Reviewerly/exHarmony huggingface dataset:
An efficient automated incremental density-based algorithm for clustering and classification

Data clustering divides the datasets into different groups. Incremental Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a famous density-based clustering technique able to find the clusters of variable sizes and shapes. The quality of incremental DBSCAN results has been influenced by two input parameters: MinPts (Minimum Points) and Eps (Epsilon). Therefore, the parameter setting is one of the major problems of incremental DBSCAN. In the present article, an improved incremental DBSCAN accorded to Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been presented to address the issue. The proposed algorithm adjusts the two parameters (MinPts and Eps) of the incremental DBSCAN via the iteration and the fitness functions to enhance the clustering precision. Moreover, our proposed method introduces suitable fitness functions for both labeled and unlabeled datasets. We have also improved the efficiency of the proposed hybrid algorithm by parallelization of the optimization process. The evaluation of the introduced method has been done through some textual and numerical datasets with different shapes, sizes, and dimensions. According to the experimental results, the proposed algorithm provides better results than Multi-Objective Particle Swarm Optimization (MOPSO) based incremental DBSCAN and a few well-known techniques, particularly regarding the shape and balanced datasets. Also, good speed-up can be reached with a parallel model compared with the serial version of the algorithm.
Research Progress of RNN Language Model

Language model is the cornerstone of natural language processing (NLP). The language model based on recurrent neural network (RNN) has become the most widely used language modeling technology because of its strong ability of self-learning and analysis of sequence data. In this paper, RNN language model is combed in detail. Firstly, the development history and application of RNN language model are briefly summarized. Then, the general mathematical description form of language model, the basic structure and calculation process of classical RNN language model are introduced. Then, the research progress of RNN language model in computational complexity and model performance improvement is mainly introduced. Finally, the future research of RNN language model is discussed.
Explainable time–frequency convolutional neural network for microseismic waveform classification

Geological hazards caused by rock failure severely threaten the safety of underground projects, and thus microseismic monitoring systems have been deployed to monitor the rock mass stability. However, due to implicit subseries patterns and sparse distinguishing features, automatic discrimination of the microseismic waveforms of rock fracturing remains a great challenge. Deep neural networks offer powerful learning ability, but the unexplainability of the neural network carries substantial risks to decision-making in safety warning. To this end, we propose an explainable convolutional neural network XTF-CNN that supplies both excellent classification performance and explainability. XTF-CNN consists of two major modules: 1) a dual-channel classification module that learns microseismic features from both the time and frequency domains and 2) an explanation module that demonstrates fine-grained and comprehensible results. Experiments are conducted using microseismic wave-forms collected from a deep tunnel project. The results indicate that XTF-CNN achieves superior classification performance over rival methods and significant comprehensibility.
Are open set classification methods effective on large-scale datasets?

Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers often require the ability to recognize inputs from outside the training set as unknowns. This problem has been studied under multiple paradigms including out-of-distribution detection and open set recognition. For convolutional neural networks, there have been two major approaches: 1) inference methods to separate knowns from unknowns and 2) feature space regularization strategies to improve model robustness to novel inputs. Up to this point, there has been little attention to exploring the relationship between the two approaches and directly comparing performance on large-scale datasets that have more than a few dozen categories. Using the ImageNet ILSVRC-2012 large-scale classification dataset, we identify novel combinations of regularization and specialized inference methods that perform best across multiple open set classification problems of increasing difficulty level. We find that input perturbation and temperature scaling yield significantly better performance on large-scale datasets than other inference methods tested, regardless of the feature space regularization strategy. Conversely, we find that improving performance with advanced regularization schemes during training yields better performance when baseline inference techniques are used; however, when advanced inference methods are used to detect open set classes, the utility of these combersome training paradigms is less evident.
A Modified Matrix Adaptation Evolution Strategy with Restarts for Constrained Real-World Problems

