Reviewerly/exHarmony huggingface dataset:
TAFM: Topic-Attention FM-encoder for Text Generation

With the development and application of deep learning, the research of text generation has made great progress, especially in the fields of machine translation, dialogue generation, etc. However, with the application of text generation model in practical engineering, the semantic control of text generation becomes an important problem to be solved urgently. At present, text generation has some good effects in emotional control and temporal control, but it has not achieved good results in semantic control of keywords. The key problem is how to use the keywords semantic information encoding and how to use the keywords semantic information in the text generation process. In view of the above problems, a self encoding and decoding framework integrating factorization machine and topic-weighted attention mechanism is proposed, which is named TAFM. The factorization machine model can focus on refining the combination characteristics between keywords, so as to more comprehensively represent the semantic information of keywords combination. The topic-weighted attention mechanism weights and adds the generated text coding and keyword semantic coding. Adjusting the weight can be vividly understood as adjusting the control intensity of semantic information. Thus, it is more conducive to the expression of topic information. The experimental results show that our method obtains a better BLEU score. In addition, our method can generate coherent sentences containing keywords through manual evaluation.
A Wrapper Approach for Video Anomaly Detection Applying Light Gradient Boosting Machine in a Multiple Instance Learning Setting

The automatic detection of video anomalies is a challenging task due to problems such as the subjectivity of the anomaly definition and the sparseness and diversity of anomalous events with the consequent difficulty in obtaining discriminative features. Furthermore, labeling videos is a laborious and expensive task. Multiple instance learning (MIL), by labeling videos instead of frames, has become a solution to mitigate this last challenge. This work presents a wrapper-based MIL approach applying LightGBM for video anomaly detection. From the evaluation with a challenging dataset, we found that our model is competitive against other published methods which used the same test setup.
The Effect of the Implementation the Wordwall Application on the Mastery German Vocabulary for the 11th Graders of SMAN 7 Malang

This study aims to determine the effect of the implementation the Wordwall application on the mastery German vocabulary for the 11th graders of SMAN 7 Malang. The approach used in this research is quantitative with quasi-experimental method andresearch design pretest and posttest control group. method Purposive sampling used in sampling is a class XI student Interests German Cross, XI IPS 2 as the control class (using PowerPoint) and XI Languages 1 as an experimental class (using the application Wordwall).The test method was used as a data collection technique. Furthermore, the data obtained were analyzed by prerequisite tests and hypothesis testing. Test the hypothesis using the T test. The results of the analysis show that the probability value (p) less than tcount = 2.001717 less than 4.247526, which means H0 is rejected. Thus, it can be concluded that there is an effect of the implementation the Wordwall application on the mastery German vocabulary for the 11th graders of SMAN 7 Malang.
Grouped Spatio-Temporal Alignment Network for Video Super-Resolution

Video super-resolution based on CNNs has recently achieved significant progress. The existing popular super-resolution methods usually impose the optical flow between neighboring frames for temporal alignment. However, estimation of accurate optical flow is hard and expends greater computation. To address this problem, we propose a novel grouped spatio-temporal alignment network (GSTAN) that effectively incorporates spatio-temporal information in a hierarchical way. The input sequence is divided into several groups corresponding to different frame rates. These groups provide complementary information, which is helpful to restore missing textures for the reference frame. Specifically, each group employs deformable 3D convolution to incorporate spatio-temporal information, which avoids artifacts from explicit motion estimation. In addition, a Gated-Dconv information filter is proposed to control information flow to focus on the fine details. Finally, these groups provide complementary information, which is integrated with the inter-group fusion module. Extensive experiments have demonstrated our method achieves state-of-the-art SR performance on several benchmark datasets.
Chain Whispering Games: The Effective Methods of Developing Receptive Language Skills of Children Aged 5 - 6 Years

This research examines the Whispering Chain Game on Receptive Language Skills of 5-6-year-old Children in Kindergartens in Makassar City. The purpose of this study was to determine the description of children's receptive language skills before being treated, to find out the description of children's receptive language skills after being treated, and to determine whether or not there was an effect of the chain-whisper game on the receptive language skills of children aged 5-6 years in kindergarten. Makassar city before and after the chain whispering activity was given. The research approach used was a quantitative approach with a Quasi-Experimental Design type of research. The population in this study were students aged 5-6 years in TK Aisyiyah Parangtambung. Sampling in this study is cluster random sampling. The sample in this study was 12 children. A total of 6 children in the experimental group and 6 children in the control group. Data collection techniques used were descriptive statistical analysis and non-parametric statistical analysis. Based on the results of the study, it can be concluded that the receptive language skills of children who were treated with the whisper-chain game method in the experimental class were better than in the control class, while Tcount = 87 &gt; Ttable = 2,228 was accepted, and H0 was rejected while Zcount was = 2.36 and Ztable = 0.4906 was rejected. H1 accepts this proves that the whisper-chain game method has a significant effect on receptive language skills in early childhood.
Text-based Talking Facial Synthesis for Virtual Host System

