Reviewerly/exHarmony huggingface dataset:
Major development under Gaussian filtering since unscented Kalman filter

Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements. Such problems appear in several branches of science and technology, ranging from target tracking to biomedical monitoring. A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering. The early Gaussian filters used a derivative-based implementation, and suffered from several drawbacks, such as the smoothness requirements of system models and poor stability. A derivative-free numerical approximation-based Gaussian filter, named the unscented Kalman filter ( UKF ) , was introduced in the nineties, which offered several advantages over the derivative-based Gaussian filters. Since the proposition of UKF, derivative-free Gaussian filtering has been a highly active research area. This paper reviews significant developments made under Gaussian filtering since the proposition of UKF. The review is particularly focused on three categories of developments: i ) advancing the numerical approximation methods; ii ) modifying the conventional Gaussian approach to further improve the filtering performance; and iii ) constrained filtering to address the problem of discrete-time formulation of process dynamics. This review highlights the computational aspect of recent developments in all three categories. The performance of various filters are analyzed by simulating them with real-life target tracking problems.
Hybrid Method Using EDMS &amp; Gabor for Shape and Texture

The shape is essential to image formats where the textual image is the best example of the binary image representation. Feature extraction is a fundamental process in pattern recognition. the shape and texture recognition system consists of five major tasks which are involved pre-processing, segmentation, feature extraction, classification and recognition. GENERALLY, less discriminative features in global and local feature approach leads to reduce in recognition rate. By proposing a global and local approach that produces more discriminative features and less dimensionality of data, these problems are overcome. Two feature extraction methods are studied namely Gabor filter and edge direction matrix (EDMS) and combination of two popular feature extraction methods is proposed The proposed method is a combination of Gabor filter and EDMS method which applied to reduce the dimensionality of data. this collaboration aims to make use of the major advantages each one presents, by simultaneously complementing each other, in order to elevate their weak points. By using classifier approaches such as random forest, the proposed combinative descriptor is compared with the state of the art combinative methods based on Gray-Level Co-occurrence matrix and moment invariant on two benchmark dataset MPEG-7 CE-Shape-1, Enghlishfnt. The experiments have shown the superiority of the introduced descriptor over the GLCM moment invariants from the literature.
FedBC: Blockchain-based Decentralized Federated Learning

Federated learning enables participants to collaborate on model training without directly exchanging raw data. Existing federated learning methods often follow the parameter server architecture, using third-party collaborators to provide aggregation and key management. In this case, the central node obtains information uploaded by other nodes. Studies have shown that with this information, the central node can infer important information, which leads to data privacy leakage. In addition, the failure on the server node can also cause the entire system to fail. We designed a completely decentralized federated learning framework based on blockchain, thereby avoiding the privacy and failure risk of the centralized structure. Moreover, we develop the corresponding model training approach. Compared with the existing methods, our framework performs better in terms of accuracy, robustness, and privacy.
SoK: Anatomy of Data Breaches

Abstract We systematize the knowledge on data breaches into concise step-by-step breach workflows and use them to describe the breach methods. We present the most plausible workflows for 10 famous data breaches. We use information from a variety of sources to develop our breach workflows, however, we emphasize that for many data breaches, information about crucial steps was absent. We researched such steps to develop complete breach workflows; as such, our workflows provide descriptions of data breaches that were previously unavailable. For generalizability, we present a general workflow of 50 data breaches from 2015. Based on our data breach analysis, we develop requirements that organizations need to meet to thwart data breaches. We describe what requirements are met by existing security technologies and propose future research directions to thwart data breaches.
The Adapter-Bot: All-In-One Controllable Conversational Model

In this paper, we present the Adapter-Bot, a generative chat-bot that uses a fixed backbone conversational model such as DialGPT (Zhang et al. 2019) and triggers on-demand dialogue skills via different adapters (Houlsby et al. 2019). Each adapter can be trained independently, thus allowing a continual integration of skills without retraining the entire model. Depending on the skills, the model is able to process multiple knowledge types, such as text, tables, and graphs, in a seamless manner. The dialogue skills can be triggered automatically via a dialogue manager, or manually, thus allowing high-level control of the generated responses. At the current stage, we have implemented 12 response styles (e.g., positive, negative etc.), 6 goal-oriented skills (e.g. weather information, movie recommendation, etc.), and personalized and emphatic responses.
Impact of auditory sense on trust and brand affect through auditory social interaction and control

