Reviewerly/exHarmony huggingface dataset:
A Learning-based Real Comment Classifier for Multimedia Teaching Quality Evaluation Systems

In this paper, we proposes a learning-based real comment capturer for multimedia teaching quality evaluation systems. The main idea is fusing the multi-stream features to feed a DFs (Deep Forests)-based classifier. We consider three context information, including the semantic, grammar and structural context. They are organized as the unit feature, intra-structural feature and inter-structural feature from bottom to top. Then, these features are socketed into a fixed-sized zero vector to re-represent the input sentences with the trees of word vectors. We employ static analysis, TreeGAN to assign and generate the numeric labels for the relations among the sub-sentences and sentences, which are combined with the semantic context to decide by two groups of RFs and complete RFs. After an AVG-MAX operation, we classify the input comments into the positive/negative classes. Due to the higher-level representation, the scribbled comments with antiphrasis can be captured accurately. Experiments report a couple of better scores for positive/negative instances.
A Comparative Study of Various Human Activity Recognition Approaches

Abstract Human Activity Recognition (HAR) is a vast and exciting topic for researchers and students. HAR aims to recognize activities by observing the actions of subjects and surrounding conditions. This topic also has many significant and futuristic applications and a basis of many automated tasks like 24*7 security surveillance, healthcare, laws regulations, automatic vehicle controls, game controls by human motion detection, basically human-computer interaction. This survey paper focuses on reviewing other research papers on sensing technologies used in HAR. This paper has covered distinct research in which researchers collect data from smartphones; some use a surveillance camera system to get video clips. Most of the researchers used videos to train their systems to recognize human activities collected from YouTubes and other video sources. Several sensor-based approaches have also covered in this survey paper to study and predict human activities, such as accelerometer, gyroscope, and many more. Some of the papers also used technologies like a Convolutional neural network (CNN) with spatiotemporal three-dimensional (3D) kernels for model development and then using to integrate it with OpenCV. There are also work done for Alzheimer’s patient in the Healthcare sector, used for their better performance in day-to-day tasks. We will analyze the research using both classic and less commonly known classifiers on distinct datasets available on the UCI Machine Learning Repository. We describe each researcher’s approaches, compare the technologies used, and conclude the adequate technology for Human Activity Recognition. Every research will be discussed in detail in this survey paper to get a brief knowledge of activity recognition.
Towards Augmented Reality-Based Remote Family Visits in Nursing Homes

Family visiting restrictions in nursing homes due to COVID-19-related measures have a major impact on elderly and their families. As an alternative communication means, TNO is developing an augmented reality (AR)-based solution to realize high-quality virtual social contact. To investigate its suitability for remote family visits in nursing homes, the AR-based solution will be compared to regular video calling in a user study involving elderly and their family members. Based on focus groups with elderly, family and caretakers, user experience (UX) indicators have been established to evaluate these virtual family visits, of which social presence was the most prominent. Remote family visits via AR-based and regular video calling are expected to result in different UX. It is hypothesized that participants will report the highest levels of social presence in the AR condition. If AR-based video calling is indeed preferred, TNO will continue and upscale the development of this technology.
Predictive Analysis of Class Attention Based on CNN Model

Abstract As the current classroom teaching reform has been integrated into every class of higher vocational colleges, modern artificial intelligence, deep learning, big data and other high-end technologies are integrated into the classroom, and classroom teaching has become more and more diverse. But the attention of modern vocational and technical students to the classroom has also received widespread attention from the society. This article uses artificial intelligence and big data technology to analyze class attention, and then uses the CNN model to predict the overall class attention. This article discusses the core literacy strategies for cultivating the efficiency of vocational and technical students in the classroom, and proposes methods such as setting suspense to guide independent inquiry, organizing learning to discover learning rules, and in-depth exploration of students’ independent learning ability, so as to increase the attention of the classroom. Based on the current popular CNN target detection algorithms can be divided into two categories. The first category is a two-stage detection algorithm, which divides the detection problem into two stages. First, generate candidate regions, and then classify the candidate regions. The typical representative of these algorithms is the R-CNN series algorithm based on region proposal. The other type is the 1-stage detection algorithm, which makes predictions for every part of the image, that is, it does not need to generate candidate regions. On this basis, a target prediction system based on the CNN model is designed and implemented, which predicts and analyzes the class attention of higher vocational colleges, uses a deep learning framework, and uses PyQt5 as an interface development framework combined with python. Experimental research results show that the designed CNN-based classroom attention model in higher vocational colleges can effectively predict and analyze students, and the effect of the practical experiment is good.
Research on video image preprocessing for monitoring abnormal behavior of mechanical operators