With the prevailing of deep learning technology, automatic virtual image synthesis has made huge progress and the popularity of virtual portraits has been growing rapidly. Traditional virtual synthesis system rely on computer graphics method driven by motion capture of a real person, which need labor and equipment costs. In view of this, our paper proposes a virtual host synthesis method based on text driven to generate lip shape and facial animation(include eye movement and head pose) from a signal facial image of a virtual host. More precisely, we use three main modules to construct a virtual host synthesis system: a speech synthesis module based on Tacotron2, a speech to landmark points module to extract mixture landmarks movement to speech, and video generation module based on conditional generative adversarial network to generate video frames and realize time-continuous automatic sport news reporting.
A Practical Method for Occupational Skills Detection in Vietnamese Job Listings

Vietnamese labor market has been under an imbalanced development. The number of university graduates is growing, but so is the unemployment rate. This situation is often caused by the lack of accurate and timely labor market information, which leads to skill miss-matches between worker supply and the actual market demands. To build a data monitoring and analytic platform for the labor market, one of the main challenges is to be able to automatically detect occupational skills from labor-related data, such as resumes and job listings. Traditional approaches rely on existing taxonomy and/or large annotated data to build Named Entity Recognition (NER) models. They are expensive and require huge manual efforts. In this paper, we propose a practical methodology for skill detection in Vietnamese job listings. Rather than viewing the task as a NER task, we consider the task as a ranking problem. We propose a pipeline in which phrases are first extracted and ranked in semantic similarity with the phrases' contexts. Then we employ a final classification to detect skill phrases. We collected three datasets and conducted extensive experiments. The results demonstrated that our methodology achieved better performance than a NER model in scarce datasets.
A Smart Cuckoo Filter with Disjoint-set to Detect the Endless Loop

Cuckoo filter is a new type of approximate membership query data structure that stores the fingerprints of set elements. Because of its higher performance than bloom filter, cuckoo filter has been developed in recent years and gradually replaced the bloom filter in many fields. However, the endless loop problem in cuckoo hashing makes element insertion inefficient and unstable, which hinders cuckoo filter development. To solve this problem, we propose a new variant of cuckoo filter called smart cuckoo filter. Smart cuckoo filter uses a variable bucket structure to save the element insertion information at each position. It keeps buckets aligned to memory, thereby reducing the space waste and inefficiency of data access. Smart cuckoo filter uses the disjoint-set to determine whether the element will fall into the endless loop before the element is inserted. Also, the insertion is friendly to the concurrency of the entire filter. The experiments demonstrate that the element insertion speed of smart cuckoo filter in the endless loops is better than that of the traditional cuckoo filter, which can reach 2.2x in some cases.
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement Learning

Safety guarantee is essential in many engineering implementations. Reinforcement learning provides a useful way to strengthen safety. However, reinforcement learning algorithms cannot completely guarantee safety over realistic op-erations. To address this issue, this work adopts control barrier functions over reinforcement learning, and proposes a compen-sated algorithm to completely maintain safety. Specifically, a sum-of-squares programming has been exploited to search for the optimal controller, and tune the learning hyperparameters simultaneously. Thus, the control actions are pledged to be always within the safe region. The effectiveness of proposed method is demonstrated via an inverted pendulum model. Compared to quadratic programming based reinforcement learning methods, our sum-of-squares programming based reinforcement learning has shown its superiority.
Measurement of Human Height by using the Convolutional Neural Network and the Trigonometric Theory

Measurement of human height from a digital camera using the technique of object detection to select a sub-image of interest by pointing the target on both feet and the human as keypoint detection by using U-Net convolutional neural network. These keypoint was used in conjunction with the relationship of triangles. It will help to be able to calculate the height of human accurately. From the experimental results when tested with three humans 162,173 and 180 cm, the maximum error is 2.76%. the percent of error increase when the distance between the human and the camera increases. Moreover, the height of the camera affects the error value. The percent of error will high when the height human.
SKYGLYPHS: REFLECTIONS on the DESIGN of a DELIGHTFUL VISUALIZATION

