Reviewerly/exHarmony huggingface dataset:
Contrastive Explanations for a Deep Learning Model on Time-Series Data

In the last decade, with the irruption of Deep Learning (DL), artificial intelligence has risen a step concerning previous years. Although Deep Learning models have gained strength in many fields like image classification, speech recognition, time-series anomaly detection, etc. these models are often difficult to understand because of their lack of interpretability. In recent years an effort has been made to understand DL models, creating a new research area called Explainable Artificial Intelligence (XAI). Most of the research in XAI has been done for image data, and little research has been done in the time-series data field. In this paper, a model-agnostic method called Contrastive Explanation Method (CEM) is used for interpreting a DL model for time-series classification. Even though CEM has been validated in tabular data and image data, the obtained experimental results show that CEM is also suitable for interpreting deep learning models that work with time-series data.
Coreference Resolution Improves Educational Knowledge Graph Construction

An educational knowledge graph provides students and teachers with detailed knowledge organization and a clear concept structure by extracting knowledge points and relationships from textbooks. A high-fidelity knowledge graph is essential for precise teaching and personalized learning. However, as an important step in knowledge graph construction, coreference resolution is often ignored or left to the end. This neglect leads to a loss in the high fidelity of the knowledge graph and may also cause clearly underestimated focuses and fewer associations between knowledge points, although the ratio of the pronouns to the entire corpus is very small (less than 5‰). In this paper, a rule and semantic-based method is proposed to resolve coreference in the knowledge graph constructed from a biology textbook. Compared with the other three algorithms, it has a better precision ratio and recall ratio. By comparing the two knowledge graphs constructed before and after coreference resolution, it can be seen that the focus has changed significantly to better align with the text, and the associations between the knowledge points are more consistent with intuition. This outcome suggests that coreference resolution improves the high fidelity of an educational knowledge graph.
Medical image retrieval for detecting pneumonia using binary classification with deep convolutional neural networks

In the concepts of Deep Learning, the ConvNets are a classified group of deep neural networks that are used to analyze the images. They find their major applications in image classification and in the medical image analysis. ConvNets are regularized, multilayer preceptors. The major challenge today is the increasing size of medical image repositories which is leading to troubles in large database management and then querying those databases for storage and retrieval and this can only be sorted out using the Content Based Medical Image Retrieval (CBMIR) systems. A major challenge in CBMIR systems is the semantic gap that exists between the low-level visual information captured by the imaging devices and high-level semantic information captured by human. This paper demonstrates a new and efficient framework with the algorithm of Deep CNN for the Feature Based Medical Image Retrieval (FBMIR) system for fast and efficient retrieval of medical and clinical images for detecting Pneumonia. The intermodal datasets are used and divided into two classes to train the network. One class consists of the lungs infected by Pneumonia and the other class contains the images of lungs which are normal. The learned features from the trained deep convolutional neural network with the fast processing feature of the hash method are used to reduce the feature space.To achieve the best retrieval results with class-based predictions 93.9% of average classification accuracy is achieved and 0.87 of mean average precision is achieved for this retrieval task. The proposed framework fit the best for top-ranked multimodal medical image retrieval.
Lightweight Bilateral Convolutional Neural Networks for Interactive Single-Bounce Diffuse Indirect Illumination

Physically correct, noise-free global illumination is crucial in physically-based rendering, but often takes a long time to compute. Recent approaches have exploited sparse sampling and filtering to accelerate this process but still cannot achieve interactive performance. It is partly due to the time-consuming ray sampling even at 1 sample per pixel, and partly because of the complexity of deep neural networks. To address this problem, we propose a novel method to generate plausible single-bounce indirect illumination for dynamic scenes in interactive framerates. In our method, we first compute direct illumination and then use a lightweight neural network to predict screen space indirect illumination. Our neural network is designed explicitly with bilateral convolution layers and takes only essential information as input (direct illumination, surface normals, and 3D positions). Also, our network maintains the coherence between adjacent image frames efficiently without heavy recurrent connections. Compared to state-of-the-art works, our method produces single-bounce indirect illumination of dynamic scenes with higher quality and better temporal coherence and runs at interactive framerates.
Transition-Aware Detection of Modes of Locomotion and Transportation Through Hierarchical Segmentation