Working environment of mechanical operators is complex, and the amount of process monitoring video information is large, which brings serious interference for human body target recognition of abnormal behavior. The algorithm is more complex, and the reaction time is longer. Through the analysis and comparison of image RGB model, YUV model and gray processing, when segmenting video sequence image, transforming RGB color image into gray image is conducive to image recognition. When the scene background is complex, the image can be analyzed in the normalized RGB color space, which can effectively eliminate the influence of shadow. The filtering denoising of Gaussian low-pass filter and median filter are analyzed, which have good denoising effect. It is very suitable for image preprocessing and denoising after recognition. Through the analysis of morphology operation such as corrosion and expansion, the cavity problem can be effectively improved. Before the target detection, preprocessing the target video sequence by computer graphics method can highlight the useful image characteristics and remove the useless image information, which can help to improve the detection effect.
Low-cost Position and Force Measurement System for Payload Transport Using UAVs

Abstract In recent years, multiple applications have emerged in the area of payload transport using unmanned aerial vehicles (UAVs). This has attracted considerable interest among the scientific community, especially the cases involving one or several rotary-wing UAVs. In this context, this work proposes a novel measurement system which can estimate the payload position and the force exerted by it on the UAV. This measurement system is low cost, easy to implement, and can be used either in indoor or outdoor environments (no sensorized laboratory is needed). The measurement system is validated statically and dynamically. In the first test, the estimations obtained by the system are compared with measurements produced by high-precision devices. In the second test, the system is used in real experiments to compare its performance with the ones obtained using known procedures. These experiments allowed to draw interesting conclusions on which future research can be based.
Classification of Wildlife Based on Transfer Learning

Wildlife is an important biological resource in China. Classifying images of wildlife through computer technology can help people identify wildlife, which is of great significance to help people understand and protect wildlife. Therefore, this issue is worth studying. Traditional methods mostly use standard Convolutional Neural Networks (CNN) to classify wild animal images, but these methods have disadvantages such as slow computing speed, long time consumption and low accuracy. With an attempt to address such issues, this paper proposes a method based on transfer-learning for classifying wild animal images. By using the pre-trained model it can save a lot of training time. The experimental results on Oregon Wildlife, using a public wildlife data set, show that the method proposed in this paper achieved 99.01% accuracy and is 57.82% more accurate than the standard Convolutional Neural Networks (CNN) method. Moreover, in terms of running time, the method presented in this paper has achieved higher efficiency, and the training time is 50% shorter than the standard method, which proves the superiority of the method proposed in this paper.
Improved cube-attack-like cryptanalysis of reduced-round Ketje-Jr and Keccak-MAC

At EUROCRYPT 2015, Dinur et al. proposed cube-attack-like cryptanalysis on reduced-round Keccak. The process of recovering the key is divided into the preprocessing and the online phase. The preprocessing phase is setting a look-up table by computing the cube sum of involved key bits. The online phase is computing the cube sum of auxiliary variables and recording the matching values in the table as candidates. Auxiliary variables help balance the complexity of the two phases by reducing the number of involved key bits. Following this idea, a series of works has been presented, mainly focusing on a better selection of cube variables, auxiliary variables and involved key bits. We provide new methods to select auxiliary variables and involved key bits. The first step is to get a precise algebraic expression of each bit after one round permutation. Then, combined with the corresponding constraints on these variables, we can construct a Mixed-integer Linear Programming (MILP) model. Secondly, unlike the previous idea that auxiliary variables are chosen to satisfy the CP-kernel property just for the consideration of controlling diffusion, we cancel this restriction and adopt a more skilled selection of auxiliary variables. Based on these two steps, we improve the cube-attack-like cryptanalysis in terms of the complexity.
An MTF-Inspired Underwater Image Quality Assessment Method

Due to the light attenuation in water, underwater image qualities are affected and various in different imaging systems. In this paper, a modulation transform function (MTF)- inspired image quality assessment method is proposed and in which a point-by-point slanted-edge algorithm is introduced to evaluate the underwater systems. Four systems with different light sources are installed to verify the method at different distances in the same water quality. The light sources of these systems are a customized 500W lamp, biconvex lens cluster (BILC) light, Fresnel lens cluster (FNLC) light and digital light processing (DLP) compensated floodlighting, respectively. Then, experiments demonstrate the superiority of DLP compensated floodlighting than the other systems and the obtained MTF curves show that when the spatial frequency is 0.5 cy/pixel, the MTF value of the DLP imaging system at the imaging distance of 1m and 3m are 0.05 and 0.1, respectively. The experiments show that the proposed method can be used to evaluate imaging quality effectively.
Research on Database Parameters Tuning Method Based on Embedded Device