In creating SkyGlyphs, our goal was to develop a data visualization that could possibly capture people’s attention and spark their curiosity to explore a dataset. This work was inspired by a mingling of research including serendipitous interactions, visualizations for public displays, and personal visualizations. SkyGlyphs is a nonconventional whimsical visualization, depicting datapoints as animated balloons in space. We designed it to encourage non-experts to casually browse the contents of a repository through visual interactions like linking and grouping of datapoints. Our contributions include SkyGlyphs’ representation and our design reflection that reveals a perspective on how to design delightful visualizations.
Peningkatan Kemampuan Menulis Surat Lamaran Pekerjaan melalui Model Pembelajaran Think-Pair-Share pada Siswa Kelas XII MIA 1 SMA Negeri 1 Pancur Batu

&#x0D; &#x0D; &#x0D; &#x0D; The purpose of this study was to determine how the ability to write job application letters for students of class XII MIA 1 SMA Negeri 1 Pancur Batu and to find out whether cooperative learning with the Think-Pair-Share model can improve the ability to write cover letters for students of class XII MIA 1 SMA Negeri 1 Pancur Batu. . The benefit of this research for students is to increase knowledge and insight in writing job application letters. The benefits of this research for teachers are: as an effort to offer new innovations in how to learn to write job application letters, as an effort to motivate students in writing job application letters, and as an effort to improve performance, abilities, and professionalism in improving the quality of learning in the classroom. The benefit of this research for researchers is to broaden knowledge and provide learning experiences that foster research abilities and skills as well as broader knowledge. This study uses classroom action research (action research) in two rounds (cycles). Each round consists of four stages, namely: planning, activities and observations, reflection, and revision. The target of this research is the students of class XII MIA 1 SMA Negeri 1 Pancur Batu. The data obtained in the form of subjective test results and observation sheets of teaching and learning activities.&#x0D; &#x0D; &#x0D; &#x0D;
The Depth Measurement of Digital Stereomicroscope System

With the continuous development of micro-systems, micro-control and relevant technologies, the demands for micro-measurements in industrial automations of production and inspection are increasing rapidly. Meanwhile, more critical requirements accuracy, efficiency, and cost are expected. To address this situation, a digital microscopic depth measurement system is constructed with a common stereo microscope and two cameras. In the work, a specific fixture is designed to connect the cameras and the microscope to enable easy optical alignment and calibration. Then a formula is summarized to build an accurate relation between disparity and depth corresponding to the specific microscopic system. Next, a CUDA enhanced fast stereo matching algorithm is proposed and practiced to give live disparity map. Finally, the disparity map is transformed into the depth map so that can achieve the depth measurement of microscopic target and perform accuracy analysis. The adjustable magnification of microscope gives feasibility for different view cope and precision requirement. The system takes several types of microcapacitors as the object of analysis, and quantitatively studies the relevance between measurement resolution-parallax-precision at the same time. Compared with some existing research, experimental results show a micron level precision and great efficiency that promises various real applications.
AR Technology of English Stereo Teaching Material Based on Computer Graphics Technology

Augmented reality uses computer graphics technology and visualization technology to generate virtual objects that do not exist in the real environment. The virtual objects and the real environment are integrated through the display device to present users with the new environment with real sensory effects. The stereoscopic English teaching materials for higher vocational colleges based on AR technology conform to the modern developmental education concept represented by Piaget, Rogers and Tao Xingzhi. Based on AR technology, this paper designs the development process of “topic selection and plan, production, research and development, resource encapsulation, check and test, publication and spread” of English stereoscopic textbooks in higher vocational colleges, and proposes to “carry out the complete design of English knowledge structure for higher vocational education, learn from advanced and mature AR technology from abroad, in-depth integration of AR technology and paper teaching materials” and other development strategies.
Crowd Counting Algorithm Based on Depthwise Separable of Dilated Convolution

With the improvement of our modernization level and the development of urbanization, the phenomenon of large crowds gathering is very common, which brings great hidden danger to public safety. Crowd counting plays a very important role in the field of intelligent video surveillance. It can estimate the total number of people according to the image and provide real-time warning, which can effectively avoid the occurrence of safety accidents. In this paper, in order to build an efficient lightweight crowd density estimation model, we proposed a dilated and depthwise separable convolution network. In the network, we build a dilated and depthwise separable convolution module, which improves the receptive field of the convolution kernel without increasing network parameters. Meanwhile, sparse convolutional kernels are used to extract sparse features, and compact convolutional kernels are used to extract more dense features, thus improving the efficiency of the convolutional kernels. Through experimental comparison on SHT Part A data set, the accuracy of the proposed method is 2.6% higher than that of MCNN, and the parameters are 7.6% less.
Denoising Labels: A Noise-robust Method for Webly-supervised Fine-grained Visual Classification

