Reviewerly/exHarmony huggingface dataset:
WinoNN: Optimizing FPGA-Based Convolutional Neural Network Accelerators Using Sparse Winograd Algorithm

In recent years, a variety of accelerators on FPGAs have been proposed to speed up the convolutional neural network (CNN) in many domain-specific application fields. Besides, some optimization algorithms, such as fast algorithms and network sparsity, have greatly reduced the theoretical computational workload of CNN inference. There are currently a few accelerators on FPGAs that support both the fast Winograd algorithm (WinoA) and network sparsity to minimize the amount of computation. However, on the one hand, these architectures feed data into processing elements (PEs) in units of blocks, some boundary losses caused by sparse irregularities cannot be avoided. On the other hand, these works have not discussed the design space exploration under the sparse condition. In this article, we propose a novel accelerator called WINONN. We fully discuss the challenges faced by supporting WinoA, weight sparsity, and activation sparsity simultaneously. To minimize the online encoding overhead caused by activation sparsity, an efficient encoding format called multibit mask (MBM) is proposed. To handle the irregularities of sparse data, we proposed a novel Scatter-Compute-Gather method in hardware design, combined with a freely sliding buffer to achieve fine-grained data loading to minimize the boundary waste. Finally, we combine a theoretical analysis and experimental method to explore the design space, allowing WINONN to get the best performance on a specific FPGA. Our high scalability design enables us to deploy sparse Winograd accelerators on very small embedded FPGAs, which is not supported in previous works. The experimental results on VGG16 show that we achieve the highest digital signal processing unit (DSP) efficiency and highest energy efficiency compared with the state-of-the-art sparse architectures.
Structure-Aware Human-Action Generation

Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence. Most existing methods borrow ideas from video generation, which naively treat skeleton nodes/joints as pixels of images without considering the rich inter-frame and intra-frame structure information, leading to potential distorted actions. Graph convolutional networks (GCNs) is a promising way to leverage structure information to learn structure representations. However, directly adopting GCNs to tackle such continuous action sequences both in spatial and temporal spaces is challenging as the action graph could be huge. To overcome this issue, we propose a variant of GCNs (SA-GCNs) to leverage the powerful self-attention mechanism to adaptively sparsify a complete action graph in the temporal space. Our method could dynamically attend to important past frames and construct a sparse graph to apply in the GCN framework, well-capturing the structure information in action sequences. Extensive experimental results demonstrate the superiority of our method on two standard human action datasets compared with existing methods.
Incels online reframing sexual violence

This small-scale study brings attention to the way the news media cover the enabling role of computer technology and social media in coalescing online communities focused on the hatred of women and the promotion of violence. While that aggression is ultimately and typically carried out against both women and men, it is specifically misogyny which underlies it. The way that the broader public receives information about incels – involuntary celebates with misogynistic and often violent tendencies – and their atrocities is through the news media, both traditional and internet. This study complements related research on misogynistic language used by social media sites like Reddit by examining the news media's language in coverage of incels' behavior associated with sexual aggression. The research applies qualitative procedures (textual analysis) and a critical feminist framework to identify the gender relations associated with incels online in news for the years 2018 and 2019.
Software-related Slack Chats with Disentangled Conversations

More than ever, developers are participating in public chat communities to ask and answer software development questions. With over ten million daily active users, Slack is one of the most popular chat platforms, hosting many active channels focused on software development technologies, e.g., python, react. Prior studies have shown that public Slack chat transcripts contain valuable information, which could provide support for improving automatic software maintenance tools or help researchers understand developer struggles or concerns.
Automatic and Robust Object Detection in X-Ray Baggage Inspection Using Deep Convolutional Neural Networks

For the purpose of ensuring public security, automatic inspection of X-ray scanners has been deployed at the entry points of many public places to detect dangerous objects. However, current surveillance systems cannot function without human supervision and intervention. In this article, we propose an effective method using deep convolutional neural networks to detect objects during X-ray baggage inspection. As a first step, a large amount of training data is generated by a specific data augmentation technique. Second, a feature enhancement module is used to improve feature extraction capabilities. Then, in order to address the foreground–background imbalance in the region proposal network, focal loss is adopted. Third, the multiscale fused region of interest is utilized to obtain more robust proposals. Finally, soft nonmaximum suppression is adopted to alleviate overlaps in baggage detection. As compared with existing algorithms, the proposed method proves that it is more accurate and robust when dealing with densely cluttered backgrounds during X-ray baggage inspection.
Applicability and Interpretability of Ward’s Hierarchical Agglomerative Clustering With or Without Contiguity Constraints

