Reviewerly/exHarmony huggingface dataset:
Recommendation algorithm based on users’ interest preferences and Restricted Boltzmann machine

In order to overcome the disadvantages of recommendation algorithm based on restricted Boltzmann machine, which only uses the severely sparse scoring matrix in the training process, and can not effectively extract user characteristics effectively, and also ignores the users' interests and item attributes. a recommendation algorithm based on users' interest preferences and Restricted Boltzmann Machines is proposed. Firstly, the Restricted Boltzmann machines for collaborative filtering algorithm is utilized to predict the user rating of the item is r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ; then the information of users' interest preference is used to establish a users' rating preference model, and predict the user rating the item is r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ; Finally, the linear regression algorithm is utilized to confirm the weights of r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> to obtain the final prediction score r . The experimental results show that the proposed algorithm in this paper can improve the predicted accuracy of the recommendation system.
Moving Object Tracking Algorithm Based on Improved Gaussian Mixture Model

The moving object image frame has characteristics such as fast change of frame, large moving range, large difference between frames and rapid change of background image, which lead to tracking foreground misjudgment, tracking lag and even losing track objectives. In order to solve this problem, we propose a moving object tracking algorithm based on Gaussian mixture model. By setting up the mixed Gaussian model and updating the dynamic pixel parameters, we eliminate the effect of pixel reduction caused by rapid change of image frame, remove background image interference, and solves the problem of tracking lag. Simulation results show that the proposed algorithm can improve the accuracy and lag of moving objects tracking in complex background and achieve good tracking performance compared with traditional algorithms.
Skeleton-Based Pedestrian Abnormal Behavior Detection with Spatio-Temporal Model in Public Places

Abstract In computer vision, there is growing interest in the recognition of pedestrian abnormal behaviors. The abnormal behavior of a person could be the sign of some dangerous activities. However, it’s still challenging to extract the discriminative spatial and temporal features effectively faced with video data. In this paper, we propose skeleton-based pedestrian abnormal behavior detection models. The base model is consisting of ResNet as a spatial feature extractor, LSTM as a global temporal feature extractor, and the ResNet network that use the dual-stream network to extract local temporal features. The proposed model is an improvement of all ResNet into Conv1x1_ResNet, and added a layer of Conv1x1_ResNet after dual-stream Conv1x1_ResNet to extract more accurate global space features. The proposed model achieved the highest accuracy of 89.29%, and the averaged get batch time is 0.3399 ms. The base model achieved 88.12% accuracy, and the averaged get batch time is 0.3174 ms less than the time taken by other models. Both models reach speed of 80 frames/sec. Compared with the models made in previous work, the base model has the shortest training time, and the proposed model provides the highest accuracy in the field of pedestrian detection.
Segmentation of Brain Stroke Lesions using Marker-based Algorithms in CT images

The computed tomography has huge role in the assessment of the hemorrhagic lesions of the brain. Physicians widely use CT to delineate the size and magnitude of the bleeding. In the medical image processing, the separation and detection of the objects is very crucial issue. The water-based segmentation (subdivision) is an approach that use to detect the closely contact margins tissues within the images. Manual outlining of the stroke in CT images considers as subjective operation that takes long time with less accuracy. In this study, the lesions were detected firstly and followed by Contrast augmentation and Segmentation. The suggested technique was evaluated to endorse its achievability and efficiency. These techniques attained 0.97 + 0.01, 0.98 + 0.02 and 0.991 + 0.01 (P = 0.001) for sensitivity, specificity and operating curve analysis, respectively. The analysis of the results images showed that the proposed approach is effective in detecting of the smaller lesions which might missed by using other segmentation methods.
Irregular Action Recognition in Court with 3D Residual Network

Several irregular behaviors, such as smoking, absent and dozing, sometimes occur in the court. To ensure the participants in court in order without irregular behaviors is of importance for a regular trial. However, manually handling such numerous surveillance videos to discriminate and prevent irregular actions is a mission impossible. Recently many effective action recognition algorithms had been proposed, which brings opportunity to overcome the difficulty. We take into account the particularities of irregular actions in court, i.e. stationary background and small range of motion, and thus propose our irregular action recognition method based on 3D residual network that focuses on modeling spatiotemporal information of motion in videos. Experiments show our method can achieve a recognition rate of 82.3% on 13 classes of irregular actions in court.
Augmented reality as a tool for open science platform by research collaboration in virtual teams