Recognizing human daily activities with motion sensors data, specifically, modes of locomotion and transportation provides important contextual information that enhances the effectiveness of mobile applications. For instance, in assisted living or sports monitoring it is essential to log driving or running episodes. Previous studies in this field have utilized a fixed-size windowing technique for segmenting the sequential data of sensors. While segmenting signals into larger windows provides more information about the signal for classifiers, it increases misclassification rate when a transition occurs between the activities (i.e., multi-class windows). This will lead to inaccurate detection and logging of the activities of interest. To identify the exact time of transition from one to another activity as precisely as possible, this article proposes a fast and efficient hierarchical search algorithm that finds the exact sample at which transition occurs. This search algorithm can be applied to any activity recognition model with various lengths of segmentation window. To further improve the performance, we propose a new structure of 2D signal inputs to be used with 2D convolutional neural networks (CNN), which improves the ability of the CNN in capturing patterns underlying in inter-axes correlations. Experimental results show that the proposed transition detection method improves the F1-score by 28% compared to using fixed-size windowing approach for multi-class windows. In addition, the proposed method is 20 times faster than the naïve search.
Membrane System-Based Improved Neural Networks for Time-Series Anomaly Detection

Anomaly detection in time series has attracted much attention recently and is quite a challenging task. In this paper, a novel deep-learning approach (AL-CNN) that classifies the time series as normal or abnormal with less domain knowledge is proposed. The proposed algorithm combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to effectively model the spatial and temporal information contained in time-series data, the techniques of Squeeze-and-Excitation are applied to implement the feature recalibration. However, the difficulty of selecting multiple parameters and the long training time of a single model make AL-CNN less effective. To alleviate these challenges, a hybrid dynamic membrane system (HM-AL-CNN) is designed which is a new distributed and parallel computing model. We have performed a detailed evaluation of this proposed approach on three well-known benchmarks including the Yahoo S5 datasets. Experiments show that the proposed method possessed a robust and superior performance than the state-of-the-art methods and improved the average on three used indicators significantly.
Attention-Based Dynamic Preference Model for Next Point-of-Interest Recommendation

Next Point-of-Interest (POI) recommendation is a core task for various location-based intelligent services. It aims to predict the POI a user tends to visit next given the user’s check-in history. However, this task is not trivial because of two challenges: 1) complicated sequential patterns of user mobility and 2) dynamic evolution of user preferences. In this paper, we propose a novel model called Attention-based Dynamic Preference Model (ADPM) for next POI recommendation, which can capture dynamic long- and short-term preferences of users. In particular, we design a spatio-temporal self-attention network to explore complicated POI-POI transition relationships and capture short-term sequential patterns of users. Moreover, we utilize a user-specific attention to learn evolving long-term preferences of users based on their constantly updated check-ins. Finally, a feature gating fusion module is introduced to adaptively combine long- and short-term preferences. We conduct extensive experiments on two real-world datasets, and the results demonstrate the effectiveness of our model.
Critical analysis on the reproducibility of visual quality assessment using deep features

Data used to train supervised machine learning models are commonly split into independent training, validation, and test sets. This paper illustrates that complex data leakage cases have occurred in the no-reference image and video quality assessment literature. Recently, papers in several journals reported performance results well above the best in the field. However, our analysis shows that information from the test set was inappropriately used in the training process in different ways and that the claimed performance results cannot be achieved. When correcting for the data leakage, the performances of the approaches drop even below the state-of-the-art by a large margin. Additionally, we investigate end-to-end variations to the discussed approaches, which do not improve upon the original.
Calibration of a QEM-EDS system for rapid determination of potassium concentrations of feldspar grains used in optical dating

Potassium (K)-rich feldspars are one of two mineral types typically used for optical dating. Feldspar grains can contain up to ~14 wt% K that gives rise to an internal dose rate component. This internal component can comprise a significant proportion of the total environmental dose rate to which a mineral grain is exposed. The environmental dose rate term—the denominator in the optical age equation—determines the rate at which electronic charge is transferred into the crystal lattice of a mineral grain over its period of burial. Not all feldspar grains have the same K concentration, so internal dose rates differ between individual grains. K concentrations can range from 0 to 14 wt% due to the variable compositions of feldspar phases, differing proportions of discrete feldspar phases and/or the presence of other mineral inclusions in grains. Numerous techniques are available for determining the K concentrations of individual grains, but are time-consuming, and either lack the spatial resolution to classify discrete mineral phases within multi-phase grains, or the coverage to obtain whole-of-grain average K concentrations. Quantitative evaluation of minerals using energy dispersive spectroscopy (QEM-EDS) is a time-efficient and automated mapping technique that has the spatial resolution to classify most mineral phases, as well as the coverage to determine their area proportions. QEM-EDS can also be used to determine elemental concentrations based on spectral matches to EDS reference spectra. It is, however, difficult to determine accurate elemental concentrations for minerals such as feldspars, where solid solutions exist. To overcome this, we establish a QEM-EDS calibration using EDS spectra from six feldspar reference standards to define five solid solution regions along the alkali and plagioclase feldspar series. We test this calibration through comparisons of the K concentrations of discrete phases of three feldspar varieties (orthoclase/microcline, albite and sanidine), derived using both QEM-EDS and wavelength dispersive spectroscopy (WDS). We assume that the WDS-derived K concentrations represent the 'true' K concentrations of the phases, and calculate, from a best-fit weighted regression of the two data sets, a correction and uncertainty estimate that can be applied to the QEM-EDS-derived K concentrations, taking into account instrument irreproducibility and any measurement bias. We also test alternative mapping step sizes to optimise efficiency, and apply this time-efficient technique to individual luminescent feldspar grains from two samples. We propose that QEM-EDS is capable of (1) classifying the range of mineral phases in grains, (2) determining the area proportions of each of these phases, and (3) obtaining accurate whole-of-grain average K concentrations of individual feldspar grains using the calibration and correction presented here.
HaiQ

