Reviewerly/exHarmony huggingface dataset:
Release the Power of Online-Training for Robust Visual Tracking

Convolutional neural networks (CNNs) have been widely adopted in the visual tracking community, significantly improving the state-of-the-art. However, most of them ignore the important cues lying in the distribution of training data and high-level features that are tightly coupled with the target/background classification. In this paper, we propose to improve the tracking accuracy via online training. On the one hand, we squeeze redundant training data by analyzing the dataset distribution in low-level feature space. On the other hand, we design statistic-based losses to increase the inter-class distance while decreasing the intra-class variance of high-level semantic features. We demonstrate the effectiveness on top of two high-performance tracking methods: MDNet and DAT. Experimental results on the challenging large-scale OTB2015 and UAVDT demonstrate the outstanding performance of our tracking method.
KPNet: Towards Minimal Face Detector

The small receptive field and capacity of minimal neural networks limit their performance when using them to be the backbone of detectors. In this work, we find that the appearance feature of a generic face is discriminative enough for a tiny and shallow neural network to verify from the background. And the essential barriers behind us are 1) the vague definition of the face bounding box and 2) tricky design of anchor-boxes or receptive field. Unlike most top-down methods for joint face detection and alignment, the proposed KPNet detects small facial keypoints instead of the whole face by in the bottom-up manner. It first predicts the facial landmarks from a low-resolution image via the well-designed fine-grained scale approximation and scale adaptive soft-argmax operator. Finally, the precise face bounding boxes, no matter how we define it, can be inferred from the keypoints. Without any complex head architecture or meticulous network designing, the KPNet achieves state-of-the-art accuracy on generic face detection and alignment benchmarks with only ∼ 1M parameters, which runs at 1000fps on GPU and is easy to perform real-time on most modern front-end chips.
Controllable and Progressive Image Extrapolation

Image extrapolation aims at expanding the narrow field of view of a given image patch. Existing models mainly deal with natural scene images of homogeneous regions and have no control of the content generation process. In this work, we study conditional image extrapolation to synthesize new images guided by the input structured text. The text is represented as a graph to specify the objects and their spatial relation to the unknown regions of the image. Inspired by drawing techniques, we propose a progressive generative model of three stages, i.e., generating a coarse bounding-boxes layout, refining it to a finer segmentation layout, and mapping the layout to a realistic output. Such a multi-stage design is shown to facilitate the training process and generate more controllable results. We validate the effectiveness of the proposed method on the face and human clothing dataset in terms of visual results, quantitative evaluations and flexible controls.
UCF-STAR: A Large Scale Still Image Dataset for Understanding Human Actions

Action recognition in still images poses a great challenge due to (i) fewer available training data, (ii) absence of temporal information. To address the first challenge, we introduce a dataset for STill image Action Recognition (STAR), containing over $1M$ images across 50 different human body-motion action categories. UCF-STAR is the largest dataset in the literature for action recognition in still images. The key characteristics of UCF-STAR include (1) focusing on human body-motion rather than relatively static human-object interaction categories, (2) collecting images from the wild to benefit from a varied set of action representations, (3) appending multiple human-annotated labels per image rather than just the action label, and (4) inclusion of rich, structured and multi-modal set of metadata for each image. This departs from existing datasets, which typically provide single annotation in a smaller number of images and categories, with no metadata. UCF-STAR exposes the intrinsic difficulty of action recognition through its realistic scene and action complexity. To benchmark and demonstrate the benefits of UCF-STAR as a large-scale dataset, and to show the role of “latent” motion information in recognizing human actions in still images, we present a novel approach relying on predicting temporal information, yielding higher accuracy on 5 widely-used datasets.
Heterogeneous Transfer Learning with Weighted Instance-Correspondence Data

Instance-correspondence (IC) data are potent resources for heterogeneous transfer learning (HeTL) due to the capability of bridging the source and the target domains at the instance-level. To this end, people tend to use machine-generated IC data, because manually establishing IC data is expensive and primitive. However, existing IC data machine generators are not perfect and always produce the data that are not of high quality, thus hampering the performance of domain adaption. In this paper, instead of improving the IC data generator, which might not be an optimal way, we accept the fact that data quality variation does exist but find a better way to use the data. Specifically, we propose a novel heterogeneous transfer learning method named Transfer Learning with Weighted Correspondence (TLWC), which utilizes IC data to adapt the source domain to the target domain. Rather than treating IC data equally, TLWC can assign solid weights to each IC data pair depending on the quality of the data. We conduct extensive experiments on HeTL datasets and the state-of-the-art results verify the effectiveness of TLWC.
INSTRUCTIONAL DESIGN AND ITS TOOLS: THEORETICAL STUDY AND CASE STUDY

