Reviewerly/exHarmony huggingface dataset:
A long video caption generation algorithm for big video data retrieval

Videos captured by people are often tied to certain important moments of their lives. But with the era of big data coming, the time required to retrieval and watch can be daunting. In this paper, novel techniques are proposed for the application of long video segmentation, which can effectively shorten the retrieval time. The motion extent of long video is detected by the improved of the spatio-temporal interest points (STIPs) detection algorithm. After that, the superframe segmentation of the filtered long video is performed to gain the interesting clip of long video. In the selection of keyframes, the region of interest is constructed by the use of the STIP already obtained on the video clips, and the saliency detection of these regions of interest is utilized to screen out video keyframes. Finally, we generate the video captions by adding attention vectors to the traditional LSTM. Our method is benchmarked on the VideoSet dataset, and evaluated by the BLEU, Meteor and Rouge.
Non-linear adaptive closed-loop control system for improved efficiency in IoT-blockchain management

IoT network generates a large amount of data. This means that the monitoring and control of these networks and the transfer of packets from the IoT network to the server can cause communications to collapse. On the other hand, due to the large volume of data stored in the databases the monitoring of the IoT network needs very powerful servers to have a high degree of efficiency. This paper presents a novel adaptive closed-loop control system and speed up searches model to improve the monitor and control efficiency in IoT networks, specially those which are based in blockchain. The non linear control model under consideration includes a new way to evaluate the optimal number of blocks that should be at the queue of the miners’ network in order to make the process efficient through the use of queuing theory. Also, a new system to speed up searches is presented by using hashmaps, which makes the monitoring process faster, reliable and efficient. The efficiency of the presented approach is illustrated by a numerical case study.
The Neural Hype and Comparisons Against Weak Baselines

Recently, the machine learning community paused in a moment of self-reflection. In a widelydiscussed paper at ICLR 2018, Sculley et al. [13] wrote: "We observe that the rate of empirical advancement may not have been matched by consistent increase in the level of empirical rigor across the field as a whole." Their primary complaint is the development of a "research and publication culture that emphasizes wins" (emphasis in original), which typically means "demonstrating that a new method beats previous methods on a given task or benchmark". An apt description might be "leaderboard chasing"-and for many vision and NLP tasks, this isn't a metaphor. There are literally centralized leaderboards1 that track incremental progress, down to the fifth decimal point, some persisting over years, accumulating dozens of entries. Sculley et al. remind us that "the goal of science is not wins, but knowledge". The structure of the scientific enterprise today (pressure to publish, pace of progress, etc.) means that "winning" and "doing good science" are often not fully aligned. To wit, they cite a number of papers showing that recent advances in neural networks could very well be attributed to mundane issues like better hyperparameter optimization. Many results can't be reproduced, and some observed improvements might just be noise.
Liver CT sequence segmentation based with improved U-Net and graph cut

Liver segmentation has always been the focus of researchers because it plays an important role in medical diagnosis. However, under the condition of low contrast between a liver and surrounding organs and tissues, CT image noise and the large difference between the liver shapes of patients, existing liver image segmentation algorithms are difficult to obtain satisfactory results. To improve this situation, we propose a liver CT sequesnce image segmentation algorithm GIU-Net, which combines an improved U-Net neural network model with graph cutting. Specifically, we initially segment a liver from a liver CT sequence using an improved U-Net and obtain the probability distribution map of the liver regions. Secondly, the sequence segmentation start slice is selected, and then the context information of the liver sequence images and the liver probability distribution map are used to construct a graph cut energy function. Finally, the segmentation is done by minimizing the graph cut energy function. Our experimental results show that GIU-Net has a good performance when segmenting liver sequence images in terms of segmentation accuracy and robustness.
Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network

