Reviewerly/exHarmony huggingface dataset:
Cross-Age Identity Difference Analysis Model Based on Image Pairs for Age Invariant Face Verification

Face recognition (FR) is a widely studied topic in the field of computer vision research. Although promising results are achieved, FR researches still face challenges of age variations. Most existing FR networks conduct classification based on the feature similarity, which may be misled by large intra class difference under age variations. Instead of calculating feature similarity, in this paper, we derive a novel cross-age face verification framework named Cross-Age Identity Difference Analysis (CIDA) model, which analyzes the identity difference between image pairs under age variations. Specifically, our framework includes two cascading networks. Firstly, an Identity Difference Feature Extractor (IDFE) is proposed to extract the difference information between the input image pair, where the identity discriminant features are effectively extracted, while other interference factors such as age, illumination, posture are suppressed. Secondly, the Direct Cross-age Verification Network (DCVN) is proposed to directly decide whether the input image pair is from the same individual. We derive a novel loss function, where the classification loss with larger age difference is assigned larger weights, which urges the classifier to pay attention to the classification process of the samples with large age gap. Besides, the loss of DCVN are integrated with the loss function of IDFE as a feedback of the final classification performance, improving the discriminant power of IDFE. Through synchronous training of the two networks, we can finally achieve end-to-end network architecture. Compared with the existing cross-age face recognition (CAFR) methods, we do not need to consider feature similarity comparison, which provides a new insight for cross-age face recognition task. Extensive experiments have been performed on the benchmark CAFR datasets which verify the effectiveness of our model.
Debiasing Item-to-Item Recommendations With Small Annotated Datasets

Item-to-item recommendation (e.g., “People who like this also like...”) is a ubiquitous and important type of recommendation in real-world systems. Observational data from historical interaction logs abound in these settings. However, since virtually all observational data exhibit biases, such as time-in-inventory or interface biases, it is crucial that recommender algorithms account for these biases. In this paper, we develop a principled approach for item-to-item recommendation based on causal inference and present a practical and highly effective method for estimating the causal parameters from a small annotated dataset. Empirically, we find that our approach substantially improves upon existing methods while requiring only small amounts of annotated data.
Comparison of activation function on extreme learning machine (ELM) performance for classifying the active compound

The active compound could interact with other molecules and can lead to a variety of positive and negative effects on the living system. Therefore, classification of the compound is very important for understanding its character and functions for human medicine. The manual classification of compound functions by laboratory test is time and cost consuming. Based on the previous research, the functions of the compound could be predicted based on its molecular structure in the format of the Simplified Molecular Input Line Entry System (SMILES). So, we employed the Extreme Learning Machine (ELM) for classifying the active compounds according to its SMILES structure. The result of this study suggested that ELM could classify the active compounds very fast and guarantee optimal performance. The accuracy and computational time of classification model were depending on the activation function. This experiment uses eleven activation functions i.e Binary Step Function, Sigmoid, Swish, Exponential Linear Squashing (ELiSH ), Hyperbolic Tangent(TanH), Hard Hiperbolic Function (HardTanH), Rectified Linear Unit (ReLU), TanhRe, Exponential Linier Units (ELUs), SoftPlus, and Leaky ReLU (LReLU). The results of experiments show that ELU and TanHRe have the best performance based on average and maximal accuracy. Accuracy of the system depends on the patterns in class and the activation functions which are used. Based on experimental results, the average accuracy can reach 80.56% on ELUs activation function and the maximum accuracy 88.73% on TanHRe.
EKENet: Efficient knowledge enhanced network for real-time scene parsing

Scene parsing is essential for many high-level AI applications, such as intelligent vehicles and traffic surveillance. In this work, we propose a highly efficient and powerful deep convolutional neural network, namely Efficient Knowledge Enhanced Network (EKENet), for parsing scenes in real-time. Unlike most existing approaches that compromise efficiency for the sake of high accuracy, EKENet achieves an ideal trade-off between the two. Our EKENet is built upon a novel building block, namely Efficient Dual Abstraction (EDA) block, which employs an efficiently parallel convolution structure for extracting spatial features and modeling cross-channel correlations in a dual fashion. Additionally, a novel light-weight Encoding-Enhancing (EE) module is designed to enhance our EKENet, which can efficiently encode high-level knowledge extracted from top layers to guide the learning of low-level features from bottom layers. Extensive experiments on challenging benchmarks, Cityscapes and CamVid datasets, demonstrate that EKENet achieves the new state-of-the-art performance in terms of speed and accuracy tradeoff.
Application of Artificial Intelligence in Renewable Energy