The article analyzes the issue of “instructional design” in domestic and international academic research and suggests its new interpretation. Particular attention is paid to instructional design tools conditionally called visual, audio, audio-visual and spatial. Instructional design tools are viewed as a combination of pedagogical and design tools within a certain set of principles (SMART goals, personalization, non-linear learning, targeting all the representational systems, reflection and feedback, aesthetics). The author describes the process of creating one of these instructional design tools and provides empirical data to prove the efficiency of applying such a tool while forming high school students’ moral values.
Lightweight Authentication for MP4 Format Container Using Subtitle Track

With massive utilization of video in every aspect of our daily lives, managing videos is crucial and demanding. The rich literature of data embedding has proven its viability in managing as well as enriching videos and other multimedia contents, but conventional methods are designed to operate in the media/compression layer. In this work, the synchronization between the audio-video and subtitle tracks within an MP4 format container is manipulated to insert data. Specifically, the data are derived from the statistics of the audio samples and video frames, and it serves as the authentication data for verification purpose. When needed, the inserted authentication data can be extracted and compared against the information computed from the received audio samples and video frames. The proposed method is lightweight because simple statistics, i.e., ‘0’ and ‘1’ at the bit stream level, are treated as the authentication data. Furthermore, unlike conventional MP4 container format-based data insertion technique, the bit stream size remains unchanged before and after data insertion using the proposed method. The proposed authentication method can also be deployed for joint utilization with any existing authentication technique for audio / video as long as these media can be multiplexed into a single bit stream and contained within an MP4 container. Experiments are carried out to verify the basic functionality of the proposed technique as an authentication method.
Emotion Recognition with Facial Landmark Heatmaps

Facial expression recognition is a very challenging problem and has attracted more and more researchers’ attention. In this paper, considering that facial expression recognition is closely related to the features of key facial regions, we propose a facial expression recognition network that explicitly utilizes the landmark heatmap information to precisely capture the most discriminative features. In addition to directly adding the information of facial fiducial points in the form of landmark heatmaps, we also propose an end-to-end network structure--heatmap aiding emotion network (HAE-Net) by embedding the landmark detection module based on stack-based hourglass network into the facial expression recognition network. Experiments on CK+, RAF and AffectNet databases show that our method achieves better results compared with the state-of-the-art methods, which demonstrates that adding additional landmark information, as well as joint training of landmark detection and expression recognition, are beneficial to improve recognition performance.
Unsupervised Scene Adaptation with Memory Regularization in vivo

We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data. Existing methods focus on minoring the inter-domain gap between the source and target domains. However, the intra-domain knowledge and inherent uncertainty learned by the network are under-explored. In this paper, we propose an orthogonal method, called memory regularization in vivo to exploit the intra-domain knowledge and regularize the model training. Specifically, we refer to the segmentation model itself as the memory module, and minor the discrepancy of the two classifiers, i.e., the primary classifier and the auxiliary classifier, to reduce the prediction inconsistency. Without extra parameters, the proposed method is complementary to the most existing domain adaptation methods and could generally improve the performance of existing methods. Albeit simple, we verify the effectiveness of memory regularization on two synthetic-to-real benchmarks: GTA5 -&gt; Cityscapes and SYNTHIA -&gt; Cityscapes, yielding +11.1% and +11.3% mIoU improvement over the baseline model, respectively. Besides, a similar +12.0% mIoU improvement is observed on the cross-city benchmark: Cityscapes -&gt; Oxford RobotCar.
Improving symbolic data visualization for pattern recognition and knowledge discovery

This paper examines the visualization of symbolic data and considers the challenges rising from its complex structure. Symbolic data is usually aggregated from large data sets and used to hide entry specific details and to transform huge amounts of data (like big data) into analyzable quantities. It is also used to offer an overview in places where general trends are more important than individual details. Symbolic data comes in many forms like intervals, histograms, categories and modal multi-valued objects. Symbolic data can also be considered as a distribution. Currently, the de facto visualization approach for symbolic data is zoomstars which has many limitations. The biggest limitation is that the default distributions (histograms) are not supported in 2D as additional dimension is required. This paper proposes several new improvements for zoomstars which would enable it to visualize histograms in 2D by using a quantile or an equivalent interval approach. In addition, several improvements for categorical and modal variables are proposed for a clearer indication of presented categories. Recommendations for different approaches to zoomstars are offered depending on the data type and the desired goal. Furthermore, an alternative approach that allows visualizing the whole data set in comprehensive table-like graph, called shape encoding, is proposed. These visualizations and their usefulness are verified with three symbolic data sets in exploratory data mining phase to identify trends, similar objects and important features, detecting outliers and discrepancies in the data.
The Impact of Emotional Intelligence on Employee Performance in Saudi Arabia Banking Sector