In combination with successful constraint handling techniques, a Matrix Adaptation Evolution Strategy (MA-ES) variant (the εMAg-ES) turned out to be a competitive algorithm on the constrained optimization problems proposed for the CEC 2018 competition on constrained single objective real-parameter optimization. A subsequent analysis points to additional potential in terms of robustness and solution quality. The consideration of a restart scheme and adjustments in the constraint handling techniques put this into effect and simplify the configuration. The resulting BP-εMAg-ES algorithm is applied to the constrained problems proposed for the IEEE CEC 2020 competition on Real-World Single-Objective Constrained optimization. The novel MA-ES variant realizes improvements over the original εMAg-ES in terms of feasibility and effectiveness on many of the real-world benchmarks. The BP-εMAg-ES realizes a feasibility rate of 100% on 44 out of 57 real-world problems and improves the best-known solution in 5 cases.
xTSeH: A Trusted Platform Module Sharing Scheme Towards Smart IoT-eHealth Devices

IoT based eHealth system brings a revolution to healthcare industry, with which the old healthcare systems can be updated into smarter and more personalized ones. The practitioners can continue monitoring the physical status of the patients at anytime and anywhere, and develop more precise treatment plans by analyzing the collected data, such as heart rate, blood pressure, blood glucose. Actually, these smart sensors used in eHealth system are smart embedded devices (SED). Due to the limitations on hardware capabilities, these inter-connected SEDs lack of security considerations in design and implementation, and face the threats from the network. To prevent the malicious users (or programs) from tampering with the SEDs, trusted platform module (TPM) is adopted, which can guarantee the system integrity via detecting unauthorized modifications to data and system environment. However, due to the limited scalability and insufficient system resources, not all SEDs can be deployed with TPM chips. To address this issue, in this paper, a TPM extension scheme (xTSeH) is proposed. In xTSeH, we have extended the functions of a TPM deployed in a SED (TSED) to those non-TPM-protected SEDs (N-TSED) via network. A shadow TPM in the form of a kernel module is designed as the trust base for the N-TSED, which is the representative of the TPM in TSED. Then, three protocols are proposed to implement the integrity verification and inter-SED authentication. Finally, a Raspberry Pi based prototype system is designed and implemented. The feasibility and usability of our scheme are proved by the analysis of the experimental results of system performance.
Assessment of dysplasia in bone marrow smear with convolutional neural network

Abstract In this study, we developed the world's first artificial intelligence (AI) system that assesses the dysplasia of blood cells on bone marrow smears and presents the result of AI prediction for one of the most representative dysplasia—decreased granules (DG). We photographed field images from the bone marrow smears from patients with myelodysplastic syndrome (MDS) or non-MDS diseases and cropped each cell using an originally developed cell detector. Two morphologists labelled each cell. The degree of dysplasia was evaluated on a four-point scale: 0–3 (e.g., neutrophil with severely decreased granules were labelled DG3). We then constructed the classifier from the dataset of labelled images. The detector and classifier were based on a deep neural network pre-trained with natural images. We obtained 1797 labelled images, and the morphologists determined 134 DGs (DG1: 46, DG2: 77, DG3: 11). Subsequently, we performed a five-fold cross-validation to evaluate the performance of the classifier. For DG1–3 labelled by morphologists, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 91.0%, 97.7%, 76.3%, 99.3%, and 97.2%, respectively. When DG1 was excluded in the process, the sensitivity, specificity, PPV, NPV, and accuracy were 85.2%, 98.9%, 80.6%, and 99.2% and 98.2%, respectively.
Intelligent and Reliable Deep Learning LSTM Neural Networks-Based OFDM-DCSK Demodulation Design

Chaos communications have widely been applied to provide secure, and anti-jamming transmissions by exploiting the irregular chaotic behavior. However, the real-valued chaotic sequences imposed on the information induce interferences to the user data, thereby leading to reliability performance degradations. To address this issue, in this paper, we propose to utilize the intelligent, and feature extraction capability of the deep neural network (DNN) to learn the transmission patterns to demodulate the received signals. In our design, we propose to construct the long short-term memory (LSTM) unit-aided intelligent DNN-based deep learning (DL) demodulator for orthogonal frequency division multiplexing-aided differential chaos shift keying (OFDM-DCSK) systems. After learning, and extracting features of information-bearing chaotic transmissions at the training stage, the received signals can be recovered efficiently, and reliably at the deployment stage. Thanks to the recursive LSTM-aided DL design, correlations between information-bearing chaotic modulated signals can be exploited to enhance reliability performances. Simulation results demonstrate with the proposed DL demodulation design, the intelligent OFDM-DCSK system can achieve more reliable performances over additive white Gaussian noise (AWGN) channel, and fading channels compared with benchmark systems.
Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017)