Recent shift towards renewable energy resources has increased research for addressing shortcomings of these energy resources. As major issues are related to intermittency and uncertainty of renewable supply, new technologies like artificial intelligence and machine learning offers lot of opportunity to address these issues as they are basically meant for processing of uncertain data. This paper analyses application of machine learning in different areas of renewable energy system like forecasting where machine learning is used to build accurate models, maximum power point tracking where machine learning provides robust and smooth control which is not much susceptible to noise in input, inverter where machine learning can be used to provide high quality power without fluctuation even when input is intermittent. Even though machine learning has many prospects which can be used to address different issues associated with renewable system, whether to employ it as effective solution to problem for given system or not depends on host of factors. This paper analyses all these issues and present a methodical exploration of applications of machine learning, its advantages and challenges in hybrid renewable energy system.
Classification of tectonic and non-tectonic seismicity based on convolutional neural network

SUMMARY In this paper, convolutional neural networks (CNNs) were used to distinguish between tectonic and non-tectonic seismicity. The proposed CNNs consisted of seven convolutional layers with small kernels and one fully connected layer, which only relied on the acoustic waveform without extracting features manually. For a single station, the accuracy of the model was 0.90, and the event accuracy could reach 0.93. The proposed model was tested using data from January 2019 to August 2019 in China. The event accuracy could reach 0.92, showing that the proposed model could distinguish between tectonic and non-tectonic seismicity.
DyeFreeNet: Deep Virtual Contrast CT Synthesis

To highlight structures such as blood vessels and tissues for clinical diagnosis, veins are often infused with contrast agents to obtain contrast-enhanced CT scans. In this paper, the use of a deep learning-based framework, DyeFreeNet, to generate virtual contrast abdominal and pelvic CT images based on the original non-contrast CT images is presented. First, to solve the overfitting issue for a deep learning-based method on small datasets, a pretrained model is obtained through a novel self-supervised feature learning network, whereby the network extracted intensity features from a large-scale, publicly available dataset without the use of annotations and classified four transformed intensity levels. Second, an enhanced high-resolution "primary learning generative adversarial network (GAN)" is then used to learn intensity variations between contrast and non-contrast CT images as well as retain high-resolution representations to yield virtual contrast CT images. Then, to reduce GAN training instability, an "intensity refinement GAN" using a novel cascade intensity refinement strategy is applied to obtain more detailed and accurate intensity variations to yield the final predicted virtual contrast CT images. The generated virtual contrast CTs by the proposed framework directly from non-contrast CTs are quite realistic with the virtual enhancement of the major arterial structures. To the best of our knowledge, this is the first work to synthesize virtual contrast-enhanced abdominal and pelvic CT images from non-contrast CT scans.
Blind Image Zero-Watermarking Algorithm Based on Radial Krawtchouk Moments and Chaotic System

Image watermarking systems are frequently used tools for copyright protection against unauthorized use of images in unsecured spaces. The conventional method is to embed a copyright mark (watermark) in the original image. However, this strategy is not suitable for sensitive images such as medical, satellite, texture and remote sensing images, etc., because the integrated watermark strongly affects the results of its application tasks. For copyright protection of this type of images, this paper proposes a robust blind zero-watermarking algorithm based on Krawtchouk Radial Moments and a chaotic system. This algorithm does not integrate any information into the original image and satisfactorily ensures robustness against various common image processing attacks and geometric distortions. The experimental study uses different categories of images to evaluate and compare the proposed algorithm with other watermarking and zero-watermarking algorithms in terms of robustness against various image attacks.
An Improved Method to Recognize Hand-over-Face Gesture based Facial Emotion using Convolutional Neural Network

An occlusion on face is typically considered as a major obstacle in recognizing emotion through facial expression. However, the hand-over-face occlusion provides useful information to recognize emotion precisely. The existing emotion recognition methods with or without hand gesture identify basic emotions such as happy, sad, angry, surprise, neutral, fear and disgust. However, additional emotions such as confident, making decision, scared, ashamed, angry and ok sign need to be identified for modern applications. In this paper, we present an empirical evaluation of an improved Hand-over-Face Gesture based Facial Emotion Recognition Method (HFG_FERM) that identifies additional unexplored emotions along with basic emotions. The proposed method includes extensive coding schema with additional hand signs to identify unexplored emotions. Additionally, it integrates convolution neural network in its design for deep and automatic feature extraction as well as for classification of emotions. The performance of HFG_FERM is evaluated under four distinct scenarios. The experimental results show that the HFG_FERM considerably increases emotion recognition accuracy.
Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More