Saudi Arabia’s banking sector plays an important role in the country’s development as it is among the leading sectors in the financial sector. Considering, two main Saudi banks (The National Commercial Bank and Saudi American bank), the present study aims to observe the impact of emotional intelligence on employee performance. The components of emotional intelligence affecting employee performance include self-management, relationship management, self-awareness, and social awareness. A quantitative methodology was applied to analyse the survey results of 300 respondents over the period from 2018 to 2019. The results show that there was a significant positive impact of self-management, self-awareness, and relationship management on employee performance. However, in the Saudi banking sector, there was no impact on social awareness on employee performance.
Monocular Depth Estimation Based on Multi-Scale Graph Convolution Networks

Monocular depth estimation is a foundation task of three-dimensional (3D) reconstruction which is used to improve the accuracy of environment perception.Because of the simpler hardware requirement, it is more suitable than other multi-view methods.In this study, a new monocular depth estimation algorithm based on graph convolution network (GCN) is proposed.The pixel-wise depth relationship is introduced into conventional convolution neural network (CNN) to make up the disadvantage of processing non-Euclidian data.And the remaining depth topological graph information on the spatial latent variables are extracted based on a multi-scale reconstruction strategy.The final results on NYU-v2 depth dataset and KITTI depth dataset demonstrate that our algorithm improves the quality of monocular depth estimation, especially there are several little objects coexisting in the scenes.
Consideration on liquid structure contributing to discrimination capability of Liquid State Machine

Liquid State Machine (LSM) is one of reservoir computing models and due to universal computing capability its application and improvement attract researchers. Meanwhile development of measurement technology reveals the existence of specific structure in brain networks, such as scale-free, small-world, and modular properties, which contribute to higher-order brain functions such as cognition and memory. In this paper, we apply various network models to a recurrent neural network of an LSM and investigate the relationship between structural properties and accuracy of discrimination. Results suggest that modularity of a recurrent neural network enhances discrimination capability of LSM.
Robust Self-Weighted Multi-View Projection Clustering

Many real-world applications involve data collected from different views and with high data dimensionality. Furthermore, multi-view data always has unavoidable noise. Clustering on this kind of high-dimensional and noisy multi-view data remains a challenge due to the curse of dimensionality and ineffective de-noising and integration of multiple views. Aiming at this problem, in this paper, we propose a Robust Self-weighted Multi-view Projection Clustering (RSwMPC) based on ℓ2,1-norm, which can simultaneously reduce dimensionality, suppress noise and learn local structure graph. Then the obtained optimal graph can be directly used for clustering while no further processing is required. In addition, a new method is introduced to automatically learn the optimal weight of each view with no need to generate additional parameters to adjust the weight. Extensive experimental results on different synthetic datasets and real-world datasets demonstrate that the proposed algorithm outperforms other state-of-the-art methods on clustering performance and robustness.
New complex-valued activation functions

We present a new type of activation functions for a complex-valued neuralnetwork (CVNN). A proposed activation function is constructed such that itfixes a given ellipse. We obtain an application to a complex-valued Hopfieldneural network (CVHNN) using a special form of the introduced complexfunctions as an activation function. Considering the interesting geometricproperties of the plane curve ellipse such as focusing property, weemphasize that these properties may have possible applications in variousneural networks.
Harmonizing business and digital enterprise strategy using SOA middle-out and service-based approach

The organization's agility represents its ability to respond fast to changes by the transformation and/or optimization of business processes.In the conditions of the ever-increasing use of digital technologies, the agility of organization can be compromised if the digital strategy and IT resources are not flexible enough to adequately respond to changed business conditions.Service-Oriented Architecture (SOA) promotes better alignment of business goals and IT resources to faster respond to changes.However, given the challenges of the digital era, especially when it comes to distribution, complexity, scalability, and delivery speed, the success of the SOA initiatives increasingly depends on the choice of delivery strategy and implementation approach.The SOA middle-out delivery strategy is guided by business strategy, strategic vision and goals, with reuse of existing IT resources and focus on urgent requirements, tactical and operational goals.On the other hand, SOA evolves towards increasing agility and a simpler, service-based implementation approach that supports fast delivery.This paper, in general, discusses the ways of more efficient alignment of business and digital enterprise strategy, based on the implementation of SOA initiatives for the development of business software systems.Within the framework of the research, the significant challenges and best practices have been identified and clearly distinguished, as well as the advantages and disadvantages, which are related to the key architectural and methodological aspects of the SOA implementation using the middle-out delivery strategy and service-based approach.
A Sentiment Analysis Based Approach for Understanding the User Satisfaction on Android Application