Voice assistants—or voice-enabled artificial intelligence—have changed the way people interact with their surroundings dramatically. Utilizing an enactive view of social cognition theory, this study demonstrates how voice assistants can act as [semi] autonomous agents to hold instantaneous social interactions with consumers. This research employed two experimental studies. Study 1 used two voice assistant mobile applications, Microsoft Cortana and Google Assistant, and Study 2 used Amazon Alexa and Microsoft Cortana. The contributions this paper makes are two-fold. First, the results illustrate how perceived auditory sense drives perceived auditory control through auditory social interactions with a voice assistant that lead to brand affect and consumers' trust in the voice assistant. Second, results shed light on the role of surprise as a repelling drive that attenuates the effect of perceived auditory control on brand affect.
A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets

In this paper, a real-time ASL recognition system was built with a ConvNet algorithm using real colouring images from a PC camera. The model is the first ASL recognition model to categorize a total of 26 letters, including (J &amp; Z), with two new classes for space and delete, which was explored with new datasets. It was built to contain a wide diversity of attributes like different lightings, skin tones, backgrounds, and a wide variety of situations. The experimental results achieved a high accuracy of about 98.53% for the training and 98.84% for the validation. As well, the system displayed a high accuracy for all the datasets when new test data, which had not been used in the training, were introduced.
Entanglement Sustainability in Quantum Radar

In this study, some important parts of a quantum radar are designed using the quantum electrodynamics theory and significantly focused on entanglement conservation. Quantum radar is generally defined as a detection sensor that utilizes the microwave photons like a classical radar and simultaneously employs quantum phenomena to improve detection, identification, and resolution capabilities. However, the entanglement is so fragile, unstable, and difficult to preserve for a long time. Also, more importantly, the entangled states have a tendency to leak away due to the noise. The points mentioned enforces that the entangled states should be carefully studied at each step of the quantum radar detection processes such as the creation of the entangled photons in the tripartite system, the amplification of the photons, the propagation into the atmosphere, and the reflection from the target. At each step, the parameters related to the real mediums and target material can affect the entangled states to leak away easily. The results of simulations indicate that the features of the tripartite system and amplifier are so important to lead the detected photons to remain entangled with the optical modes. Nonetheless, it is found that a lot of entangled photons lose the related non-classical correlation.
A large-scale empirical study on vulnerability distribution within projects and the lessons learned

The number of vulnerabilities increases rapidly in recent years, due to advances in vulnerability discovery solutions. It enables a thorough analysis on the vulnerability distribution and provides support for correlation analysis and prediction of vulnerabilities. Previous research either focuses on analyzing bugs rather than vulnerabilities, or only studies general vulnerability distribution among projects rather than the distribution within each project. In this paper, we collected a large vulnerability dataset, consisting of all known vulnerabilities associated with five representative open source projects, by utilizing automated crawlers and spending months of manual efforts. We then analyzed the vulnerability distribution within each project over four dimensions, including files, functions, vulnerability types and responsible developers. Based on the results analysis, we presented 12 practical insights on the distribution of vulnerabilities. Finally, we applied such insights on several vulnerability discovery solutions (including static analysis and dynamic fuzzing), and helped them find 10 zero-day vulnerabilities in target projects, showing that our insights are useful.
Prediction of Factors That Affect the Knowledge and Use Higher Education Professors from Spain Make of ICT Resources to Teach, Evaluate and Research: A Study with Research Methods in Educational Technology