For fine-grained visual classification (FGVC), the annotation of fine-grained data usually requires expert knowledge, which makes it difficult to obtain the fine-grained data. Recently, an increasing number of researchers start to pay attention to using web images for fine-grained classification directly. However, noisy labels inevitably exist in web images. Noisy labels are harmful to Convolutional Neural Networks (CNNs) due to their strong capability in fitting any data. To this end, we propose an end-to-end label denoising based fine-grained classification method to tackle noisy labels. We separate the clean samples and the noisy samples based on the cosine similarity between the sample and the class center. For samples with low cosine similarity to the class center, we select the reusable samples with prediction uncertainty and correct the labels of reusable samples to the true labels. Finally, the clean samples and the corrected reusable samples are fed into the network for further training. We provide extensive experimental results to demonstrate the superiority of the proposed approach. The data and source code of this work have been made available at: https://github.com/huarm123/LDWFC.git.
A Novel Hog-Based Template Matching Method for SAR and Optical Image

Due to multiplicative speckle noise in Synthetic Aperture Radar(SAR) image and significant intensity difference between different data, it is difficult to match SAR and optical image accurately. In this paper, we propose a novel template matching method for SAR and optical image named multi-Dimensional Matching Histogram of Oriented Gradient (mDM-HOG). Firstly, in order to reduce the negative effect of speckle noise on gradient calculation, the ratio of exponentially weighted averages(ROEWA) operator is introduced to calculate the gradient magnitude and orientation in SAR image. Then, using the obtained gradient information, we extract the 3-D pixelwise HOG feature for both images. Finally, we separate the 3-D feature map to nine sub-maps and measure the similarity of the sub-maps to obtain the template matching result. The experimental result shows that in comparison with the existing methods, the proposed template matching method has higher accuracy when locating the position of SAR image in optical image.
Siamese Network Tracker by Attention Module and Relation Detector Module

In recent years, object tracking techniques based on Siamese networks have shown excellent tracking performance. However, in the tracking process, there will be many similar objects, and it is easy to track the wrong object due to the weak discriminative ability of the network. At the same time, the classification and regression of SiamRPN ++ are usually optimized independently, which will cause a mismatch problem, that is, the location with the highest classification confidence is not necessarily the object. To address these problems, we proposed a Siamese network tracker by attention module and relation detector module (SiamAR). First, we introduce a multi-scale attention mechanism in SiamRPN++ to capture information at different scales, and fuse spatial attention and channel attention to improving the ability to learn feature information. Not only different receptive fields are obtained, but also useful features are selectively focused and less useful features are suppressed. In order not to affect the computational efficiency, the method of grouping parallel computing is used. Secondly, we add a relation detector module to our tracker to filter out distractors from the background and distinguish the object in the cluttered background. Experiment results show that our algorithm out-performs several well-known tracking algorithms in terms of tracking accuracy and robustness.
TermiNETor: Early Convolution Termination for Efficient Deep Neural Networks

The efficiency of convolutional neural networks (CNNs) is dictated by the computationally intensive convolution operations. Thereby, extensive research has sought to alleviate the cost of convolution. In this work, we opt to skipping the ineffectual outputs, which can be used along with the other approaches such as quantization and pruning. Contrary to the previous works that skip ineffectual inputs, i.e., zero activations and weights, we predict and skip the ineffectual outputs by exploiting two key attributes of the state-of-the-art CNN architectures: ReLU (which filters out the negative outputs) is the widely-used activation function, and max-pooling (which discards the non-max outputs) is the commonly used down-sampling layer. To realize that, we propose TermiNETor, which breaks the convolution to bitwise shift-and-add operations. During the inference, weights are processed bit by bit, whereby TermiNETor uses a simple heuristic to predict whether the final output will be ineffectual. Upon prediction, TermiNETor terminates the convolution for ineffectual outputs. We propose a calibration flow that takes the prediction-based forward propagation into account and realizes more accurate predictions. In addition, we propose a novel hardware architecture to take advantage of early termination by feeding new convolution to the released processing elements. We demonstrate the effectiveness of TermiNETor on various networks and datasets. Experimental results show that our framework achieves up to 1.7× reduction of operation count compared to non-skipping baseline without accuracy degradation. TermiNETor accelerator, augmented with the skipping flow, improves the average energy efficiency by 3.84× over the zero weight/input skipping accelerator SCNN, and by 1.98× over FuseKNA which skips zero inputs as well as repetitive computations in a bit-serial manner. The proposed architecture exhibits good throughput and energy consumption scalability by increasing the processing elements, meaning that it can also target high-performance applications while preserving the energy efficiency.