The study assesses the long-term spatial and temporal solar resource variability in America using the 20-year National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB). The coefficient of variation (COV) is used to analyze the spatial and temporal (interannual and seasonal) variability. Further, both spatial and temporal long-term variability are analyzed using the Köppen-Geiger climate classification. The temporal variability is found that, on average, the continental United States (CONUS) COV reaches up to 5% for global horizontal irradiance (GHI) and 10% for direct normal irradiance (DNI), and that the NSRDB domain's COV is roughly twice that of CONUS. For the seasonal variability analysis, the winter months are found to exhibit higher COV than the other seasons. In particular, December exhibits the highest variability, reaching on average 30% for DNI and 20% for GHI over various areas. On the other hand, the summer months demonstrate significantly lower variability, reaching only less than 20% for DNI and 10% for GHI, on average. Similarly, the spatial variability is analyzed by comparing each pixel to its neighbors. The long-term spatial variability is found to increase with the number of neighboring pixels being considered, which is equivalent to an increase in distance (within a 100-km x 100-km square grid). As expected, the DNI spatial variability is higher than that of GHI. Moreover, the annual solar irradiance anomalies are found to reach ±25% for both GHI and DNI (and even exceed those value in some instances) during each year of the 20-year period.
Platform design for lifelog-based smart lighting control

Lifelog refers to daily life records of individuals and includes their environmental, activity, emotional, and biometric information. The utilization of lifelogs makes it possible to provide a personalized lighting environment in line with a user's characteristics. However, no such customized lighting environment has been proposed thus far because methods for collecting and classifying the necessary data and a platform for synthesizing the data are not available. Therefore, in this study, the concept of the lifelog-based smart lighting control is introduced, and lifelog collection methods and a method to recommend the appropriate lighting environment for an individual by analyzing the collected information are proposed. Sensors, lighting controllers, and control interfaces required for lifelog-based smart lighting control were installed in a mock-up space, a machine learning server was set up, and a platform for implementing the optimal lighting environment was constructed by connecting these devices to the cloud. The platform constructed in this study creates a personalized lighting environment by utilizing lifelogs that are divided into emotional information collected via instant message (IM) text analysis, activity information collected using a location and activity tracker, and environmental information collected from weather data.
Learning Temporary Block-Based Bidirectional Incongruity-Aware Correlation Filters for Efficient UAV Object Tracking

In the field of UAV object tracking, correlation filter based approaches have received lots of attention due to their computational efficiency.The methods learn filters by the ridge regression and generate response maps to distinguish the specified target from the background.An ideal filter can predict the object's position in a new frame, and in turn, can backtrack the object in the past frames.However, the neglect of tracking reversibility in most methods limits the potential of using inter-frame information to improve performance.In this work, a novel bidirectional incongruity-aware correlation filter is presented based on the nature of tracking reversibility.The proposed method incorporates the response-based bidirectional incongruity, which represents the gap between the filters' discriminative difference in the forward and backward tracking perspective caused by object appearance changes.It enables the filter not only to inherit the discriminability from previous filters but also to enhance the generalization capability to unpredictable appearance variations in upcoming frames.Moreover, a temporary block-based strategy is introduced to empower the filter accommodate more drastic object appearance changes and make more effective use of inter-frame information.Comprehensive experiments are conducted on three challenging UAV tracking benchmarks, including UAV123@10fps, DTB70, and UAVDT.Experimental results indicate that the proposed method has superior performance compared with the other 34 state-of-the-art trackers.Our approach permits real-time performance at ∼46.8 FPS on a single CPU and is suitable for UAV online tracking applications.
Oracle AutoML

Machine learning (ML) is at the forefront of the rising popularity of data-driven software applications. The resulting rapid proliferation of ML technology, explosive data growth, and shortage of data science expertise have caused the industry to face increasingly challenging demands to keep up with fast-paced develop-and-deploy model lifecycles. Recent academic and industrial research efforts have started to address this problem through automated machine learning (AutoML) pipelines and have focused on model performance as the first-order design objective. We present Oracle AutoML, a novel iteration-free AutoML pipeline designed to not only provide accurate models, but also in a shorter runtime. We are able to achieve these objectives by eliminating the need to continuously iterate over various pipeline configurations. In our feed-forward approach, each pipeline stage makes decisions based on metalearned proxy models that can predict candidate pipeline configuration performances before building the full final model. Our approach, which builds and tunes only the best candidate pipeline, achieves better scores at a fraction of the time compared to state-of-the-art open source AutoML tools, such as H2O and Auto-sklearn. This makes Oracle AutoML a prime candidate for addressing current industry challenges.
Card-based protocols for secure ranking computations

