Reviewerly/exHarmony huggingface dataset:
Advances in iridium nano catalyst preparation, characterization and applications

The present article defines the state-of-the-art of iridium nano catalyst. In this article, the attempts are made to discuss the syntheses, characterizations, applications, toxicity and future perspectives aspects. Over all, the available information on nano iridium catalyst is included in this article. The most important applications of nano iridium catalyst are water splitting, hydrogen and oxygen generation, chemical reactions, fuel cells, sensors (detection), absorption and some other miscellaneous. The growth of the research papers and applications of iridium nano catalyst indicated bright future in various industrial applications. It was observed that iridium nano catalysts are not fully developed and under their development stages. Certainly, these nano materials will be very valuable in a wide range of scientific and industrial applications in near future.
An improved color image encryption scheme and cryptanalysis based on a hyperchaotic sequence

In this paper, a three-dimensional (3D) discrete hyperchaotic map is introduced on the basis of the Marotto theorem. On the basis of the 3D discrete hyperchaotic map, an improved color image encryption scheme is designed. In this algorithm, the chaotic sequence generated by the 3D discrete hyperchaotic map confuses each pixel position of the color image, and the pixel values are replaced using DNA sequence operation to diffuse them. Security is analyzed by several common attack methods. Cryptanalysis shows that the improved encryption algorithm has superior security characteristics: not only does it prevent common attacks, but it does so more effectively than others and is easy to realize.
A novel multi-step Q-learning method to improve data efficiency for deep reinforcement learning

Deep reinforcement learning (DRL) algorithms with experience replays have been used to solve many sequential learning problems. However, in practice, DRL algorithms still suffer from the data inefficiency problem, which limits their applicability in many scenarios, and renders them inefficient in solving real-world problems. To improve the data efficiency of DRL, in this paper, a new multi-step method is proposed. Unlike traditional algorithms, the proposed method uses a new return function, which alters the discount of future rewards while decreasing the impact of the immediate reward when selecting the current state action. This approach has the potential to improve the efficiency of reward data. By combining the proposed method with classic DRL algorithms, deep Q-networks (DQN) and double deep Q-networks (DDQN), two novel algorithms are proposed for improving the efficiency of learning from experience replay. The performance of the proposed algorithms, expected n-step DQN (EnDQN) and expected n-step DDQN (EnDDQN), are validated using two simulation environments, CartPole and DeepTraffic. The experimental results demonstrate that the proposed multi-step methods greatly improve the data efficiency of DRL agents while further improving the performance of existing classic DRL algorithms when incorporated into their training.
Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption.
LBPH-based Enhanced Real-Time Face Recognition

Facial recognition has always gone through a consistent research area due to its non-modelling nature and its diverse applications.As a result, day-to-day activities are increasingly being carried out electronically rather than in pencil and paper.Today, computer vision is a comprehensive field that deals with a high level of programming by feeding the input images/videos to automatically perform tasks such as detection, recognition and classification.Even with deep learning techniques, they are better than the normal human visual system.In this article, we developed a facial recognition system based on the Local Binary Pattern Histogram (LBPH) method to treat the real-time recognition of the human face in the low and high-level images.We aspire to maximize the variation that is relevant to facial expression and open edges so to sort of encode edges in a very cheap way.These highly successful features are called the Local Binary Pattern Histogram (LBPH).
SoK

Dynamic binary instrumentation (DBI) techniques allow for monitoring and possibly altering the execution of a running program up to the instruction level granularity. The ease of use and flexibility of DBI primitives has made them popular in a large body of research in different domains, including software security. Lately, the suitability of DBI for security has been questioned in light of transparency concerns from artifacts that popular frameworks introduce in the execution: while they do not perturb benign programs, a dedicated adversary may detect their presence and defeat the analysis. The contributions we provide are two-fold. We first present the abstraction and inner workings of DBI frameworks, how DBI assisted prominent security research works, and alternative solutions. We then dive into the DBI evasion and escape problems, discussing attack surfaces, transparency concerns, and possible mitigations. We make available to the community a library of detection patterns and stopgap measures that could be of interest to DBI users.
An effective refined artificial bee colony algorithm for numerical optimisation