Formal methods used to validate software designs, like Alloy, OCL, and B, are powerful tools to analyze complex structures (e.g., architectures, object-relational mappings) captured as sets of relational constraints. However, their applicability is limited when software is subject to uncertainty (derived, e.g., from lack of control over third-party components, interaction with physical elements). In contrast, quantitative verification has emerged as a powerful way of providing quantitative guarantees about the performance, cost, and reliability of systems operating under uncertainty. However, quantitative verification methods do not retain thefl exibility of relational modeling in describing structures, forcing engineers to trade structural exploration for analytic capabilities that concern probabilistic and other quantitative guarantees. This paper contributes a method (HaiQ) that enhances structural modeling/synthesis with quantitative guarantees in the style provided by quantitative verification. It includes a language for describing structure and (stochastic) behavior of systems, and a temporal logic that allows checking probability and reward-based properties over sets of feasible design alternatives implicitly described by the relational constraints in a HaiQ model. We report the results of applying a prototype tool in two domains, on which we show the feasibility of synthesizing structural designs that optimize probabilistic and other quantitative guarantees.
A Heuristic Path Planning Algorithm for Inspection Robots

In this paper a novel heuristic path planning algorithm based on cross-entropy optimization is proposed for substation inspection robots. We seek to provide a low-cost, fast and effective solution for such robots. The optimization problem is modeled based on a topology map of the substation, it turns out to be non-convex yet requires real-time solution. A classification algorithm is applied to simplify the task points to reduce the computational cost and to speed up the path planning. The cross-entropy optimization algorithm is used to derive the shortest path among the simplified task points. Then, a path resolution algorithm is implemented to resolve the simplified path to the original task space and eventually get the real inspection path. Simulative and experimental studies have verified feasibility and effectiveness of the proposed method. Particularly, the algorithm implemented on an embedded computer delivers a solution for 153 task points in only three seconds, while very low repetition rate (about 0.2%) is approached.
A Stable Algorithm computing high-order 3D Zernike Moments and Shape Reconstructions

3D Zernike moments are used as the 3D object shape descriptors. In this paper, we introduce a series of stable algorithms for calculating high-order the 3D Zernike moments. A kind of recurrence formula among 3D Zernike radial polynomials is presented. A voxel-based algorithm is hereby developed. The proposed method provides an accurate reconstruction image. When our algorithms are applied to the 3D object 'Einstein bust' with 100x100x100 voxels, the error rate for the shape reconstruction attains its value 0.000012 and 0 for computing all moments up to order 350 (390 respectively).
Fork Probability Analysis of PoUW Consensus Mechanism

In Blockchain, fork is a fundamental impediment to consensus mechanisms such as Proof-of-Work (PoW). In this paper, we study the fork probability of the famous Proof-of-Useful-Work (PoUW) consensus mechanism, which has recently been proposed to solve the huge waste of computing resources in PoW. With PoUW, each miner first does useful work (e.g., data classification), and then competes for the leader according to the number of the executed CPU instructions. Compared with conventional factors such as network delay, the time of doing useful work is a new and salient factor leading to a fork for a PoUW-based blockchain. Taking into account some important factors (e.g., the time of doing useful work, computational power, network delay), we propose a theoretical model to characterize the impact of these factors on the PoUW fork probability. Extensive simulations verify that our model is very accurate. This study is helpful for providing guidance of configuring PoUW parameters.
Privacy Preserving Data Integration Protocol