Abstract Answer-Set Programming (ASP) is a powerful and expressive knowledge representation paradigm with a significant number of applications in logic-based AI. The traditional ground-and-solve approach, however, requires ASP programs to be grounded upfront and thus suffers from the so-called grounding bottleneck (i.e., ASP programs easily exhaust all available memory and thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been developed, but many state-of-the-art techniques for grounded ASP solving have not been available to them yet. In this work we present, for the first time, adaptions to the lazy-grounding setting for many important techniques, like restarts, phase saving, domain-independent heuristics, and learned-clause deletion. Furthermore, we investigate their effects and in general observe a large improvement in solving capabilities and also uncover negative effects in certain cases, indicating the need for portfolio solving as known from other solvers.
On the phase transition of Wilks’ phenomenon

Summary Wilks’ theorem, which offers universal chi-squared approximations for likelihood ratio tests, is widely used in many scientific hypothesis testing problems. For modern datasets with increasing dimension, researchers have found that the conventional Wilks’ phenomenon of the likelihood ratio test statistic often fails. Although new approximations have been proposed in high-dimensional settings, there still lacks a clear statistical guideline regarding how to choose between the conventional and newly proposed approximations, especially for moderate-dimensional data. To address this issue, we develop the necessary and sufficient phase transition conditions for Wilks’ phenomenon under popular tests on multivariate mean and covariance structures. Moreover, we provide an in-depth analysis of the accuracy of chi-squared approximations by deriving their asymptotic biases. These results may provide helpful insights into the use of chi-squared approximations in scientific practices.
Blockchain in the Internet of Things: Architectures and Implementation

The world is becoming more interconnected every day.With the high technological evolution and the increasing deployment of it in our society, scenarios based on the Internet of Things (IoT) can be considered a reality nowadays.However, and before some predictions become true (around 75 billion devices are expected to be interconnected in the next few years), many efforts must be carried out in terms of scalability and security.In this study we propose and evaluate a new approach based on the incorporation of Blockchain into current IoT scenarios.The main contributions of this study are as follows: i) an indepth analysis of the different possibilities for the integration of Blockchain into IoT scenarios, focusing on the limited processing capabilities and storage space of most IoT devices, and the economic cost and performance of current Blockchain technologies; ii) a new method based on a novel module named BIoT Gateway that allows both unidirectional and bidirectional communications with IoT devices on real scenarios, allowing to exchange any kind of data; and iii) the proposed method has been fully implemented and validated on two different real-life IoT scenarios, extracting very interesting findings in terms of economic cost and execution time.The source code of our implementation is publicly available in the Ethereum testnet.
Aircraft Seam Feature Extraction from 3D Raw Point Cloud via Hierarchical Multi-structure Fitting

Advanced 3D scanners and depth cameras facilitate aircraft skin assembly, by acquiring high-precision point clouds. The measurement of skin seam quality directly affects the aerodynamic performance of aircraft. The extraction of the seam feature is an essential process in aircraft assembly. We propose a robust seam point extraction approach from raw point clouds of aircraft skins. Motivated by the non-uniform distribution of the acquired point cloud, we first devise a new tensor voting algorithm to exclude most of the points not belonging to seam structures. Our main contribution is the design of a hierarchical multi-structure fitting algorithm to classify the extracted points into non-seam points and seam points. We design the line constraint and the misjudgment strategy to cope with the seam feature extraction. In addition, we show that our approach can be applied into the gap-and-flush measurement (GAFM) of skin seams: it avoids the error of fitting two close lines into one line, which is a common and arduous case for the state of the arts. A number of experiments demonstrate the effectiveness and reliability of our approach on both synthetic and real-world raw data.
An Institutional Repository in a Developing Country: security and Ethical Encounters at the Bindura University of Science Education, Zimbabwe

The article explores the recent experiences encountered by the Bindura University of Science Education (BUSE) Library, Zimbabwe in the daily operations of its institutional repository (IR) with emphasis on security and ethical issues. It examines the present scenario in the management of the IR. BUSE Library set up an IR to archive and enable access to the university’s scholarly research output. The account profiles the recent challenges encountered by the library as it manages its institutional repository. There are legal and ethical issues undermining the security and ethical use of the BUSE IR, for example, cyber threats and unauthorised access. The poor performance of the network firewall has exposed the IR server to cyber-attacks. The ethical challenges relate to plagiarism and predatory publishers who prey on researchers whose work is hosted on the IR. The article recommends measures to enhance security and overcome challenges in the management of the IR.
Asymptotic distribution and detection thresholds for two-sample tests based on geometric graphs