Hierarchical agglomerative clustering (HAC) with Ward’s linkage has been widely used since its introduction by Ward (Journal of the American Statistical Association, 58(301), 236–244, 1963). This article reviews extensions of HAC to various input data and contiguity-constrained HAC, and provides applicability conditions. In addition, different versions of the graphical representation of the results as a dendrogram are also presented and their properties are clarified. We clarify and complete the results already available in an heterogeneous literature using a uniform background. In particular, this study reveals an important distinction between a consistency property of the dendrogram and the absence of crossover within it. Finally, a simulation study shows that the constrained version of HAC can sometimes provide more relevant results than its unconstrained version despite the fact that the constraint leads to optimize the objective criterion on a reduced set of solutions at each step. Overall, this article provides comprehensive recommendations, both for the use of HAC and constrained HAC depending on the input data, and for the representation of the results.
Intelligent information processing for language education: The use of artificial intelligence in language learning apps

This paper attempts to bring new ideas regarding the use of artificial intelligence in language learning apps. Artificial intelligence has seen a dramatic increase and its utilization in various aspects of data science, information science and various platforms is unprecedented. Its ubiquitousness is evident in smart cities, in online marketing, and data mining, however, its utilization in language learning apps is still somehow neglected. There are a few questions, such as why it is so, etc., this paper attempts to address. The research focuses on several most used language learning apps and the presence of artificial intelligence in them. The findings of the paper are as follows: basically none of the analysed apps uses any kind of machine learning, artificial intelligence or deep learning, and they are mostly based on predefined algorithms that do not utilize the full potential of the computational power we have currently available. The paper also suggests possible solutions and brings practical advice on how to implement artificial intelligence in these apps. The paper is important for any education innovation at the beginning of the 21 century. Without his innovative approach, education will lack sustainability and competitiveness, which will present a serious threat to the whole educational system.
R4 Det: Refined single-stage detector with feature recursion and refinement for rotating object detection in aerial images

The detection of objects with multi-orientations and multi-scales in aerial images is receiving increasing attention because of numerous useful applications in computer vision, image understanding, satellite remote sensing and surveillance. However, such detection can be exceedingly challenging because of a birds eye view, multi-scale rotating objects with large aspect ratios, dense distributions and extremely imbalanced categories. Despite the considerable progress that has been made, detection performance falls considerably below that required for real-world applications. In this paper, we propose an accurate and fast end-to-end detector to address the aforementioned challenges. Our contributions are threefold. First, inspired by the looking and thinking twice mechanism, recursive neural networks and the DetectoRS detector, we propose a novel encoder-decoder based architecture by introducing the recursive feature pyramid into a single-stage object detection framework. The improved backbone network can generate increasingly powerful multi-scale representations for classification and regression. Second, we propose a refined single-stage detector with feature recursion and refinement for rotating objects. Third, we use instance balance to improve focal loss, thereby optimizing the loss in the correct direction. Extensive experiments on two challenging aerial image object detection public datasets, DOTA and HRSC2016, show that the proposed R4Det detector achieves the state-of-the-art accuracy while running very fast. Moreover, further experiments show that our detector is more robust to adversarial image patch attacks than the previous state-of-art detector.
Short term forecasting of solar radiation and power output of 89.6kWp solar PV power plant

Short term forecasting of solar radiation is useful for power plant operations, grid balancing, real-time unit dispatching, automatic generation control and trading. Solar forecasting is an essential tool in solar PV power plant to improve quality of energy delivery to the grid and to reduce weather dependent ancillary costs. In this paper short term forecasting of solar radiation and power output of 89.6 kWp solar PV power plant has been conducted. A new model has been proposed to conduct short term solar forecasting for different days of the year at Amity University Haryana (AUH) campus (28.4595°N, 77.0266°E) located in the northern region of India whereas auto-regressive integrated moving average (ARIMA) model is applied to forecast power output from the solar PV power plant. Root mean square error (RMSE) and Forecast Score (FS) has been used to for accessing the quality of forecasting models. The proposed model for prediction of solar radiation on tilted surface is simple and has very high accuracy. The model has ability to incorporate uncertainty due to environmental conditions. The proposed model is compared with Smart Persistence model and ARIMA model and it has been observed that it has better RMSE and Forecast Score than both Smart Persistence model and ARIMA model for both 15 min and 30 min time horizon. ARIMA model provides a reliable forecast for both solar radiation and solar PV power output. It is flexible enough to accept more information and its performance improves with the increase in number of data points.
Reservoir Computing with Neuromemristive Nanowire Networks