The consistency of user satisfaction on mobile application has been more competitive because of the rapid growth of multi-featured applications. The analysis of user reviews or opinions can play a major role to understand the user's emotions or demands. Several approaches in different areas of sentiment analysis have been proposed recently. The main objective of this work is to assist the developers in identifying the user's opinion on their apps whether positive or negative. A sentiment analysis based approach has been proposed in this paper. NLP-based techniques Bags-of-Words, N-Gram, and TF-IDF along with Machine Learning Classifiers, namely, KNN, Random Forest (RF), SVM, Decision Tree, Naive Byes have been used to determine and generate a well-fitted model. It's been found that RF provides 87.1% accuracy, 91.4% precision, 81.8% recall, 86.3% F1-Score. 88.9% of accuracy, 90.8% of precision, 86.4% of recall, and 88.5% of F1-Score are obtained from SVM.
A Multi-Feature Representation of Skeleton Sequences for Human Interaction Recognition

Inspired from the promising performances achieved by recurrent neural networks (RNN) and convolutional neural networks (CNN) in action recognition based on skeleton, this paper presents a deep network structure which combines both CNN for classification and RNN to achieve attention mechanism for human interaction recognition. Specifically, the attention module in this structure is utilized to give various levels of attention to various frames by different weights, and the CNN is employed to extract the high-level spatial and temporal information of skeleton data. These two modules seamlessly form a single network architecture. In addition, to eliminate the impact of different locations and orientations, a coordinate transformation is conducted from the original coordinate system to the human-centric coordinate system. Furthermore, three different features are extracted from the skeleton data as the inputs of three subnetworks, respectively. Eventually, these subnetworks fed with different features are fused as an integrated network. The experimental result shows the validity of the proposed approach on two widely used human interaction datasets.
Robust RGBD Tracking via Weighted Convolution Operators

Discriminative Correlation Filter (DCF) based trackers achieve superior performance with continuous conceptual improvement in the tracking field. By now, traditional DCF based trackers suppose that the tracked target is rigid and could be represented by an axis-aligned rectangular box well. Those trackers suffer from object deformation, irregular object shape and partial occlusion where the target bounding box is filled with both target and background pixels. Recently, the depth information captured by the depth sensors offer complementary information to RGB data. Generally, depth information highlights the foreground target from the background, which mitigates the backgournd effect in the bounding box. In this paper, we propose to learn Weighted Convolution Operators (WCO) for robust RGBD tracking. First, WCO integrate deep features extracted from the RGB channels and hand-craft features extracted from the depth channel to enhance target representation. Second, a weight map is jointly derived from the depth and color information to highlight the foreground area. Each value on the weight map demonstrates the possibility of this pixel pertaining to the foreground area. Last, WCO is optimized with the Preconditioned Congugate Gradient (PCG) Method during correlation filter training. Our proposed WCO tracker achieves the top performance on the Princetion Tracking Benchmark (PTB), which demonstrates the validity of our RGBD tracking framework.
The Implementation of Mobile Seamless Learning Strategy in Mastering Students' Concepts for Elementary School

Implementation of the Mobile Seamless Learning Strategy at SDU Bumi Kartini has a significant influence on students' concept mastery ability, Mobile seamless learning also has advantages 1) students are able to learn unlimited classrooms and time, 2) students can learn anytime and anywhere, 3) integrated learning between formal education and non-formal education, 4) students are digital native so they are very familiar with the world of mobile phones, 5) students can learn both personally and socially, 6) students are able to learn both digitally and physically. The research conducted at Bumi Kartini Elementary School in Jepara Central Java Indonesia, This is quantitative research conducted using a quasi-experimental whit non-equivalent control group. The participants are student IV class (Makkah, Jeddah, Medina, Yunani) Subject were selected by classing cluster random sampling.
Prediction of Sepsis Using LSTM with Hyperparameter Optimization with a Genetic Algorithm

Here in this study, we propose an algorithm for the prediction of sepsis using Long Short-Term Memory (LSTM) neural networks.The algorithm was trained and validated on public dataset proposed by PhysioNet Challenge 2019.We have used a differential evolution genetic algorithm to find optimal training hyperparameters and probability threshold for the inference phase.The deployed version of the algorithm obtained normalized utility score 0.278 based on full challenge test set (team name: ISIBrno; rank 27 of 78).Presented method required 50 mins of single-core CPU processing time.