In this paper, we consider the problem of testing the equality of two multivariate distributions based on geometric graphs constructed using the interpoint distances between the observations. These include the tests based on the minimum spanning tree and the $K$-nearest neighbor (NN) graphs, among others. These tests are asymptotically distribution-free, universally consistent and computationally efficient, making them particularly useful in modern applications. However, very little is known about the power properties of these tests. In this paper, using the theory of stabilizing geometric graphs, we derive the asymptotic distribution of these tests under general alternatives, in the Poissonized setting. Using this, the detection threshold and the limiting local power of the test based on the $K$-NN graph are obtained, where interesting exponents depending on dimension emerge. This provides a way to compare and justify the performance of these tests in different examples.
Outlier data mining model for sports data analysis

The results of data mining can be used to predict the physical health status of sports athletes and college sports students and provide physical fitness warnings, so that students can pay attention to physical health status and adjust their physical exercise status. Discrete Morse theory, as a powerful optimization theory, plays a big role in algorithm optimization. This paper combines data mining and discrete Morse theory to propose a grid clustering algorithm based on discrete Morse theory. Moreover, according to the theorem that the cell complex reaches the optimum when it has the smallest possible critical point, this study applies the concept of critical points in the discrete Morse theory to optimize the grid clustering process to obtain clustering results. In addition, this study uses the improved C4.5 algorithm to analyze the physical fitness assessment results and obtains a valuable analysis of the physical fitness assessment results.
Conversational Intelligence Challenge: Accelerating Research with Crowd Science and Open Source

Development of conversational systems is one of the most challenging tasks in natural language processing, and it is especially hard in the case of open‐domain dialogue. The main factors that hinder progress in this area are lack of training data and difficulty of automatic evaluation. Thus, to reliably evaluate the quality of such models, one needs to resort to time‐consuming and expensive human evaluation. We tackle these problems by organizing the Conversational Intelligence Challenge (ConvAI) — open competition of dialogue systems. Our goals are threefold: to work out a good design for human evaluation of open‐domain dialogue, to grow open‐source code base for conversational systems, and to harvest and publish new datasets. Over the course of ConvAI1 and ConvAI2 competitions, we developed a framework for evaluation of chatbots in messaging platforms and used it to evaluate over 30 dialogue systems in two conversational tasks — discussion of short text snippets from Wikipedia and personalized small talk. These large‐scale evaluation experiments were performed by recruiting volunteers as well as paid workers. As a result, we succeeded in collecting a dataset of around 5,000 long meaningful human‐to‐bot dialogues and got many insights into the organization of human evaluation. This dataset can be used to train an automatic evaluation model or to improve the quality of dialogue systems. Our analysis of ConvAI1 and ConvAI2 competitions shows that the future work in this area should be centered around the more active participation of volunteers in the assessment of dialogue systems. To achieve that, we plan to make the evaluation setup more engaging.
Design and Development of AD-CGAN: Conditional Generative Adversarial Networks for Anomaly Detection

Whether in the realm of software or hardware, datasets representing the state of systems are mostly imbalanced.This imbalance is because these systems' reliability requirements make the occurrence of an anomaly a rare phenomenon.Hence, most datasets on anomaly detection have a relatively small percentage that captures the anomaly.Recently, generative adversarial networks (GAN) have shown promising results in image generation tasks.Therefore, in this research work, we build on conditional GANs (CGAN) to generate plausible distributions of a given profile to solve the challenge of data imbalance in anomaly detection tasks and present a novel framework for anomaly detection.Firstly, we learn the pattern of the minority class data samples using a single class CGAN.Secondly, we use the knowledge base of the single class CGAN to generate samples that augment the minority class samples so that a binary class CGAN can train on the typical and malicious profiles with a balanced dataset.This approach inherently eliminates the bias imposed on algorithms from the dataset and results in a robust framework with improved generalization.Thirdly, the binary class CGAN generates a knowledge base that we use to construct the cluster-based anomaly detector.During testing, we do not use the single class CGAN, thereby providing us with a lean and efficient algorithm for anomaly detection that can do anomaly detection on semi-supervised and non-parametric multivariate data.We test the framework on logs and image-based anomaly detection datasets with class imbalance.We compare the performance of AD-CGAN with GAN-derived and non-GAN-derived state of the art algorithms on benchmark datasets.AD-CGAN outperforms most of the algorithms in the standard metrics of Precision, Recall, and F-1 Score.Where AD-CGAN does not perform better in the parameters used, it has the advantage of being lightweight.Therefore, it can be deployed for both online and offline anomaly detection tasks since it does not use an input sample inversion strategy.