A large community of research has been developed in recent years to analyze social media and social networks, with the aim of understanding, discovering insights, and exploiting the available information. The focus has shifted from conventional polarity classification to contemporary application-oriented fine-grained aspects such as, emotions, sarcasm, stance, rumor, and hate speech detection in the user-generated content. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we propose a deep learning model called sAtt-BLSTM convNet that is based on the hybrid of soft attention-based bidirectional long short-term memory (sAtt-BLSTM) and convolution neural network (convNet) applying global vectors for word representation (GLoVe) for building semantic word embeddings. In addition to the feature maps generated by the sAtt-BLSTM, punctuation-based auxiliary features are also merged into the convNet. The robustness of the proposed model is investigated using balanced (tweets from benchmark SemEval 2015 Task 11) and unbalanced (approximately 40000 random tweets using the Sarcasm Detector tool with 15000 sarcastic and 25000 non-sarcastic messages) datasets. An experimental study using the training- and test-set accuracy metrics is performed to compare the proposed deep neural model with convNet, LSTM, and bidirectional LSTM with/without attention and it is observed that the novel sAtt-BLSTM convNet model outperforms others with a superior sarcasm-classification accuracy of 97.87% for the Twitter dataset and 93.71% for the random-tweet dataset.
Emperor Penguins Colony: a new metaheuristic algorithm for optimization

A metaheuristic is a high-level problem independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Metaheuristic algorithms attempt to find the best solution out of all possible solutions of an optimization problem. A very active area of research is the design of nature-inspired metaheuristics. Nature acts as a source of concepts, mechanisms and principles for designing of artificial computing systems to deal with complex computational problems. In this paper, a new metaheuristic algorithm, inspired by the behavior of emperor penguins which is called Emperor Penguins Colony (EPC), is proposed. This algorithm is controlled by the body heat radiation of the penguins and their spiral-like movement in their colony. The proposed algorithm is compared with eight developed metaheuristic algorithms. Ten benchmark test functions are applied to all algorithms. The results of the experiments to find the optimal result, show that the proposed algorithm is better than other metaheuristic algorithms.
QoE Modeling for HTTP Adaptive Video Streaming–A Survey and Open Challenges

With the recent increased usage of video services, the focus has recently shifted from the traditional quality of service-based video delivery to quality of experience (QoE)-based video delivery. Over the past 15 years, many video quality assessment metrics have been proposed with the goal to predict the video quality as perceived by the end user. HTTP adaptive streaming (HAS) has recently gained much attention and is currently used by the majority of video streaming services, such as Netflix and YouTube. HAS, using reliable transport protocols, such as TCP, does not suffer from image artifacts due to packet losses, which are common in traditional streaming technologies. Hence, the QoE models developed for other streaming technologies alone are not sufficient. Recently, many works have focused on developing QoE models targeting HAS-based applications. Also, the recently published ITU-T Recommendation series P.1203 proposes a parametric bitstream-based model for the quality assessment of progressive download and adaptive audiovisual streaming services over a reliable transport. The main contribution of this paper is to present a comprehensive overview of recent and currently undergoing works in the field of QoE modeling for HAS. The HAS QoE models, influence factors, and subjective test methodologies are discussed, as well as existing challenges and shortcomings. The survey can serve as a guideline for researchers interested in QoE modeling for HAS and also discusses possible future work.
Boosted neural network ensemble classification for lung cancer disease diagnosis

Accurate diagnosis of Lung Cancer Disease (LCD) is an essential process to provide timely treatment to the lung cancer patients. Artificial Neural Networks (ANN) is a recently proposed Machine Learning (ML) algorithm which is used on both large-scale and small-size datasets. In this paper, an ensemble of Weight Optimized Neural Network with Maximum Likelihood Boosting (WONN-MLB) for LCD in big data is analyzed. The proposed method is split into two stages, feature selection and ensemble classification. In the first stage, the essential attributes are selected with an integrated Newton–Raphsons Maximum Likelihood and Minimum Redundancy (MLMR) preprocessing model for minimizing the classification time. In the second stage, Boosted Weighted Optimized Neural Network Ensemble Classification algorithm is applied to classify the patient with selected attributes which improves the cancer disease diagnosis accuracy and also minimize the false positive rate. Experimental results demonstrate that the proposed approach achieves better false positive rate, accuracy of prediction, and reduced delay in comparison to the conventional techniques.
AdaptiveFace: Adaptive Margin and Sampling for Face Recognition

