Reviewerly/exHarmony huggingface dataset:
Incorporating Frame Image and Frame Sequence into Ensemble Learning Networks to Improve the Accuracy of Physical Bullying-Detecting Model

Abstract In this research, a convenient, reliable, and fast system is built for violence detection in schools. This system includes two independent model designed separately for detecting violence in videos and pictures. This ensemble learning structure allows the system to be more accurate and less dependent on the background, or, minimize the influence of changing circumstances. The reason is that the image-identifying network did better on classifying the image, while the video-identifying network can minimize the influence of the background by using optical flow. Therefore, by combining them together the total accuracy increases and the dependency on environment is minimized. The video-identifying model was based on Darknet19, ResNet, optical flow and LSTM. Optical flow could eliminate the background’s influence by extracting moving features. The image-identifying model was based on ResNet. ResNet was tested and selected from 3 different popular networks due to its high accuracy. The system was trained and tested on data from various datasets online as well as different videos and pictures from multiple sources. The final system achieves an accuracy of 90.90% from multiple-sourced videos, and is visualized for a more straight-forward result. The final test shows that the system not only has a high accuracy, but also can process data with an astonishing speed of 75 FPS (frames per second), which is about 3 times faster than normal videos. All of these imply a high practical value of the final system.
Three-dimensional Localization of Needle Tip Immersed in Medium

Pick and place through micro manipulators has been an indispensable tool for a variety of biomedical applications. In tissue engineering, 3D tissue can be fabricated through the stacking of multiple cells sheets together. To enable precise handling and alignment of cell sheets, accurate detection of a probe tip, which could experience wave distortion from the culture medium during tip manipulation is very important. In this paper, the 3D position of a tip is evaluated using a vision system. Based on 2D images from consecutive frames, the kernel describing the effect from motion is first computed using a motion blur model. Then, an auto regressive (AR) method is employed to extract the scale factor corresponded to the depth of Z coordinate. Parameters in the model are recursively updated using Unscented Kalman Filter (UKF) algorithm. By finding the tip position in the 2D image, its depth information can be found accordingly. Experiments were conducted to examine the performance and accuracy in evaluating the position of a tip immersed in an aqueous environment.
Individually Separated Wireless Access Point to Protect User’s Private Information

Due to the miniaturization, diversification, and massification of devices, the amount of installation of wireless AP (Access Point)s has increased, and the users can utilize the Internet by connecting their devices such as laptops, mobile phones, and tablet PCs to wireless APs when desired. However, there has been a big concern about the threat to steal user’s personal information from the devices connected to the wireless APs. People think this can be prevented by using the encrypted channel of the wireless AP, but the passwords of the wireless APs used in coffee shops, restaurants, or public places are null or a shared-single, so that the malicious user can also obtain such a password and the user’s personal information may be easily exposed. In this paper, we propose a protection scheme for user’s personal information delivered within the domain of a wireless AP and we introduce a method using a POS (Point of Sale) system for the scheme. A random SSID and password are generated through the POS system, and a user’s dedicated AP is generated based on this. Users can register their SSID and password with the device to protect them from the threat of personal information leakage by malicious users.
Research on Detection Methods of Driving Postures Based on Deformable Component Model

In order to enhance the accident prevention ability of vehicle active safety system and driving safety warning system, the driving attitude region detection method based on deformable component model is studied, and the detection method based on direction gradient histogram feature and support vector machine, the detection method based on Haar feature and Adaboost, and the detection method based on local binary mode feature and Adaboost are compared and analyzed. The experimental results show that the detection method based on deformable component model is better than the other three detection methods, and its detection accuracy is up to 97.5%.
Research on high precision image measurement system

With the rapid development of computer technology, image measurement technology has been widely used because of its non-contact, high precision and other advantages. In recent years, potable consumer devices like mobile phone, VR/AR device, tablet computer etc. become more and more popular. The number of high precision small workpieces has increased exponentially. There is an urgent need for the high accuracy and high speed measurement approach for the quality control of the small workpieces. Due to the contradiction between accuracy and field of view, the existing image measuring instruments are not sufficient, and it is difficult to meet the needs of high precision and high efficiency measurement. In this paper, several aspects of high-precision image measurement system are explored, including hardware system construction, double side telecentric lens design, high-precision template matching technology, system calibration, sub-pixel feature extraction and image segmentation algorithm. According to the actual demand, a high precision detection system is integrated, a high-precision image rapid measurement system for small workpieces (size &lt; 70mm) is designed and developed. The accuracy of 2um and speed of 7000UPH are obtained which can meet the industrial requirements.
Generating Adversarial Samples with Convolutional Neural Network