Due to the rise of new technologies, further development of digital competence by professors is necessary. The aim of this paper is to know the pedagogical digital competence of lecturers in Spain, focused on a model called PDC-TER: ICT resources to Teach, to Evaluate and to Research; as well as to predict those variables that affect the level of this competence through different regressions (Multiple Linear Regression, MLR). A study has been carried out in the Spanish territory, with a total of 867 lecturers. For it, an ex post facto non-probabilistic study based on the survey technique has been proposed. The results show that the professors have an average level of digital pedagogical competence, while in the 2.0 tools for teaching and research dimensions, it has a medium high level of competence, as well as a medium level in relation to the use of tools 2.0 for the evaluation. Furthermore, one of the variables predictive of the level of digital competence in the three dimensions of the PDC-TER model, is the number of research and innovation projects in which they have participated. These data highlight the need to improve the digital competence of professors in order to meet the demands of the qualified professions of the future, and therefore, prepare students for it.
Lightweight Stacked Hourglass Network for Human Pose Estimation

Human pose estimation is a problem that continues to be one of the greatest challenges in the field of computer vision. While the stacked structure of an hourglass network has enabled substantial progress in human pose estimation and key-point detection areas, it is largely used as a backbone network. However, it also requires a relatively large number of parameters and high computational capacity due to the characteristics of its stacked structure. Accordingly, the present work proposes a more lightweight version of the hourglass network, which also improves the human pose estimation performance. The new hourglass network architecture utilizes several additional skip connections, which improve performance with minimal modifications while still maintaining the number of parameters in the network. Additionally, the size of the convolutional receptive field has a decisive effect in learning to detect features of the full human body. Therefore, we propose a multidilated light residual block, which expands the convolutional receptive field while also reducing the computational load. The proposed residual block is also invariant in scale when using multiple dilations. The well-known MPII and LSP human pose datasets were used to evaluate the performance using the proposed method. A variety of experiments were conducted that confirm that our method is more efficient compared to current state-of-the-art hourglass weight-reduction methods.
TGCN: Tag Graph Convolutional Network for Tag-Aware Recommendation

Tag-aware recommender systems (TRS) utilize rich tagging records to better depict user portraits and item features. Recently, many efforts have been done to improve TRS with neural networks. However, these solutions rustically rely on the tag-based features for recommendation, which is insufficient to ease the sparsity, ambiguity and redundancy issues introduced by tags, thus hindering the recommendation performance. In this paper, we propose a novel tag-aware recommendation model named Tag Graph Convolutional Network (TGCN), which leverages the contextual semantics of multi-hop neighbors in the user-tag-item graph to alleviate the above issues. Specifically, TGCN first employs type-aware neighbor sampling and aggregation operation to learn the type-specific neighborhood representations. Then we leverage attention mechanism to discriminate the importance of different node types and creatively employ Convolutional Neural Network (CNN) as type-level aggregator to perform vertical and horizontal convolutions for modeling multi-granular feature interactions. Besides, a TransTag regularization function is proposed to accurately identify user's substantive preference. Extensive experiments on three public datasets and a real industrial dataset show that TGCN significantly outperforms state-of-the-art baselines for tag-aware top-N recommendation.
An efficient and secure data sharing scheme for mobile devices in cloud computing

Abstract With the development of big data and cloud computing, more and more enterprises prefer to store their data in cloud and share the data among their authorized employees efficiently and securely. So far, many different data sharing schemes in different fields have been proposed. However, sharing sensitive data in cloud still faces some challenges such as achieving data privacy and lightweight operations at resource constrained mobile terminals. Furthermore, most data sharing schemes have no integrity verification mechanism, which would result in wrong computation results for users. To solve the problems, we propose an efficient and secure data sharing scheme for mobile devices in cloud computing. Firstly, the scheme guarantees security and authorized access of shared sensitive data. Secondly, the scheme realizes efficient integrity verification before users share the data to avoid incorrect computation. Finally, the scheme achieves lightweight operations of mobile terminals on both data owner and data requester sides.
Developing an Unsupervised Real-Time Anomaly Detection Scheme for Time Series With Multi-Seasonality