We present simulations based on a model of self- assembled nanowire networks with memristive junctions and neural network-like topology. We analyze the dynamical voltage distribution in response to an applied bias and explain the network conductance fluctuations observed in our previous experimental studies. We then demonstrate the potential of neuromorphic nanowire networks as a physical reservoir by performing benchmark reservoir computing tasks. The tasks include sine wave nonlinear transformation, sine wave auto- generation and forecasting the Mackey-Glass chaotic time series.
Reactive Temporal Logic Planning for Multiple Robots in Unknown Environments

This paper proposes a new reactive mission planning algorithm for multiple robots that operate in unknown environments. The robots are equipped with individual sensors that allow them to collectively learn and continuously update a map of the unknown environment. The goal of the robots is to accomplish complex tasks, captured by global co-safe Linear Temporal Logic (LTL) formulas. The majority of existing temporal logic planning approaches rely on discrete abstractions of the robot dynamics operating in known environments and, as a result, they cannot be applied to the more realistic scenarios where the environment is initially unknown. In this paper, we address this novel challenge by proposing the first reactive, and abstraction-free LTL planning algorithm that can be applied for complex mission planning of multiple robots operating in unknown environments. Our algorithm is reactive in the sense that temporal logic planning is adapting to the updated map of the environment and abstraction-free as it does not rely on designing abstractions of robot dynamics. Our proposed algorithm is complete under mild assumptions on the structure of the environment and the sensor models. Our paper provides extensive numerical simulations and hardware experiments that illustrate the theoretical analysis and show that the proposed algorithm can address complex planning tasks in unknown environments.
Dynamic robot path planning system using neural network

With the development of science and technology, the intelligent robot has become an important tool in our production and life. It not only improves people’s living standards but also promotes economic development. At present, the related technology in the field of the intelligent robot has been developed rapidly, but at the same time, many technical problems have been exposed. The single path planning problem can be well solved, but the dynamic path planning of a robot is one of the current technical difficulties. At present, the genetic algorithm is the mainstream scheme, but its control accuracy is still lacking in practical application. To solve this problem, this paper proposes a dynamic path planning scheme for intelligent robots based on a fuzzy neural network. The research of this paper is mainly divided into four parts. The first part is to analyze the current situation of technology research in this field and put forward the idea of this paper by analyzing the shortcomings of existing technologies. The second part is the research of related basic theory, which deeply studies the core theory of intelligent robot and dynamic path planning, which provides a theoretical basis for the later model implementation. The third part is the design and implementation of dynamic path planning based on a fuzzy neural network. This paper gives the design principle and specific improvement method in detail. At the end of the paper, that is, the fourth part, through comparative analysis experiments, further proves the superiority of the fuzzy neural network algorithm. Compared with the traditional particle swarm optimization algorithm, it can significantly improve the control accuracy and robustness of the model.
Women Empowerment in UAE: A Systematic Review

Women in the UAE have long been recognized as equal partners in national development and many efforts were taken to pursue empowering women in cultural, social, and economic fields. This systematic review is distinctive; that it synthesizes research on the influence of different factors affecting women leadership/entrepreneurship positions in the UAE. It aims to investigate the key factors affecting the female entrepreneurs and leaders, and to develop a conceptual model on the principal social and cultural factors inducing the success of Emirati women in attaining senior leadership roles or leading entrepreneurs. Moreover, this study proposes a set of new directions for future research venues to address the current dearth of empirical work on women’s leadership in the UAE, despite all the empowering programs and initiatives. It includes articles using quantitative and qualitative methods from different databases like ProQuest and EBSCO. The study reviews databases and selects many high-quality articles to be reviewed using a set of inclusion and exclusion criteria. 23 articles were chosen after removing the duplicated ones. From a practical perspective, this study is important while it summarizes and evaluates all factors affecting women empowering in leadership roles and tries to offer a proper model for a large number of women empowerment drivers within the UAE setting.
A Fluid Flow Data Set for Machine Learning and its Application to Neural Flow Map Interpolation