The provision of open science is defined as a general policy aimed at overcoming the barriers that hinder the implementation of the European Research Area (ERA). An open science foundation seeks to capture all the elements needed for the functioning of ERA: research data, scientific instruments, ICT services (connections, calculations, platforms, and specific studies such as portals). Managing shared resources for the community of scholars maximizes the benefits to society. In the field of digital infrastructure, this has already demonstrated great benefits. It is expected that applying this principle to an open science process will improve management by funding organizations in collaboration with stakeholders through mechanisms such as public consultation. This will increase the perception of joint ownership of the infrastructure. It will also create clear and non-discriminatory access rules, along with a sense of joint ownership that stimulates a higher level of participation, collaboration and social reciprocity. The article deals with the concept of open science. The concept of the European cloud of open science and its structure are presented. According to the study, it has been shown that the structure of the cloud of open science includes an augmented reality as an open-science platform. An example of the practical application of this tool is the general description of MaxWhere, developed by Hungarian scientists, and is a platform of aggregates of individual 3D spaces.
Fine-grained Scheduling in FPGA-Based Convolutional Neural Networks

FPGA has been considered as a promising solution to accelerate Convolutional Neural Networks (CNNs) for its excellent performance in energy efficiency and programmability. However, prior designs are usually designed for inference only as designers can map pre-trained models to the hardware in a very efficient way. However, those approaches may not be suitable for training CNN models. In this paper, we propose FConv, in which the CPU and FPGA work together in a fine-grained manner. The FPGA accelerator in FConv uses one Winograd-based convolver, which reduces the design complexity and improves performance. We apply double-buffer for output routine to effectively overlap computation and data transfer. We also integrate multiple PEs to improve data parallelism. We propose our analytical model for prediction and use it as a guide in task scheduling. We find the upper limit of performance under the current design based on the analytical model. We evaluate our design on VGG-16 and Densnet-40 on ImageNet and CIFAR-10. We achieve 262.43 GOP/s on the VGG-16 model, which is 2.13× of the performance compared to FFT-based implementation on the same platform. We also achieve at most 4×+ performance improvement compared MKL with 20 threads running on 10 core Intel processors.
Multi-Scale Dense Selective Kernel Spatial Attention Network for Single Image De-raining

Different rain streaks attached to a single image will affect the visibility of the image. According to earlier researches, it is a challenging problem for single image rain streaks removal due to the various orientations and scales of rain streaks. We propose a multi-scale dense selective kernel spatial attention network for single image de-raining. To extract the information of the features from rain streaks effectively and accurately, we adopt selective kernel units which allow each neuron to adaptively adjust its receptive field with the multi-scale manner on the channel dimension. To better guide the feature extraction, we utilize spatial attention blocks which focus on the informative parts of spatial map via the multi-scale mechanism on the spatial dimension. Additionally, we further exploit densely connected network architecture to strengthen the feature reuse and ensure the useful information propagate persistently. We conduct extensive experiments on several datasets to demonstrate the considerable performance improvement of our proposed method over other state-of-the-art methods. Simultaneously, we present an ablation study to indicate the performance improvement obtained by different modules of our proposed method.
Research on Elderly Fall Prediction for Walking Posture of Elderly-Assistant Robot

A prediction method of elderly fall based on RBF neural network and multi-sensor information fusion was proposed in this paper, which could help the user of elderly-assistant robot walk outside safe and reduce the damage caused by falls. Firstly, overall scheme of the elderly fall prediction system was designed. Tactile sensors, tri-axial acceleration sensor and gyroscope were used to collect the touch information of user's hands, the acceleration information and angle of inclination information of human trunk, respectively. Then, three kinds of feature information were extracted separately and fused up by RBF neural network to get probability of elderly fall. If the probability exceeded the threshold, it was judged that the human body had a tendency to fall. Finally, experimental system construction and verification were carried out and the results showed that the prediction method of the user fall was reliable, and the overall prediction accuracy rate was 98%. Among them, the prediction accuracy of normal samples and user fall samples were 100% and 96%), respectively. This method can predict the user fall more accurately, and it provides some guarantees for preventing elderly fall.
Color Based Segmentation Using Fuzzy C Means For Bamboo As EnvironmentallyFriendly Material

Abstract Bamboo is an environmentally friendly material. Many benefits of bamboo that we can get. Indonesia as a bamboo-producing country needs easy techniques to make good classification of bamboo. Bamboo is composed of fibers and fiber adhesives. There are various kinds of bamboo in Indonesia. This study uses digital image processing with fuzzy c means based segmentation to identify bamboo. Segmentation is an important thing in image processing. By using fuzzy c means in segmentation in this study obtained good segmentation results. This study uses 4 types of bamboo, namely Javanese bamboo, Ori bamboo and Petung bamboo and Wulung bamboo. There are 40 images as training images and 12 test images. The results of segmentation show that fuzzy c means produces good segmentation with the number of iterations between 20-23 and time ranging from 0.11 to 0.15. The accuracy of this test reaches 80%.
Research on Semantic Consistency Problem Based on Word2vec