Abstract Various kinds of embedded terminal devices have enriched our lives. As a consequence, a huge amount of data has been generated, which often results in slow operation of the devices and a dramatic drop in data processing capacity. Database parameter tuning will be an important way to maintain or improve the performance of the devices. As database performance is affected by multiple parameters, manual adjustment is more and more difficult. With the development of machine learning, database automatic parameter tuning technology has become one of the main choices to solve this problem. This paper proposes to use MARS regression algorithm to optimize the database parameters of embedded devices, to perform regression prediction and fitting on each part of the data space respectively, and obtain the optimal parameter through the processes of forward, backward, and model selection. Experiments show that compared with the traditional method, the parameter optimization results generated through the method proposed in this article are very nearly the same as those obtained through the traditional method, but the time efficiency is significantly improved, hence improving the efficiency of embedded project development.
: Color decomposition-based adversarial examples for screen devices

Adversarial examples can easily fool existing powerful deep neural networks. However, we find that the attack ability of most existing adversarial attack methods is significantly degraded once the generated adversarial examples are shown on screen devices and are further captured. This is mainly attributed to two challenges: (1) Extra noises and variance during the capturing process, such as lens distortion and diverse capturing angle. (2) They get stuck in a self-contradictory problem between visual quality and attack ability. Inspired by the properties of the human visual system (HVS), this paper dedicatedly designs the first color decomposition-based adversarial example method CDAE for screen devices. Specifically, it decomposes one regular screen frame into two symmetric adversarial frames with maximum modifications while theoretically guaranteeing the visual quality perceived by human observers. Thanks to the powerful generalization ability of the proposed method, we can combine it with most adversarial example generation methods and achieve state-of-the-art attack ability. Additionally, it can also be used to protect important information from leakage and attack existing video action recognition networks.
Parallel collaborative planning for the coupled system of underground heavy-load robot

At present, designing and planning of robots are mainly based on path planning. This mode cannot meet requirements of real-time and precise planning for robots, especially under complex working conditions. Therefore, a parallel collaborative planning strategy is proposed in this paper, which parallel collaborates optimal task allocation planning and optimal local path planning. That is, according to real-time dynamic working environment of robots, the dynamic optimal task allocation planning strategy for coupled system of robot in low coupling state is adopted, to improve real-time working efficiency of underground heavy-load robot. Meanwhile, the parallel elite particle swarm optimization algorithm is adopted to improve accuracy of path tracking and controlling. Finally, the two planning strategies are collaborated parallel to realize intelligent and efficient planning of whole complex coupled system for underground heavy-load robot. The simulation and experiment results show that the parallel collaborative planning algorithm proposed in this paper has perfect controlling effects: Total flow of overall system is saved by 11.03 L, execution time saved by 16.8 s and implementation efficiency has been improved by 10 times. Therefore, the parallel collaborative planning strategy proposed in this paper can not only meet requirements of high efficiency and precision of intelligent robot under complex working conditions, but also greatly improve real-time working effectiveness and robustness of robots, so as to provide a reference for dynamic planning of complex intelligent engineering machinery, and also supply design basis for development of multi-robot collaborative system.
Examining and Predicting Helpfulness of reviews based on Naive Bayes

Abstract Online reviews give a significant asset to potential clients to settle on buy choices. Be that as it may, the utmost extent of accessible reviews just as the enormous varieties in the review standard present a major hindrance to the compelling utilization of the reviews, as the extreme accommodating reviews might be covered in the huge measure of poor-standard reviews. The objective of this framework is to create models and calculations for anticipating the supportiveness of reviews, which gives the theorize for finding the extreme accommodating reviews for given items. We initially exhibit that the supportiveness of reviews depending upon the three significant variables: the commentator mastery, the composing approach of review, and the practicality of reviews. Contrasted with different all around examined conclusion investigation and feeling rundown issues, less exertion has been made to break down the nature of online reviews. The target of this paper is to fill right now naturally assessing the “supportiveness” of reviews and subsequently creating novel models to distinguish the most accommodating reviews for a specific item.
An Image Multi-scale Feature Recognition Method Based on Image Saliency