On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. Firstly, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Secondly, a large portion of time series data have complex seasonality features. Thirdly, on-line anomaly detection needs to be fast and reliable. In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and an efficient detection algorithm that computes LTI in a real-time manner and scores each data point robustly in terms of its probability of being anomalous. We have conducted extensive experimentation to evaluate our algorithm with several datasets from both public repositories and production environments. The experimental results show that our scheme outperforms existing representative anomaly detection algorithms in terms of the commonly used metric, Area Under Curve (AUC), while achieving the desired efficiency.
Evolving Nonlinear S-Boxes With Improved Theoretical Resilience to Power Attacks

Substitution boxes are the main nonlinear component of block ciphers. The security of these ciphers against linear, differential, or side-channel attacks is dependent on the design of such component and their intrinsic properties. There are several methods that aim to cryptographically define, generate, or search for strong substitution boxes. The application of combinatorial optimization algorithms is one of the most useful methodologies in this research area. In this article, we present a novel hybrid method based on the Leaders and Followers and hill-climbing over Hamming Weight Classes metaheuristics, coupled with a new trade-off fitness function that generates 8-bit bijective substitution boxes with good resisting properties towards classical cryptanalysis and side-channel attacks by power consumption. We address the best Pareto optimal solutions for the multi-objective optimization of non-linearity and confusion coefficient variance.
Advancing AI for Earth Science: A Data Systems Perspective

Tackling data challenges and incorporating physics into machine learning models will help unlock the potential of artificial intelligence to answer Earth science questions.
Classically simulating near-term partially-distinguishable and lossy boson sampling

Boson Sampling is the problem of sampling from the same distribution as indistinguishable single photons at the output of a linear optical interferometer. It is an example of a non-universal quantum computation which is believed to be feasible in the near term and cannot be simulated on a classical machine. Like all purported demonstrations of "quantum supremacy", this motivates optimizing classical simulation schemes for a realistic model of the problem, in this case Boson Sampling when the implementations experience lost or distinguishable photons. Although current simulation schemes for sufficiently imperfect boson sampling are classically efficient, in principle the polynomial runtime can be infeasibly large. In this work, we develop a scheme for classical simulation of Boson Sampling under uniform distinguishability and loss, based on the idea of sampling from distributions where at most k photons are indistinguishable. We show that asymptotically this scheme can provide a polynomial improvement in the runtime compared to classically simulating idealised Boson Sampling. More significantly, we show that in the regime considered experimentally relevant, our approach gives an substantial improvement in runtime over other classical simulation approaches.
AxCell: Automatic Extraction of Results from Machine Learning Papers

Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers.In this paper, we present AXCELL, an automatic machine learning pipeline for extracting results from papers.AXCELL uses several novel components, including a table segmentation subtask, to learn relevant structural knowledge that aids extraction.When compared with existing methods, our approach significantly improves the state of the art for results extraction.We also release a structured, annotated dataset for training models for results extraction, and a dataset for evaluating the performance of models on this task.Lastly, we show the viability of our approach enables it to be used for semi-automated results extraction in production, suggesting our improvements make this task practically viable for the first time.Code is available on GitHub.
Breast cancer prediction model with decision tree and adaptive boosting

In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.
High-accuracy 3D surface measurement using hybrid multi-frequency composite-pattern temporal phase unwrapping

Multi-frequency temporal phase unwrapping (TPU) has been extensively used in phase-shifting profilometry (PSP) for the high-accuracy measurement of objects with surface discontinuities and isolated objects. However, a large number of fringe patterns are commonly required. To reduce the number of required patterns, a new hybrid multi-frequency composite-pattern TPU method was developed using fewer patterns than conventional TPU. The new method combines a unit-frequency ramp pattern with three low-frequency phase-shifted fringe patterns to form three composite patterns. These composite patterns are used together with three high-frequency phase-shifted fringe patterns to generate a high-accuracy phase map. The optimal high frequency to achieve high measurement accuracy and reliable phase unwrapping is determined by analyzing the effect of temporal intensity noise on phase error. Experimental results demonstrated that new grayscale hybrid and color hybrid multi-frequency composite-pattern TPU methods can achieve a high-accuracy measurement using only six and three images, respectively.