In order to ensure the effective convergence of data with the same semantics but different expressions when collaborative business data is aggregated, this paper studies the semantic consistency problem. Firstly, the word vector model based on word2vec is trained to obtain the data word vector, then the cosine similarity of the data word vector can be calculated. Then the clustering based on the cosine similarity is used to obtain the preliminary core word set. Finally, the final core word set is obtained through the rule-based correction. Experiments and analysis show that this method can effectively complete business requirements and achieve high accuracy
A Path Planning Algorithm for Sweeping Robot Based on Improved Neural Network

This paper plans to solve the problem of the lacks of traditional path planning algorithm in self-learning and the disadvantages in local path planning. We propose a path-planning algorithm for sweeping robots based on improved neural network. It is aimed to provide a collision-free planning route for robots in an unknown environment. This algorithm is based on the neural network algorithm, which concludes a four-layer structure of network, and designs the energy function as the evaluation function of the network. First I need to find the extreme value of the energy function, and then I need to adjust the motion based on the path points, next, the robot completes the path planning task. The effectiveness of the method is proved by simulation experiments.
Research on Prediction of Movie Box Office Based on Internet Comments

Movie box office is the key factor affecting movie revenue, and box office prediction has received widespread attention. By analyzing the characteristics of movie online reviews, this paper uses semantic orientation, k-Nearest Neighbor (k-NN) nearest neighbor classification algorithm and regression equation to construct movie box office prediction model, and validates this method with actual movie data.
Multimodal Word Discovery and Retrieval With Spoken Descriptions and Visual Concepts

In the absence of dictionaries, translators, or grammars, it is still possible to learn some of the words of a new language by listening to spoken descriptions of images. If several images, each containing a particular visually salient object, each co-occur with a particular sequence of speech sounds, we can infer that those speech sounds are a word whose definition is the visible object. A multimodal word discovery system accepts, as input, a database of spoken descriptions of images (or a set of corresponding phone transcriptions) and learns a mapping from waveform segments (or phone strings) to their associated image concepts. In this article, four multimodal word discovery systems are demonstrated: three models based on statistical machine translation (SMT) and one based on neural machine translation (NMT). The systems are trained with phonetic transcriptions, MFCC and multilingual bottleneck features (MBN). On the phone-level, the SMT outperforms the NMT model, achieving a 61.6% F1 score in the phone-level word discovery task on Flickr30k. On the audio-level, we compared our models with the existing ES-KMeans algorithm for word discovery and present some of the challenges in multimodal spoken word discovery.
Social and Cultural Factors Influence on English Speaking Abilities

Social and cultural factors assume a huge job in learning climate. The speaker foundation assumes an indispensable job in English speaking skills, and it could be a dialect capacity that is horrifying by social and cultural segments. The researcher utilized the technique to hold the majority of the improvement occurred inside the class in regular settings and conjointly to gauge the speaking troubles experienced by technical course aspirants. This examination focuses on the impact of socio-cultural factors on the English-speaking abilities and it comprises observations, interviews, unstructured meetings, and surveys. The combination of different culture foundation students took part and accepted the need for social air and cultural support, which can empower the dimension of English expressive knowledge because they feel unsure while communicating. This examination is to discover useful inputs for learners, parents, and educationists to upgrade the procedures of instruction and figuring out ways to improve speaking abilities in English.
Attention Based BiLSTM-MCNN for Sentiment Analysis

Convolutional neural networks (CNN) and recurrent neural networks (RNN) have proven to be effective text model representations in text sentiment analysis. However, CNN only considers the local information between consecutive words, ignoring the context-dependent information of long distance between words, and the pooling operation will lose some semantic information in the process of forward propagation; RNN does not show a significant advantage over CNN in the case of short text lengths. This paper extracts text features by integrating attention based Bi-directional long short-term memory networks (AttBiLSTM) and MCNN (Multi-channel CNN) networks to improve the text representation of the model. Contrasting experiments on the two datasets show that the hybrid model effectively improves the accuracy of text classification.
Ccfcnet: Channel-Communication Factorization Convnet for Real-Time Semantic Segmentation