Deep learning has become a hot research direction in the field of computer vision, and has been widely applied in the fields of intelligent transportation, intelligent security and so on. Because deep learning is vulnerable to adversarial samples, therefore poses a great threat to some safety-sensitive applications such as autonomous driving. In order to study the application of convolutional neural networks in adversarial sample generation and to lay the foundation for future research adversarial sample characteristics, we propose a convolutional neural network for generating adversarial samples, which can successfully fool the deep learning model.
Depth information calculation method for unstructured objects based on deep neural network

Depth information perception of unstructured scene images is an important problem for applications using computer vision. This paper proposes a method based on deep learning combined with self-attention mechanism to reason the depth information of unstructured indoor targets, which effectively solves the problem of blurred image detail and insufficient layering in depth information reasoning in unstructured scenes. First, the deep learning-based encoder-decoder model is trained to learn the depth information of indoor scenes on large 3D datasets. The trained model has good results for general structured indoor scenes. Secondly, the soft self-attention mechanism is used to obtain the disparity information between the upper and lower sequences of the input image, by which the depth map obtained in the first step is corrected to enhance the accuracy of depth. Finally, in order to get clear objects with obvious boundaries in the depth response map, the nearest neighbor regression is used to correct the contour of the objects. The experimental results show that the proposed method has very good depth information reasoning ability for indoor unstructured scenes. Through depth information reasoning, the obtained objects have obvious texture structure, strong geometric features, clear contour edges and delicate layers, and also the misleading of deep information reasoning in reflective and highlight areas is eliminated.
Learning Essential Region Mask In Spatial Dimension For Saliency Detection

Visual saliency detection is a fundamental problem in both cognitive and computational sciences including computer vision. With VGG-16 net as backbone of our inference network, a fundamental point is to learn representative multi-scale response and multi-level integration. Moreover, because of the character of saliency detection, learning an attention mask is constructive for improving the preference. To learn a superior saliency detection model, we introduce a neural network architecture emphasizing multi-level integration and attention application. We design a spatial attention mask generator cooperating with channel attention to produce representative features. With a Inference method with refinement, we can get results with respectable performance. Experimental results demonstrate that our proposed method is capable of achieving superior performance on all public benchmarks, performing well on each dataset by comprehensively utilizing global features and local features.
Research on Watermark Tampering Blind Recognition Algorithm Based on Recognition Factor

As an important protection method of digital copyright, whether digital watermarking technology can effectively identify tampering attacks and protect watermark information is an important research topic in the field of digital copyright. In this paper, a dual watermarking algorithm based on discrete cosine transform and discrete wavelet transform in gray space is designed. The algorithm embeds a kind of identification factor while embedding a robust watermark, which can intuitively judge the attack mode of the image and has good robustness. Simulation experiments show that the algorithm can effectively protect the watermark information after being subjected to different tampering and attack, and solves the problem that a single watermark cannot have two characteristics.
Exploring mod 2 <i>n</i>-queens games

Abstract We introduce a two player game on an n × n chessboard where queens are placed by alternating turns on a chessboard square whose availability is determined by the parity of the number of queens already on the board which can attack that square. The game is explored as well as its variations and complexity.
A study on depth map generation using a light field camera and a monocular RGB camera based on deep learning

A depth map and an RGB image taken by a light field camera for training data are arranged in a dataset pair; the datasets are learnt through a deep learning method called pix2pix, which is a type of conditional generative adversarial network. We can generate depth maps using only a monocular mobile camera without the light field camera based on our proposed method. Low accuracy on depth is a technical issue for the light field camera; however, the proposed method improves the depth accuracy due to the generalization ability of neural networks.
Joint Caption Detection and Inpainting Using Generative Network

Video decaptioning is the task of removing overlayed text in the video sequences and filling it with coherent and semantically meaningful content. In this work, we propose a generative CNN to do caption detection and decaptioning task jointly in an end-to-end fashion. Our network does frame level decaptioning and thus the same network can be adapted to do image decaptioning or other similar tasks. Our network is capable of inpainting video frames that contain text overlays in various size, background, color, and location. All the experiments were performed on the dataset provided in the ECCV’18 Satellite Workshop Chalearn LAP Inpainting Competition Track 2 - Video decaptioning. We also secured the third rank in the competition. Most of our work is inspired by previous work on image generation and image inpainting.
SAR village targets extraction and heterogonous image registration

