public opinion dissemination based on generative adversarial network

An epidemic is a typical public health emergency that refers to the occurrence and rapid spread of disease. A good epidemic transmission model plays a crucial role in preventing an epidemic. The epidemic transmission model is largely similar to the model of sentiment analysis and transmission on social media. Therefore, this paper intend to use the method of deep learning to explore the key issues of theme and sentiment analysis from the perspective of public opinion analysis. In order to fully extract the features automatically, we combine the following methods: multi-channel inputs, multi-granularity convolution kernels, direct connection with high-speed channels, and this paper proposes the multi-channel and multi-kernel (MCMK) model. Furthermore, we leverage generative adversarial nets to combine several single tasks, called Joint-MCMK model, which achieves information sharing and improves the training speed and model accuracy. To verify the validity of our proposed models, this paper experimented with the short text topic classification dataset TREC [1] and the sentiment analysis dataset IMDB [2]. Results achieved 98.6% and 92.6% respectively, which are superior to the highest existing industry benchmark (96.1% and 92.58%). In addition, this paper compared the training spread differences between the joint-MCMK model and MCMK model, which shows that the joint-MCMK model has better performance at training speed. Finally, the control variable method was used to analyze the multiple effects of the different factors. The optimal value of some relevant parameters in our models were verified by several experiments.
Modeling traveler experience for designing urban mobility systems

Travelers interact with a large number and variety of products and services during their journeys. The quality of a travel experience depends on a whole urban mobility system considered in space and time. This paper outlines the relevant concepts to be considered in designing urban mobility. The goal is to provide a language and insights for the early stages of a design process. A literature review sheds light on the complexity of urban mobility from technical, sociotechnical, and user experience perspectives. Observations of experiences in urban areas provide data for describing and understanding travel experience patterns and issues. The paper proposes a conceptual model to describe and analyze different facets of traveler experience and categorizes problems that travelers face when they interact with an urban mobility system. A case study is reported illustrating the use of the conceptual model in identifying travel problems for a demand-responsive transport service.
Efficient LBS queries with mutual privacy preservation in IoV

Public awareness on privacy stimulates many researches about privacy-preserving location based services (LBS) in terms of providing mutual privacy to both LBS and its users. However, the high latency of privacy preservation in LBS becomes a main obstacle for applying LBS to Internet of Vehicles (IoV). To solve this problem, we propose two privacy-preserving LBS query schemes (kNN and T-kNN) by taking the advance of fog computing and by applying oblivious transfer (OT) and ciphertext-policy attribute based encryption (CP-ABE). Given a query from a vehicle, both schemes return k nearest POIs as response, with the difference that T-kNN supports fine-grained type based POI queries. Based on our proposed oblivious key transfer and privacy-preserving secret key generation, both schemes preserve mutual privacy of both LBS provider and vehicles. Complexity analysis and empirical study show that our approach outperforms the other two state-of-the-art works.
The Forbidden Region Self-Organizing Map Neural Network

Self-organizing maps (SOMs) are aimed to learn a representation of the input distribution which faithfully describes the topological relations among the clusters of the distribution. For some data sets and applications, it is known beforehand that some regions of the input space cannot contain any samples. Those are known as forbidden regions. In these cases, any prototype which lies in a forbidden region is meaningless. However, previous self-organizing models do not address this problem. In this paper, we propose a new SOM model which is guaranteed to keep all prototypes out of a set of prespecified forbidden regions. Experimental results are reported, which show that our proposal outperforms the SOM both in terms of vector quantization error and quality of the learned topological maps.
Short Term Solar Irradiance Forecast based on Image Processing and Cloud Motion Detection

Photovoltaic (PV) grid integration has been the epicenter of research across the globe since their intermittent nature of solar generation can be more predictable. Irradiance forecast using different methods for various time horizons has been the center of attention in the recent literature. In this study, a framework for a very short term irradiance forecast is proposed via combining image processing and machine learning. A series of whole sky images is used for this purpose. Cloud detection and movement tracking are accomplished based on image processing algorithms, future position of the clouds and occlusion to the sun. Then, the irradiance drop is predicted using machine learning algorithms. The effectiveness of the proposed technique is evaluated by the Root Mean Square Error (RMSE) between the actual and forecast values of solar irradiance.
Descriptive Image Analysis. Foundations and Descriptive Image Algebras