Abstract In this paper, we address a challenging real-time semantic segmentation task by proposing a lightweight encoder-decoder architecture. The architecture called channel-communication factorization ConvNet (CCFCNet), in which both efficiency and accuracy are taken into account. The core of our work is channel-communication factorization convolution module (CCFC) and dense dialation-rate block (DDB). CCFC module, where split, channel communication and fuse operations are utilized to greatly save reasoning time and improve the quality of information refinement with a few additional calculations. Meanwhile, four CCFC modules with different dialation rates form a dense dialation-rate block (DDB), which can obtain denser feature refinement and enlarge receptive field to improve the segmentation accuracy of objects with different size in images. The proposed architecture, which contains only 1.25M parameters, achieves a mean IOU of 71.4% on the Cityscapes dataset and can run over to 42FPS on a single GTX 1080Ti GPU. It makes a better trade-off between accuracy and efficiency than state-of-the-art methods with comparable performance.
Quantum Chicken Swarm Optimization with Levy Flight and Its Application in Parameter optimization of Random Forest

In order to solve the problem of unbalanced global and local search ability and optimization accuracy in chicken swarm optimization, we propose a quantum chicken swarm optimization with Levy flight (L-QCSO). First, the quantum system is used to increase the search space of the roosters. Second, the Levy flights strategy is used to improve the hens behavior. Finally, the improved chicken swarm optimization is used to optimize the number of tree and the number of split variables pre-selected by the tree node on the random forest. In the experiment, results show that the optimized chicken swarm optimization is effective and better than the chicken swarm optimization on the benchmark functions. In the end, the optimized chicken swarm optimization is used to optimize RF, which is applied into oil layer recognition. Results show that this optimization model is superior to the RF in terms of recognition rate and time.
A prototype of solar panel position control system based on image processing

Abstract The position of the solar panel towards sunlight is very influential on the electrical energy production. The solar panel orthogonal to the sunlight is the best position to produce electrical energy. The aim of this research is creating a prototype of the solar panel position control system. The expected position of the system is solar panels are always perpendicular to the sun. In this research, we used image processing to control the solar panel position. We used a webcam to capture a shadow image of a 4cm bolt that placed on a flat plane. Then, the image from the shadow is processed to get the angle of the sun. We use trigonometric formulas to convert bolt shadows into angles. After that, the angle used as input for the solar panel drive motor. Thus, the solar panel can be adjusted to follow the angle of the sun. So that the positions are orthogonal to each other. The research finding discovered that the prototype can always follow the direction of incoming sunlight with an accuracy rate towards the center point is 95% for azimuth (X-axis) and 96% for altitude (Y-axis).
Design of decentralized personal loaning platform based on blockchain

With the growth of Internet and computer technology in the world, the p2p loaning model has gradually caught the attention of many, but with the exposure of its flaws, the issues of its insufficient credibility and regulatory confusion have sequentially emerged. The emergence of blockchain and smart contract technology have brought new solutions to curb these issues. Smart contracts can be directly used to control transfer and trading of digital assets. The blockchain environment ensures that the entire transaction process is transparent, which invisibly increases the credibility of the loaning platform and reduces series of problems caused by inadequate supervision. This paper proposes a personal loaning platform based on the blockchain technology and illustrates in detail the design and business process of the smart contract. Experiments show that the smart contract implementation on the blockchain of personal loaning completes the steps of borrowing, repayment and compensation smoothly.
Panoramic View of Human Jaw Under Ambiguity Intraoral Camera Movement

Intra-oral cameras are recommended tools in every dental clinic. The intra-oral camera allows dentists to capture images of difficult-to-reach areas in the mouth. Oral dental applications based on visual data suffer from various challenges such as the environmental effects and the unstable camera movement. We propose an approach, which addresses these challenges, to stitch human teeth images that are captured by an intra-oral camera. First, due to the low density of texture on the tooth surface, normal maps are estimated to reveals the impacted geometric properties of each image inside an area, boundary, and shape. Normal maps are rich with features, which can be used in the stitching process. Second, we investigate the unrestricted camera movement problem. The camera may be moved along the jaw curve with different angles and distances due to handshaking. To overcome this problem, we test each frame, after warping it, and only correct frames are used to generate the panoramic view. The proposed approach shows a similar performance of other cases, in which the camera movement is restricted.
Eye Tracking Solutions in Cognitive CAPTCHA Authentication

This paper will describe new areas of eye tracking application connected with secure user authentication, using cognitive CAPTCHA codes. Cognitive CAPTCHA require special knowledge or perception skills, during selection of visual parts for personal authentication. In such protocols can be also implemented contactless interfaces in the form of eye tracking devices. Application of such technology allow to trace the point of attention which should be focused on proper visual elements in proper semantic order for successful user authentication.