Various complex problems have recently encouraged research and development of different bio-inspired optimisation algorithms, a well-known instance being the artificial bee colony (ABC) algorithm, both due to its simplicity and performance. Building upon the basic algorithm enabled further gains in performance but brought alongside it some specific costs and problems. The improved variants available in the literature often introduce additional user-defined parameters and sometimes completely infringe the algorithm structure. Focusing the search process on exploitation has proven to be a good first step of improvement in most cases, but analysing the effects of this modification on a limited set of standard benchmark functions could lead to a skewed perspective. This paper proposes a novel algorithm based on ABC that keeps the original structure intact, introduces a new solution update equation and an extended scout bee phase focusing the search on more prominent solutions without introducing new control parameters. Based on the conducted experimental analysis, it is able to outperform various competitive algorithms on a large test bed of benchmark functions and several real-world problems. The effects of the particular proposed modifications are also analysed and attention is given to two variants of the standard algorithm.
A clustering approach for detecting implausible observation values in electronic health records data

Identifying implausible clinical observations (e.g., laboratory test and vital sign values) in Electronic Health Record (EHR) data using rule-based procedures is challenging. Anomaly/outlier detection methods can be applied as an alternative algorithmic approach to flagging such implausible values in EHRs. The primary objectives of this research were to develop and test an unsupervised clustering-based anomaly/outlier detection approach for detecting implausible observations in EHR data as an alternative algorithmic solution to the existing procedures. Our approach is built upon two underlying hypotheses that, (i) when there are large number of observations, implausible records should be sparse, and therefore (ii) if these data are clustered properly, clusters with sparse populations should represent implausible observations. To test these hypotheses, we applied an unsupervised clustering algorithm to EHR observation data on 50 laboratory tests from Partners HealthCare. We tested different specifications of the clustering approach and computed confusion matrix indices against a set of silver-standard plausibility thresholds. We compared the results from the proposed approach with conventional anomaly detection (CAD) approaches, including standard deviation and Mahalanobis distance. We found that the clustering approach produced results with exceptional specificity and high sensitivity. Compared with the conventional anomaly detection approaches, our proposed clustering approach resulted in significantly smaller number of false positive cases. Our contributions include (i) a clustering approach for identifying implausible EHR observations, (ii) evidence that implausible observations are sparse in EHR laboratory test results, (iii) a parallel implementation of the clustering approach on i2b2 star schema, and (3) a set of silver-standard plausibility thresholds for 50 laboratory tests that can be used in other studies for validation. The proposed algorithmic solution can augment human decisions to improve data quality. Therefore, a workflow is needed to complement the algorithm's job and initiate necessary actions that need to be taken in order to improve the quality of data.
RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications

The Jacobian matrix (or the gradient for single-output networks) is directly related to many important properties of neural networks, such as the function landscape, stationary points, (local) Lipschitz constants and robustness to adversarial attacks. In this paper, we propose a recursive algorithm, RecurJac, to compute both upper and lower bounds for each element in the Jacobian matrix of a neural network with respect to network’s input, and the network can contain a wide range of activation functions. As a byproduct, we can efficiently obtain a (local) Lipschitz constant, which plays a crucial role in neural network robustness verification, as well as the training stability of GANs. Experiments show that (local) Lipschitz constants produced by our method is of better quality than previous approaches, thus providing better robustness verification results. Our algorithm has polynomial time complexity, and its computation time is reasonable even for relatively large networks. Additionally, we use our bounds of Jacobian matrix to characterize the landscape of the neural network, for example, to determine whether there exist stationary points in a local neighborhood.
A Mean Field Theory of Batch Normalization

We develop a mean field theory for batch normalization in fully-connected feedforward neural networks. In so doing, we provide a precise characterization of signal propagation and gradient backpropagation in wide batch-normalized networks at initialization. Our theory shows that gradient signals grow exponentially in depth and that these exploding gradients cannot be eliminated by tuning the initial weight variances or by adjusting the nonlinear activation function. Indeed, batch normalization itself is the cause of gradient explosion. As a result, vanilla batch-normalized networks without skip connections are not trainable at large depths for common initialization schemes, a prediction that we verify with a variety of empirical simulations. While gradient explosion cannot be eliminated, it can be reduced by tuning the network close to the linear regime, which improves the trainability of deep batch-normalized networks without residual connections. Finally, we investigate the learning dynamics of batch-normalized networks and observe that after a single step of optimization the networks achieve a relatively stable equilibrium in which gradients have dramatically smaller dynamic range. Our theory leverages Laplace, Fourier, and Gegenbauer transforms and we derive new identities that may be of independent interest.
Exploiting Explicit Paths for Multi-hop Reading Comprehension