Image recognition refers to the technology which processes, analyzes and understands images with computer so as to recognize various targets and objects of different patterns. To effectively combine image recognition and intelligent algorithm can enhance the efficiency of image feature analysis, improve the detection accuracy and guarantee real-time detection. In image feature recognition, the following problems exist: the description of accurate object features, object blockage, complex and changeable scenes. Whether these problems can be effectively solved has great significance in improving the stability and robustness of object recognition algorithm. This paper takes image salience as the fundamental framework, and makes in-depth study of the problems of effective object appearance description, multi-feature fusion and multi-feature adaptive combination. Then it proposes an image multi-scale feature recognition method based on image salience and it can better locate the saliency object in the image, and more evenly highlight the salient object and significantly suppress background noises. The experiment results prove that salient region detection algorithm can better stress the entire salient image.
Recommendation System Journalist For Getting Top News Based On Twitter Data

Abstract One application that can be utilized in finding the latest news is by utilizing the development of information and communication technology such as seeing the delivery of public information through social media such as twitter. This study implements the system which is able to get the trending or top news based on twitter data. This information will connect to the journalist via mobile application. It helps journalists to get the potential news to be reported in their media. The application will group each tweets into various themes based on the cosine similarity. The tweets which are more the certain threshold, can be indicated as the top news. The top news is then classified into one category news by using SVM algorithm. The training data becomes from the source data of one media news. The specific topic of this result will ease to order the journalist cover this top news. The system can be implemented well.
One-hour Ahead Solar Irradiance/Power Forecasting Using Radial Basis Function Neural Network with Fuzzy Activation Function

Due to increasing awareness of global warming, solar photovoltaics have received much attention. However, solar photovoltaic power generation is associated with intermittence and uncertainty as a result of the meteorological conditions. Accordingly, accurate predictions of the power output from photovoltaic arrays is important for the efficient operation of power systems. This paper presents a supervised learning-based Radial Basis Function Neural Network (RBFNN) neural network for 1-hour ahead solar irradiance/power forecasting. The proposed RBFNN employed double-Gaussian functions as its basis functions, which are type-II fuzzy activation functions. It was found that the type-II fuzzy activation is able to deal with the uncertainty of data. The genetic algorithm was used to optimize the weighting/bias parameters as well as two means/standard deviations of each double Gaussian function. In order to explore the performance of the proposed RBFNN, three structures of RBFNNs are examined: parallel, cascaded and separated RBFNNs. From the simulation results, it was found that the proposed parallel RBFNN outperforms the cascaded and separated RBFNNs. Moreover, the proposed RBFNN with double-Gaussian activation functions attains better accuracy than traditional multi-layer feedforward neural network and RBFNN with single-Gaussian activation functions.
Local Path Planning Algorithm of Mobile Robot by Modified Optimized Parameters Method

Path planning algorithm is an important part that affects the navigation effect of autonomous mobile robot. In order to improve the navigation effect, many scholars have studied SLAM and path planning algorithm. However, few people have studied the evaluation parameters of local path planning algorithm Dynamic Window Approach. Aiming at the problem of selecting and assigning evaluation parameters of the local path planning algorithm DWA, this paper presents a new set of solutions. The optimal parameter combination is solved through a combination of simulation and experiment. The results show that the obtained parameter combination can be more smoothly and efficiently in the process of mobile robot navigation path.
To Enhance Security Mechanism in ATM Account Using Biometric System

Abstract The services we use on our day-to-day life like ATM will raise a confidence that it is safe to use for daily transactions that have withdrawals, balance enquiry and take care of all other transactions ATM machine reads the card, which could be Savings or Current accounts. After verification process one can withdraw or transfer or deposit the amount. In such situations, some fraud may occur where some individuals notice others ATM account details and perform transaction process. To overcome such disturbances Biometrics can be applied. Every individual can perform his/her transactional process with Two step verification process i.e., with pin number and biometric verification process. In this way, for some extent the ATM account fraud can be controlled.
Optimized Data Compression through Effective Analysis of JPEG Standard

Data compression is the process of encoding the information with fewer bits than the original information in order to get more throughput and accommodate less bandwidth. JPEG has been the popular image compression technique along with its variant for different applications. With a little modification to the traditional JPEG quantization tables, exhilarating results can be obtained without much compromise in the image quality. In this paper the performance of JPEG is analyzed with the help of modified quantized tables used in the quantization process. The image quality analysis parameters such as Peak Signal to Noise Ratio, Mean squared error and Structural Similarity Index Measure along with the compression ratio as the function of different quality factors obtained by the modified quantized tables are measured.