Training large-scale unbalanced data is the central topic in face recognition. In the past two years, face recognition has achieved remarkable improvements due to the introduction of margin based Softmax loss. However, these methods have an implicit assumption that all the classes possess sufficient samples to describe its distribution, so that a manually set margin is enough to equally squeeze each intra-class variations. However, real face datasets are highly unbalanced, which means the classes have tremendously different numbers of samples. In this paper, we argue that the margin should be adapted to different classes. We propose the Adaptive Margin Softmax to adjust the margins for different classes adaptively. In addition to the unbalance challenge, face data always consists of large-scale classes and samples. Smartly selecting valuable classes and samples to participate in the training makes the training more effective and efficient. To this end, we also make the sampling process adaptive in two folds: Firstly, we propose the Hard Prototype Mining to adaptively select a small number of hard classes to participate in classification. Secondly, for data sampling, we introduce the Adaptive Data Sampling to find valuable samples for training adaptively. We combine these three parts together as AdaptiveFace. Extensive analysis and experiments on LFW, LFW BLUFR and MegaFace show that our method performs better than state-of-the-art methods using the same network architecture and training dataset. Code is available at https://github.com/haoliu1994/AdaptiveFace.
An Interval Type-3 Fuzzy System and a New Online Fractional-Order Learning Algorithm: Theory and Practice

The main reason of the extensive usage of the fuzzy systems in many branches of science is their approximation ability. In this paper, an interval type-3 fuzzy system (IT3FS) is proposed. The uncertainty modeling capability of the proposed IT3FS is improved in contrast to type-1 and type-2 fuzzy systems (T1FS and T2FS). Because in the proposed IT3FS, the membership is defined as an interval type-2 fuzzy set, whereas in T1FS and T2FS, the membership is crisp value and type-1 fuzzy set, respectively. An online fractional-order learning algorithm is given to optimize the consequent parameters of the IT3FS. The stability of the learning algorithm is proved by utilizing the Lyapunov method. The validity of the proposed fuzzy system is illustrated by both simulation and the experimental studies. It is shown that the proposed fuzzy system and associated learning algorithm result in better approximation performance in comparison with the other well-known approaches.
Content-aware generative modeling of graphic design layouts

Layout is fundamental to graphic designs. For visual attractiveness and efficient communication of messages and ideas, graphic design layouts often have great variation, driven by the contents to be presented. In this paper, we study the problem of content-aware graphic design layout generation. We propose a deep generative model for graphic design layouts that is able to synthesize layout designs based on the visual and textual semantics of user inputs. Unlike previous approaches that are oblivious to the input contents and rely on heuristic criteria, our model captures the effect of visual and textual contents on layouts, and implicitly learns complex layout structure variations from data without the use of any heuristic rules. To train our model, we build a large-scale magazine layout dataset with fine-grained layout annotations and keyword labeling. Experimental results show that our model can synthesize high-quality layouts based on the visual semantics of input images and keyword-based summary of input text. We also demonstrate that our model internally learns powerful features that capture the subtle interaction between contents and layouts, which are useful for layout-aware design retrieval.
Improved Mixed-Example Data Augmentation

In order to reduce overfitting, neural networks are typically trained with data augmentation, the practice of artificially generating additional training data via label-preserving transformations of existing training examples. While these types of transformations make intuitive sense, recent work has demonstrated that even non-label-preserving data augmentation can be surprisingly effective, examining this type of data augmentation through linear combinations of pairs of examples. Despite their effectiveness, little is known about why such methods work. In this work, we aim to explore a new, more generalized form of this type of data augmentation in order to determine whether such linearity is necessary. By considering this broader scope of "mixed-example data augmentation", we find a much larger space of practical augmentation techniques, including methods that improve upon previous state-of-the-art. This generalization has benefits beyond the promise of improved performance, revealing a number of types of mixed-example data augmentation that are radically different from those considered in prior work, which provides evidence that current theories for the effectiveness of such methods are incomplete and suggests that any such theory must explain a much broader phenomenon.
Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting

Crowd counting is a challenging task in the presence of drastic scale variations, the clutter background, and severe occlusions, etc. Existing CNN-based counting methods tackle these challenges mainly by fusing either multi-scale or multi-context features to generate robust representations. In this paper, we propose to address these issues by leveraging the heterogeneous attributes compounded in the density map. We identify three geometric/semantic/numeric attributes essentially important to the density estimation, and demonstrate how to effectively utilize these heterogeneous attributes to assist the crowd counting by formulating them into multiple auxiliary tasks. With the multi-fold regularization effects induced by the auxiliary tasks, the backbone CNN model is driven to embed desired properties explicitly and thus gains robust representations towards more accurate density estimation. Extensive experiments on three challenging crowd counting datasets have demonstrated the effectiveness of the proposed approach.
D-UNet: A Dimension-Fusion U Shape Network for Chronic Stroke Lesion Segmentation

Assessing the location and extent of lesions caused by chronic stroke is critical for medical diagnosis, surgical planning, and prognosis. In recent years, with the rapid development of 2D and 3D convolutional neural networks (CNN), the encoder-decoder structure has shown great potential in the field of medical image segmentation. However, the 2D CNN ignores the 3D information of medical images, while the 3D CNN suffers from high computational resource demands. This paper proposes a new architecture called dimension-fusion-UNet (D-UNet), which combines 2D and 3D convolution innovatively in the encoding stage. The proposed architecture achieves a better segmentation performance than 2D networks, while requiring significantly less computation time in comparison to 3D networks. Furthermore, to alleviate the data imbalance issue between positive and negative samples for the network training, we propose a new loss function called Enhance Mixing Loss (EML). This function adds a weighted focal coefficient and combines two traditional loss functions. The proposed method has been tested on the ATLAS dataset and compared to three state-of-the-art methods. The results demonstrate that the proposed method achieves the best quality performance in terms of DSC = 0.5349 ± 0.2763 and precision = 0.6331 ± 0.295).
Refine and Distill: Exploiting Cycle-Inconsistency and Knowledge Distillation for Unsupervised Monocular Depth Estimation

Nowadays, the majority of state of the art monocular depth estimation techniques are based on supervised deep learning models. However, collecting RGB images with associated depth maps is a very time consuming procedure. Therefore, recent works have proposed deep architectures for addressing the monocular depth prediction task as a reconstruction problem, thus avoiding the need of collecting ground-truth depth. Following these works, we propose a novel self-supervised deep model for estimating depth maps. Our framework exploits two main strategies: refinement via cycle-inconsistency and distillation. Specifically, first a student network is trained to predict a disparity map such as to recover from a frame in a camera view the associated image in the opposite view. Then, a backward cycle network is applied to the generated image to re-synthesize back the input image, estimating the opposite disparity. A third network exploits the inconsistency between the original and the reconstructed input frame in order to output a refined depth map. Finally, knowledge distillation is exploited, such as to transfer information from the refinement network to the student. Our extensive experimental evaluation demonstrate the effectiveness of the proposed framework which outperforms state of the art unsupervised methods on the KITTI benchmark.
A New Quadratic Binary Harris Hawk Optimization for Feature Selection

Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms.
Impact of augmented reality technology on academic achievement and motivation of students from public and private Mexican schools. A case study in a middle-school geometry course

In this paper, the authors show that augmented reality technology has a positive impact on learning-related outcomes of middle-school Mexican students. However, the impact varies depending on whether students were enrolled in public or private schools. The authors designed an augmented reality application for students to practice the basic principles of geometry, and a similar application which encompasses identical learning objectives and content deployed in a Web-based learning environment. A 2 × 2 × 2 factorial design was employed with 93 participants to investigate the effect of type of technology (web, augmented reality), type of school (private, public), and time of assessment (pre, post) on motivation, and declarative learning. The results show that: (1) there is an interactive effect of type of technology, type of school, and time of assessment when students' achievement scores are measured; (2) students using the augmented reality-based learning environments scored higher in post-test than those using the web-based application; (3) the augmented reality learning environment was more learning effective compared with the web-based learning environment in public schools, but not in private schools; (4) there is not an interactive effect of type of technology, type of school and time of assessment when students' motivation is measured; (5) students from private schools reported higher levels of motivation compared with those from public schools when using the augmented reality learning environment. The research findings imply that in Mexico, augmented reality technology can be exploited as an effective learning environment for helping middle-school students from public and private schools to practice the basic principles of Geometry.