Since the signal-to-noise ratio of synthetic aperture radar (SAR) image is low, it is difficult to match SAR images with optical images accurately. A new method of SAR village targets extraction and heterogonous image registration is proposed in this study. Median filter and partial differential method are first used to remove speckle noise; next, the grey level co-occurrence matrix algorithm is used by calculating four typical texture parameters of it to obtain preliminary regions of interest (ROI) of village targets and false alarm areas are cleared by proposed local-grey-level congruency method; then proper numbers of Fourier descriptors and morphological algorithms are used to describe ROI again to maintain the boundary features of targets better. Finally, HU moment invariants, which is combined with Euclidean distance and cosine similarity measurements, are utilised to achieve heterogonous image registration. The experimental results show that the root-mean-square-error of the registration image is small and the heterogonous registration image is accurate, demonstrating that the authors’ method is stable and accurate, and owns certain research value and broad application prospect as well.
Investigating users<b>'</b> affective load in collaborative search

ABSTRACT This paper draws on affective load theory to explore the characteristics of users' affective load in collaborative searching. In this experimental study, a total of 32 participants were recruited and divided into 16 pair‐groups. Each pair‐group collaboratively completed three search tasks in the Coagmento system. Users reported more irritation than anxiety, frustration, and rage. Moreover, the greater a task's perceived difficulty, the higher the irritation, anxiety and frustration felt by users. Users' affective load was higher when they spent more time on a search than they expected to. Conversely, their sense of retrieval success, self‐motivation to search contributed to reducing users' perceived affective load, but not as much as satisfaction with collaborative search results, and satisfaction with collaborative task outcomes did.
How to Combine Virtual and Reality in Archaeology Communication

Within digital archaeology, an important part is centered on technologies that allow representing, or replaying, ancient environments. It is a field where scientific competences' contribution to contents makes a difference, and pedagogical repercussion are stimulating. Among the other reality technologies, the Mixed Reality, giving the possibility to experience in front of the users' eyes both static models of individual objects and entire landscapes, it is increasingly used in archaeological contexts as display technology, with different purposes such as educational, informative, or simply for entertainment. This chapter provides a high-level overview about possible orientations and uses of this technology in cultural heritage, also sketching its use in gaming within the role of gaming itself in smart communication of archaeological contents and issues.
A Fast Image Matching Approach for Real-Time Embedded Systems

With the development of real-time embedded systems, image matching based on binary feature descriptors is getting more and more attention for the needs of devices' low computation complexity and lower memory. However, the main approaches such as BRISK and ORB cannot well juggle the speed and robustness. In this paper, a special and efficient image matching approach for real-time embedded system based on combined algorithms was proposed. For the target of faster performance, CenSurE algorithm adopting STAR filter instead of BLoG filter was chosen as features detector and FREAK algorithm using Mean filter to replace Gaussian filter was introduced in features description. The integral image processing method also plays important roles in operation. Moreover, faster matching strategy named multiple hierarchical clustering trees was applied to deal with massive binary features matching. The comparative experiments for viewpoint, blur, illumination, rotation and scale indicate that our image matching approach possesses excellent speed and robustness.
Message and Image Encryption Embedding Data to GF(2m) Elliptic Curve Point for Nodes in Wireless Sensor Networks

This work proposes encryption of text data and image by embedding data as elliptic curve point. Elliptic curve cryptography (ECC) is selected to satisfy the constraints of wireless sensor networks due to its high security for smaller key bit length. Finite field arithmetic is utilized efficiently in this reconfigurable cryptosystem. MATLAB is employed for pre-computations for text data and image input conversion. This architecture is tailored for cryptographic applications specifically consisting of cost-efficient Xilinx Spartan-xc3s100e-4-fg320 FPGA with Verilog coding. Time and area constraints are analyzed and compared with previous implementations considering both the approaches. The total encryption and decryption time results are around 10.09021 μs for 100 × 100 images and 0.029 μs for a message. Computational and combination path delay is not observed in any module design implementation. The dynamic mapping of input data and a cipher image with high randomness indicate the good security, i.e., less vulnerable to attacks. To evaluate the strength of the proposed method, an entropy statistical analysis is performed on plain and encrypted images. As per authors’ knowledge both data and image mapping on the elliptic curve are considered for the first time to utilize in wireless sensor nodes.
Analysis of the Relationship Between Psychological Manipulation Techniques and Personality Factors in Targeted Emails