Recently, large amount of data is collected by various organizations. Generally, data consists of various attributes such as name, address, medical term, etc. Related to the same person, different organizations often possess data with different attributes. If we can integrate data kept in different organization related to the same person without violating privacy, detailed analyzes such as cause investigation or relations among attributes could be realized. In such a scenario, we do not need personal information while it should be protected securely. Importantly, the data exactly integrates data associated with the same person. In this paper, we classify attributes in data into three of matching attributes, analyzing attributes, and others. Then, we propose a privacy preserving data integration protocol while handling data privacy appropriately according to classification of matching, analyzing attributes, and others.
Data Integrity Checking Supporting Reliable Data Migration in Cloud Storage

With the rapid development of cloud computing, more and more users are willing to employ cloud storage. As a result, an increasing number of companies invest cloud storage thus provide users with data storage services, which equipped with distinct quality, e.g., storage capacity, access speed, security, prices and reliability. To enjoy more suitable data storage service, the user may change the cloud service providers. As a result, outourced data migration between two different cloud servers has become a fundamental requirement. Therefore, how to securely migrate outsourced data from one semi-trusted cloud to another becomes a primary concern of user. To solve this problem, we construct a blockchain-based publicly verifiable outsourced data migration scheme supporting integrity verification. In the proposed scheme, the user can migrate outsourced data blocks from the original cloud to the target cloud without retrieving them from the original cloud. Meanwhile, the target cloud can check the data integrity to guarantee that the data blocks are migrated integrally. Moreover, by making use of the decentration of blockcahin, the proposed scheme can achieve public verifiability without requiring any third party auditor. Finally, we provide the security analysis and efficiency evaluation, which can respectively demonstrate the security and practicality of the novel proposed scheme.
Atomic Fisher information and entanglement forecasting for quantum system based on artificial neural network and time series model

Abstract In this article, we apply a statistical model for forecasting the quantum entanglement between a two‐qubit and optical field in binomial distribution. We explore the link between the atomic Fisher information, quantum entropy, and the statistical properties of the field. The qubit‐qubit entanglement is investigated through concurrence during the interaction time. The dynamics of the statistical quantities will be forecasted using the time series and neural network models. The effect of the field distribution parameter (number of successes) is examined by the time series models and artificial neural network. We compare the accuracy of both modes from the perspective of the dynamic of the quantum entropy and atomic Fisher information. A statistical description for the data has been obtained and is discussed to show the statistical technique analysis the data of statistical quantities. The results obtained have several applications and are related with quantum statistics and quantum information processing.
The Secret Life of Commented-Out Source Code

Source code commenting is a common practice to improve code comprehension in software development. While comments often consist of descriptive natural language, surprisingly, there exists a non-trivial portion of comments that are actually code statements, i.e., commented-out code (CO code), even in well-maintained software systems. Commented-out code practice is rarely studied and often excluded in prior studies on comments due to its irrelevance to natural language. When being openly discussed, CO practice is generally considered a bad practice. However, there is no prior work to assess the nature (prevalence, evolution, motivation, and necessity of utilization) of CO code practice.
A Blockchain-Indexed Storage supporting Scalable Data Integrity in Supply Chain Traceability

In this paper, we propose the blockchain-indexed storage (BIS) to provide scalable and secure IoT-enabled supply chain traceability. Although blockchain can prevent data from being maliciously tampered with, scalability is the challenge to directly store supply chain data into blockchain. A massive IoT devices can generate enormous transactions and data which can significantly increase latency and cost. Instead of directly storing supply chain data into blockchain, BIS stores data and data fingerprint into off-chain storages and blockchain, respectively. In addition, BIS supports adaptive transaction arrival rate to control latency and cost according to demand. Our preliminary implementation and analysis show the feasibility of applying BIS to provide scalable and secure supply chain traceability.
Improved YOLOv3 Algorithm for Ship Target Detection

To solve the problem of ship target detection in complex surface environment, a real-time Darknet-53 network model based on a deep learning framework is incorporated with the improved YOLOv3 algorithm. This method combines a Soft Non-Maximum Suppression (Soft-NMS) algorithm and a Frequency-Tuned (FT) salient region detection algorithm to detect the features of ships on water. By replacing the original NMS algorithm with Soft-NMS algorithm, the detection effect of the algorithm for small target and overlapping target is improved obviously; Incorporates the saliency region features obtained by the FT algorithm based on frequency adjustment. The saliency region features obtained by FT contain more overall information of the target. Experimental results demonstrate that compared with the original YOLOv3, the detection accuracy and speed of the proposed method are considerably improved.