In recent years, deep learning has opened countless research opportunities across many different disciplines. At present, visualization is mainly applied to explore and explain neural networks. Its counterpart-the application of deep learning to visualization problems-requires us to share data more openly in order to enable more scientists to engage in data-driven research. In this paper, we construct a large fluid flow data set and apply it to a deep learning problem in scientific visualization. Parameterized by the Reynolds number, the data set contains a wide spectrum of laminar and turbulent fluid flow regimes. The full data set was simulated on a high-performance compute cluster and contains 8000 time-dependent 2D vector fields, accumulating to more than 16 TB in size. Using our public fluid data set, we trained deep convolutional neural networks in order to set a benchmark for an improved post-hoc Lagrangian fluid flow analysis. In in-situ settings, flow maps are exported and interpolated in order to assess the transport characteristics of time-dependent fluids. Using deep learning, we improve the accuracy of flow map interpolations, allowing a more precise flow analysis at a reduced memory IO footprint.
Overview of Machine Learning Part 1

The extensive body of research and advances in machine learning (ML) and the availability of a large volume of patient data make ML a powerful tool for producing models with the potential for widespread deployment in clinical settings. This article provides an overview of the classic supervised and unsupervised ML methods as well as fundamental concepts required for understanding how to develop generalizable and high-performing ML applications. It also describes the important steps for developing a ML model and how decisions made in these steps affect model performance and ability to generalize.
Classical and Robust Regression Analysis with Compositional Data

Abstract Compositional data carry their relevant information in the relationships (logratios) between the compositional parts. It is shown how this source of information can be used in regression modeling, where the composition could either form the response, or the explanatory part, or even both. An essential step to set up a regression model is the way how the composition(s) enter the model. Here, balance coordinates will be constructed that support an interpretation of the regression coefficients and allow for testing hypotheses of subcompositional independence. Both classical least-squares regression and robust MM regression are treated, and they are compared within different regression models at a real data set from a geochemical mapping project.
Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques

Abstract Background Oral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer. Very few researches have been carried out for the automatic diagnosis of OSCC using artificial intelligence techniques. Though biopsy is the ultimate test for cancer diagnosis, analyzing a biopsy report is a very much challenging task. To develop computer‐assisted software that will diagnose cancerous cells automatically is very important and also a major need of the hour. Aim To identify OSCC based on morphological and textural features of hand‐cropped cell nuclei by traditional machine learning methods. Methods In this study, a structure for semi‐automated detection and classification of oral cancer from microscopic biopsy images of OSCC, using clinically significant and biologically interpretable morphological and textural features, are examined and proposed. Forty biopsy slides were used for the study from which a total of 452 hand‐cropped cell nuclei has been considered for morphological and textural feature extraction and further analysis. After making a comparative analysis of commonly used methods in the segmentation technique, a combined technique is proposed. Our proposed methodology achieves the best segmentation of the nuclei. Henceforth the features extracted were fed into five classifiers, support vector machine, logistic regression, linear discriminant, k ‐nearest neighbors and decision tree classifier. Classifiers were also analyzed by training time. Another contribution of the study is a large indigenous cell level dataset of OSCC biopsy images. Results We achieved 99.78% accuracy applying decision tree classifier in classifying OSCC using morphological and textural features. Conclusion It is found that both morphological and textural features play a very important role in OSCC diagnosis. It is hoped that this type of framework will help the clinicians/pathologists in OSCC diagnosis.
Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images

Classification of histology images is a task that has been widely explored on recent computer vision researches. The most studied approach for this task has been the application of deep learning through a convolutional neural network (CNN) model. However, the use of CNNs in the context of histological images classification has yet some limitations such as the need of large datasets, the slow training time and the difficult to implement a generalized model able to classify different types of histology tissues. In this paper, we propose an ensemble model based on handcrafted fractal features and deep learning that consists of combining the classification of two CNNs by applying the sum rule. We apply feature extraction to obtain 300 fractal features from different histological datasets. These features are reshaped into a 10×10×3 matrix to compose an artificial image that is given as input to the first CNN. The second CNN model receives as input the correspondent original image. After combining the results of both CNNs, accuracies that range from 89.66% up to 99.62% were obtained from five different datasets. Moreover, our model was able to classify images from datasets with imbalanced classes, without the need for images having the same resolution, and in relative fast training time. We also verified that the obtained results are compatible with the most recent and relevant studies recently published in the context of histology image classification.