Consider a group of people who want to know the “rich list” among them, namely the ranking in terms of their total assets, without revealing any information about the actual value of their assets. This can be achieved by a “secure ranking computation,” which was first considered by Jiang and Gong (2006) [2]; they constructed a secure ranking computation protocol based on a public-key cryptosystem. In this paper, instead of using a public-key cryptosystem, we use a deck of physical cards to provide secure ranking computation protocols. Therefore, our card-based protocols do not rely on computers, and they are simple and easy for humans to implement. Specifically, we design four protocols considering tradeoffs between the number of cards and the number of shuffles required to execute the protocols. We also present a guide to choose an appropriate protocol according to the number of people participating in the protocol and the size of the input range. To be precise, whereas our protocols make all players know the rich list, the Jiang–Gong scheme makes each player know his/her rank only; to achieve the same task (as the Jiang–Gong scheme) using a deck of cards is an intriguing open problem.
The drivers of financial development: Global evidence from internet and mobile usage

We study impact of internet and mobile usage on nine different indicators of financial development (FD), including depth, access, and efficiency of both, financial markets, and financial institutions, as well as overall financial development. We apply Granger causality and cointegration tests, PMG ARDL and PDOLS, and a two-step system GMM to a sample of 109 economies and two sub-samples (62 low- and middle-income economies (LMEs), 47 high-income economies (HIEs)) over the period of 1998–2017. The Granger causality tests show long-run bi-directional causality between internet/mobile usage and financial development. We find that internet usage has a significant negative impact on overall financial development, which could be attributed to a negative impact on financial institutions with all their three dimensions, depth, access, and efficiency. At the same time, internet has significant positive impact on financial markets with its three dimensions. Contrary to the opposing effects internet usage, mobile usage has a significant positive impact on all nine indices of financial development. The PMG ARDL and PDOLS estimations clarify that the positive impact of the internet is a short run effect, while the negative effect is a long-run one. The mobile usage impact is a long-run phenomenon. The estimations for two sub-samples show consistently positive impact of mobile phones in HIEs, whereas the results for LMEs are less robust.
Learning Long-Term Structural Dependencies for Video Salient Object Detection

Existing video salient object detection (VSOD) methods focus on exploring either short-term or long-term temporal information. However, temporal information is exploited in a global frame-level or regular grid structure, neglecting interframe structural dependencies. In this paper, we propose to learn long-term structural dependencies with a structure-evolving graph convolutional network (GCN). Particularly, we construct a graph for the entire video using a fast supervoxel segmentation method, in which each node is connected according to spatio-temporal structural similarity. We infer the inter-frame structural dependencies of salient object using convolutional operations on the graph. To prune redundant connections in the graph and better adapt to the moving salient object, we present an adaptive graph pooling to evolve the structure of the graph by dynamically merging similar nodes, learning better hierarchical representations of the graph. Experiments on six public datasets show that our method outperforms all other state-of-the-art methods. Furthermore, We also demonstrate that our proposed adaptive graph pooling can effectively improve the supervoxel algorithm in the term of segmentation accuracy.
Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data

High-energy physics detectors, images, and point clouds share many similarities in terms of object detection. However, while detecting an unknown number of objects in an image is well established in computer vision, even machine learning assisted object reconstruction algorithms in particle physics almost exclusively predict properties on an object-by-object basis. Traditional approaches from computer vision either impose implicit constraints on the object size or density and are not well suited for sparse detector data or rely on objects being dense and solid. The object condensation method proposed here is independent of assumptions on object size, sorting or object density, and further generalises to non-image-like data structures, such as graphs and point clouds, which are more suitable to represent detector signals. The pixels or vertices themselves serve as representations of the entire object, and a combination of learnable local clustering in a latent space and confidence assignment allows one to collect condensates of the predicted object properties with a simple algorithm. As proof of concept, the object condensation method is applied to a simple object classification problem in images and used to reconstruct multiple particles from detector signals. The latter results are also compared to a classic particle flow approach.