The damage from targeted email attacks continues to be an acute issue for Internet users. Several recent studies have demonstrated that psychological manipulation techniques (e.g. Cialdini’s principles) are used effectively in phishing mails, the susceptibility to Cialdini’s principles correlates with the personality factors (so-called Big Five), and the use of AI can serve to facilitate the assessment of the personality factors based on social media information. Based on the results outlined in the aforementioned studies, this paper considers the possibility of a new type of attack that uses open source intelligence (OSINT) tools to obtain social media information about the target and then misuse personality estimation tools and psychological manipulation techniques to create malicious emails with a highly effective level of psychological manipulation for each specific target. In this paper, to estimate the possibility of such attack, investigation and analysis in relation to such questions as whether Cialdini’s principles work in targeted emails, and whether the effectiveness of Cialdini’s principles in targeted email correlates to personality factors was performed through conducting a user experiment.
The challenges of fake news and need of media literacy

A credible media and information system is prerequisite for sustainable growth of any society. It becomes more important when 65% population of the country is below the age of 35 years. In the present media environment, which is full of misinformation, distorted facts and biased opinions, the task becomes more difficult. The quantum of fake news in circulation becomes a big challenge for them because they neither have skills nor knowledge to distinguish the difference between true and false news. A big predicament is that they are part of a semi-literate society which is more vulnerable to the rumors and fake news. In addition to that, socio-political environment of the country has worsened the situation. Since media has become a battleground for the political fights, rumor and fake news are becoming lethal weapons. The problem has aggravated due to the increasing use of social media platforms like Facebook and WhatsApp both by the politicians and political parties during elections. As we know that there are no gatekeepers in social media and also there is also no system of checks and balance there, it is a bigger threat than mainstream media. In this scenario, the role of media literacy becomes very important for sustainable growth and development. But it has its own challenges as well. Hence, this study focuses on how to make the young generation media literate.
A Practical Way to Assess the Perceptual Information Loss of Tone-mapped Images

Due to the standard dynamic range (SDR) feature of those widely used displays, high dynamic range (HDR) images need to be tone-mapped for its visualization. Tone-mapping operators (TMOs) convert HDR image to different SDR images, thus the perceptual information loss of these tone-mapped images during the process of tone-mapping become a natural question. In this work, an objective method based on measuring the entropy loss between HDR and SDR images is proposed. We tested several different approach of this method, and determined which is better by calculating their correlation with subjective mean opinion score (MOS) conducted in a tone-mapped images databased (TMID). This method is then compared and combined with other full-reference tone-mapping image assessment methods: TMQI, FSITM and CDMI. Only linear combinations of these methods are tested since this method is a practical way, and the combination with best correlation was selected as the final quality score.
Forensic Analysis Recognition

Automatic systems for the analysis of textual information are highly relevant for the sake of developing completely autonomous assessment and authentication mechanisms in online learning scenarios. Since written texts within e-learning systems are mostly associated with exams and evaluations, it is critical to authenticate the identity of participants. Although there are some efforts from the natural language processing community to develop automated tools for assessing education in a very high semantic level (e.g., to provide feedback in argumentative tasks, to evaluate coherence, etc.), solutions are still far from being implemented because of the complexity of the associated problems and the unstructured information contained in written language. Efforts on authentication mechanisms, on the other hand, are more promising to be implemented in e-learning platforms because of the bulk of work devoted to the analysis of authorship. Despite this fact, it is only recently that authorship analysis is being started to be used in e-learning scenarios. This chapter provides an introduction to authorship analysis as a tool for performing forensic analysis, emphasizing the application of this sort of methods in e-learning scenarios. State of the art representations for text analysis are evaluated and compared in the authorship verification task. The goal of these experiments is to determine the best way to represent texts in the context of authorship verification. Finally, an overview of open challenges and research opportunities in this direction are outlined and briefly discussed.
A Configurable Implementation of the SHA-256 Hash Function

This paper proposes a hardware solution for the SHA-256 hash function offering a number of configurable architecture-level features. This flexibility allows for exploring various trade-offs between performance, area occupation, and power consumption. As confirmed by the experimental results, the approach succeeds in exposing the effects of different architectural configurations on the resulting implementation.