The paper is devoted to Descriptive Image Analysis (DA) — a leading line of the modern mathematical theory of image analysis. DA is a logically organized set of descriptive methods, mathematical objects, and models and representations aimed at analyzing and evaluating the information represented in the form of images, as well as for automating the extraction from images of knowledge and data needed for intelligent decision-making. The basic idea of DA consists of embedding all processes of analysis (processing, recognition, understanding) of images into an image formalization space and reducing it to (1) construction of models/representations/formalized descriptions of images; (2) construction of models/representations/formalized descriptions of transformations over models and representations of images. We briefly discuss the basic ideas, methodological principles, mathematical methods, objects, and components of DA and the basic results determining the current state of the art in the field. Image algebras (IA) are considered in the context of a unified language for describing mathematical objects and operations used in image analysis (the standard IA by Ritter and the descriptive IA by Gurevich).
Discovery and Recognition of Emerging Human Activities Using a Hierarchical Mixture of Directional Statistical Models

Human activity recognition plays a significant role in enabling pervasive applications as it abstracts low-level noisy sensor data into high-level human activities, which applications can respond to. With more and more activity-aware applications deployed in real-world environments, a research challenge emerges-discovering and learning new activities that have not been pre-defined or observed in the training phase. This paper tackles this challenge by proposing a hierarchical mixture of directional statistical models. The model supports incrementally, continuously updating the activity model over time with the reduced annotation effort and without the need for storing historical sensor data. We have validated this solution on four publicly available, third-party smart home datasets, and have demonstrated up to 91.5 percent accuracies of detecting and recognising new activities.
Efficient Robust Model Fitting for Multistructure Data Using Global Greedy Search

In this paper, a new robust model fitting method is proposed to efficiently segment multistructure data even when they are heavily contaminated by outliers. The proposed method is composed of three steps: first, a conventional greedy search strategy is employed to generate (initial) model hypotheses based on the sequential "fit-and-remove" procedure because of its computational efficiency. Second, to efficiently generate accurate model hypotheses close to the true models, a novel global greedy search strategy initially samples from the inliers of the obtained model hypotheses and samples subsequent data subsets from the whole input data. Third, mutual information theory is applied to fuse the model hypotheses of the same model instance. The conventional greedy search strategy is used to generate model hypotheses for the remaining model instances, if the number of retained model hypotheses is less than that of the true model instances after fusion. The second and the third steps are performed iteratively until an adequate solution is obtained. Experimental results demonstrate the effectiveness and efficiency of the proposed method for model fitting.
Aggregating CNN and HOG features for Real-Time Distracted Driver Detection

Detecting distracted behaviors of drivers, and warning them in real-time can reduce the number of road accidents. Recently, Convolutional Neural Network (CNN) has been successfully applied for this task, however, a huge number of learn-able parameters makes it problematic for real-time systems. To alleviate this issue, we propose a robust method that consists of a modification of AlexNet architecture with the aggregation of HOG features. The number of parameters in our model compared to AlexNet reduces from 62.3M to 9.7M, where evaluation on publicly available dataset shows our model's comparative accuracy of 93.19% against 93.65% of the original AlexNet.
Nature-Inspired Multiobjective Cancer Subtype Diagnosis

Cancer gene expression data is of great importance in cancer subtype diagnosis and drug discovery. Many computational methods have been proposed to classify subtypes using those data. However, most of the previous computational methods suffer from poor interpretability, experimental noises, and low diagnostic quality. To address those problems, multiobjective ensemble cuckoo search based on decomposition (MOECSA) is proposed to optimize those four objectives simultaneously including the number of features, the accuracy, and two entropy-based measures: the relevance and the redundancy, classifying the cancer gene expression data with high predictive power for different cardinality levels under multiple objectives. A novel binary encoding is proposed to choose gene subsets from the cancer gene expression data for calculating four objective functions. Furthermore, an effective ensemble mechanism blended in the cuckoo search algorithm framework is applied to balance the convergence speed and population diversity in MOECSA. To demonstrate the effectiveness and efficiency of the proposed algorithm, experiments on thirty-five benchmark cancer gene expression datasets, four independent disease datasets, and one sequencing-based dataset are carried out to compare MOECSA with the six state-of-the-art multiobjective evolutionary algorithms and seven traditional classification algorithms. The experimental results in different perspectives demonstrate that MOECSA has better diagnosis performance than others at multiple levels.
Interactive Visual Event Analytics: Opportunities and Challenges

Visual tools that analyze events can reveal patterns, interesting rarities, and troubling anomalies in a vast array of data sets. New research and the integration of statistical data-mining methods is required to address data quality, scalability, theoretical grounding, and meaningful presentations.