We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question.Although inspired by multi-hop reasoning over knowledge graphs, our proposed approach operates directly over unstructured text.It generates potential paths through passages and scores them without any direct path supervision.The proposed model, named PathNet, attempts to extract implicit relations from text through entity pair representations, and compose them to encode each path.To capture additional context, Path-Net also composes the passage representations along each path to compute a passage-based representation.Unlike previous approaches, our model is then able to explain its reasoning via these explicit paths through the passages.We show that our approach outperforms prior models on the multi-hop Wikihop dataset, and also can be generalized to apply to the OpenBookQA dataset, matching stateof-the-art performance.
Hand Gesture Recognition Based on Active Ultrasonic Sensing of Smartphone: A Survey

With the rapid development of Internet of Things, hand gesture recognition has drawn wide attention in the field of ubiquitous computing because it provides us with simple and natural human-computer interaction mode.Among these various implementations, hand gesture recognition using ultrasonic signals of smartphone has become a hot research topic due to its various advantages.In this paper, we consider the smartphone as an active sonar sensing system to identify hand movements.Specifically, the speakers emit ultrasonic signal and the microphone on the same phone receives the changed echo affected by hand movements.This paper investigates the state-of-the-art hand gesture applications and presents a comprehensive survey on the characteristics of studies using the active sonar sensing system.Firstly, we review the existing research of hand gesture recognition based on acoustic signals.After that, we introduce the characteristics of ultrasonic signal and describe the fundamental principle of hand gesture recognition.Then, we focus on the typical methods used in these studies and present a detailed analysis on signal generation, feature extraction, preprocessing, and recognition methods.Next, we investigate the state-of-the-art ultrasonic-based applications of hand gesture recognition using smartphone and analyze them in detail from dynamic gesture recognition and hand tracking.Afterwards, we make a discussion about these systems from signal acquisition, signal processing, and performance evaluation to obtain some insight into development of the ultrasonic hand gesture recognition system.Finally, we conclude by discussing the challenges, insight, and open issues involved in hand gesture recognition based on ultrasonic signal of the smartphone.
Code Generation as a Dual Task of Code Summarization

Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development. Various neural network-based approaches are proposed to solve these two tasks separately. However, there exists a specific intuitive correlation between CS and CG, which has not been exploited in previous work. In this paper, we apply the relations between two tasks to improve the performance of both tasks. In other words, exploiting the duality between the two tasks, we propose a dual training framework to train the two tasks simultaneously. In this framework, we consider the dualities on probability and attention weights, and design corresponding regularization terms to constrain the duality. We evaluate our approach on two datasets collected from GitHub, and experimental results show that our dual framework can improve the performance of CS and CG tasks over baselines.
The Proposal of Undersampling Method for Learning from Imbalanced Datasets

Highly imbalanced data, which occurs in many real-world applications, often makes machine-based processing difficult or even impossible. The over- and under-sampling methods help to tackle this issue, however they often have serious shortcomings. In this paper different methods of class balancing, especially those obtained by undersampling, are analyzed. Besides, a new solution is presented. The method is oriented toward finding and thinning clusters of majority class examples. Removing observations from high-density areas can lead to a less loss of information than in the case of removing individual examples or these from less-density areas. Such approach makes the distribution of examples more even. The effectiveness of the method is demonstrated through extensive comparisons to other undersampling methods with the use of eighteen public datasets. The results of experiments show that in many cases the proposed solution allows to achieve better performance than other tested techniques.
Token-based typology and word order entropy: A study based on Universal Dependencies

Abstract The present paper discusses the benefits and challenges of token-based typology, which takes into account the frequencies of words and constructions in language use. This approach makes it possible to introduce new criteria for language classification, which would be difficult or impossible to achieve with the traditional, type-based approach. This point is illustrated by several quantitative studies of word order variation, which can be measured as entropy at different levels of granularity. I argue that this variation can be explained by general functional mechanisms and pressures, which manifest themselves in language use, such as optimization of processing (including avoidance of ambiguity) and grammaticalization of predictable units occurring in chunks. The case studies are based on multilingual corpora, which have been parsed using the Universal Dependencies annotation scheme.
Transformer and seq2seq model for Paraphrase Generation

Paraphrase generation aims to improve the clarity of a sentence by using different wording that convey similar meaning.For better quality of generated paraphrases, we propose a framework that combines the effectiveness of two models -transformer and sequence-tosequence (seq2seq).We design a two-layer stack of encoders.The first layer is a transformer model containing 6 stacked identical layers with multi-head self-attention, while the second-layer is a seq2seq model with gated recurrent units (GRU-RNN).The transformer encoder layer learns to capture long-term dependencies, together with syntactic and semantic properties of the input sentence.This rich vector representation learned by the transformer serves as input to the GRU-RNN encoder responsible for producing the state vector for decoding.Experimental results on two datasets-QUORA and MSCOCO using our framework, produces a new benchmark for paraphrase generation.
LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization

Photovoltaic (PV) output is susceptible to meteorological factors, resulting in intermittency and randomness of power generation.Accurate prediction of PV power output can not only reduce the impact of PV power generation on the grid but also provide a reference for grid dispatching.Therefore, this paper proposes an LSTM-attention-embedding model based on Bayesian optimization to predict the day-ahead PV power output.The statistical features at multiple time scales, combined features, time features and wind speed categorical features are explored for PV related meteorological factors.A deep learning model is constructed based on an LSTM block and an embedding block with the connection of a merge layer.The LSTM block is used to memorize and attend the historical information, and the embedding block is used to encode the categorical features.Then, an output block is used to output the prediction results, and a residual connection is also included in the model to mitigate the gradient transfer.Bayesian optimization is used to select the optimal combined features.The effectiveness of the proposed model is verified on two actual PV power plants in one area of China.The comparative experimental results show that the performance of the proposed model has been significantly improved compared to LSTM neural networks, BPNN, SVR model and persistence model.
Study of 3D Virtual Reality Picture Quality

Virtual Reality (VR) and its applications have attracted significant and increasing attention. However, the requirements of much larger file sizes, different storage formats, and immersive viewing conditions pose significant challenges to the goals of acquiring, transmitting, compressing and displaying high quality VR content. Towards meeting these challenges, it is important to be able to understand the distortions that arise and that can affect the perceived quality of displayed VR content. It is also important to develop ways to automatically predict VR picture quality. Meeting these challenges requires basic tools in the form of large, representative subjective VR quality databases on which VR quality models can be developed and which can be used to benchmark VR quality prediction algorithms. Towards making progress in this direction, here we present the results of an immersive 3D subjective image quality assessment study. In the study, 450 distorted images obtained from 15 pristine 3D VR images modified by 6 types of distortion of varying severities were evaluated by 42 subjects in a controlled VR setting. Both the subject ratings as well as eye tracking data were recorded and made available as part of the new database, in hopes that the relationships between gaze direction and perceived quality might be better understood. We also evaluated several publicly available IQA models on the new database, and also report a statistical evaluation of the performances of the compared IQA models.
Improving exploration and exploitation via a Hyperbolic Gravitational Search Algorithm

Finding the best compromise between exploration and exploitation phases of a search algorithm is a hard task. The Gravitational Search Algorithm (GSA) is an evolutionary algorithm based on the Newton’s universal law of gravitation. Many studies show that GSA suffers from slow exploitation which generates premature convergence. This paper proposes a Hyperbolic Gravitational Search Algorithm (HGSA) able to find an optimal balance between exploration and exploitation. The main contributions of this work are: the definition of suitable hyperbolic acceleration coefficients, the dynamic regulation of the gravitational constant coefficient through hyperbolic function and the definition of a decreasing hyperbolic function for the best agents which attract the other agents. The proposed algorithm is compared with well-known search algorithms on classical benchmark functions, CEC-06 2019 benchmark functions and three-bar truss design problem. The results show that HGSA is better than other algorithms in terms of optimization and convergence performances.