Reviewerly/exHarmony huggingface dataset:
Char-word fusion method for Chinese NER

The task of Chinese named entity recognition (CNER) is closely related to Chinese word segmentation, because most Chinese entities are composed of words. The CNER model with words as the minimum input unit is called word based CNER model. However, wrong word segmentation will lead to wrong entity recognition results, so the CNER model with single word as input (char based) has gradually become the focus of research. Recently, many efforts have begun to integrate external word information to improve the performance of word based CNER model. And softlexicon are excellent word information fusion methods. When using softlexicon method for word fusion, too much matching word set information will be introduced, resulting in additional "noise" interference, resulting in entity recognition errors. Aiming at the problems of softlexicon method, this paper proposes a word information Selected-Softword-Concat method (SSC), which effectively solves the problem of "noise" in softlexicon method by introducing word segmentation information. The experimental results on four general CNER data sets show that compared with softlexicon method, the F1 value of the model constructed by SSC method is increased by 0.94, 0.92, 0.78 and 0.25 respectively, which effectively solves the "noise" problem of softlexicon.
Destroy the Robust Commercial Watermark via Deep Convolutional Encoder-Decoder Network

Nowadays, robust watermark is widely used to protect the copyright of multimedia. Robustness is the most important ability for watermark in application. Since the watermark attacking algorithm is a good way to promote the development of robust watermark, we proposed a new method focused on destroying the commercial watermark. At first, decorrelation and desynchronization are used as the preprocessing method. Considering that the train set of thousands of watermarked images is hard to get, we further use the Bernoulli sampling and dropout in network to achieve the training instance extension. The experiments show that the proposed network can effectively remove the commercial watermark. Meanwhile, the processed image can result in good quality that is almost as good as the original image.
Data Acquisition from Cloud Network Storage

forensic actions are taken to determine causes of system failure and to diagnose network security breaches. The challenge and problem for network investigators is that many of the data repositories are now virtualized and Cloud distributed. The requirement is to devise effective and systematic methods for data acquisition that are robust in the new networking contexts and sufficiently comprehensive for fault determination. This paper reports the extraction of evidence from virtualized RAM in the Cloud context on a virtual machine. Such evidence informs network system fault correction, and attack diagnosis. The contribution of this research is to promote an awareness of valuable evidence held in Cloud virtual machines, where it is located, and the extraction tools. The new data collection scope for network investigators is thus demonstrated in the Cloud network context.
Robustness of multipartite entangled states for fermionic systems under noisy channels in non-inertial frames

Abstract We investigate robustness of bipartite and tripartite entangled states for fermionic systems in non-inertial frames, which are under noisy channels. We consider two Bell states and two Greenberger-Horne-Zeilinger (GHZ) states, which possess initially the same amount of entanglement, respectively. By using genuine multipartite (GM) concurrence, we analytically derive the equations that determine the difference between the robustness of these locally unitarily equivalent states under the amplitude-damping channel. We find that tendency of the robustness for two GHZ states evaluated by using three-tangle τ and GM concurrence as measures of genuine tripartite entanglement is equal to each other. We also find that the robustness of two Bell states is equal to each other under the depolarizing, phase damping and bit flip channels, and that the same is true for two GHZ states.
SVDN: A spatially variant degradation network for blind image super-resolution

Blind super-resolution (SR) aims at generating a high-resolution image from a low-resolution image where the degradation kernels are unknown. Most existing blind super-resolution approaches apply an estimated blur kernel as a degradation prior to the entire image. However, due to the intrinsic degradation characteristics of camera lens or different depth-of-filed in the image, the degradation kernels in different regions are not exactly the same. To address this issue, we propose a spatially variant degradation network (SVDN) for blind SR with a pixel-wise kernel estimation block to handle the complex and unknown degradations in blind SR tasks. Moreover, to fully exploit the estimated degradation information, the output of network is influenced by the estimated kernel via an affine transformation applied to the feature maps in each middle layer. The proposed method permits a lot of freedom in adapting the image degradation locally. Extensive experiments on synthetic and real-world images demonstrate that the proposed method generates more accurate SR results under the complex degradation settings and provide naturally visual results both on the in-focus and out-of-focus regions of the images.
Single-Image Dehazing

Image dehazing is a useful technique which can eliminate the bad effect of haze on images and enhance the performances of image/video processing algorithms in hazy weather. In this chapter, we first propose a single-image dehazing method. We estimate the initial transmission properly based on latent region segmentation and refine the estimated initial transmission by an objective function with a novel weighted L1-norm regularization term. The half-quadratic splitting minimization method is employed to solve this optimization problem. We also define an evaluation function to estimate the reliable global atmospheric light. With the refined transmission map and atmospheric light, we recover the haze-free image by the haze imaging model. We then propose a retinex-based decomposition model for a hazy image and a novel end-to-end image dehazing network. In the model, the illumination of the hazy image is decomposed into natural illumination for the haze-free image and residual illumination caused by haze. Based on this model, we design a deep retinex dehazing network (RDN) to jointly estimate the residual illumination map and the haze-free image. Our RDN consists of a multiscale residual dense network and a U-Net with channel and spatial attention mechanisms. The multiscale residual dense network can simultaneously capture global contextual information from small-scale receptive fields and local detailed information from large-scale receptive fields to precisely estimate the residual illumination map caused by haze. And in the dehazing U-Net, we apply the channel and spatial attention mechanisms in the skip connection to achieve a trade-off between overdehazing and underdehazing by automatically adjusting the channel-wise and pixel-wise attention weights so as to obtain a refined haze-free image.
Design of Reconnaissance Image Compression System Based on Semantic Segmentation

In order to ensure the real-time processing of reconnaissance images and achieve complete and accurate acquisition of “high-value information” in images based on limited wireless channels, the paper proposes a scheme for reconnaissance image compression system based on semantic segmentation. The scheme proposes an image compression method: according to the task requirements, the method selects the corresponding semantic category of the target to perform semantic segmentation on the reconnaissance image, and performs hierarchical and lossless compression on the semantic segmentation result according to the importance of the target and then transmits it. And the method is combined with the existing direct compression and ROI-based image compression methods to achieve the purpose of obtaining high-quality reconnaissance information based on the limited wireless channel capacity under different mission requirements.
Comparison Analysis of Acorn Algorithm and Snow Algorithm on Smart Card using Java Card

Data theft and manipulation are threats that often occur in the use of smart cards. The data stored on the smart card will be changed when the data exchange is carried out, so that data with different contents will be received by the recipient. Cryptography is a solution that can be used to secure data on a smart card so that its contents cannot be changed. Acorn's algorithm is an algorithm that can guarantee its security aspects in terms of confidentiality and authentication. Snow's algorithm is an algorithm that is designed to be implemented on hardware. Acorn's Algorithm and Snow's Algorithm can be implemented on resource-limited devices such as smart cards because both algorithms are lightweight. The encrypted data on the smart card is the user ID and user balance, the data will be converted into ciphertext so that it cannot be read or changed by unauthorized parties. Test vector testing, and encryption and decryption performance testing are carried out to ensure the system can run properly. The results of the average performance of encryption and decryption time in the Acorn algorithm are 5.3ms of encryption time and 5.9ms of decryption time, while the meantime of encryption in the snow algorithm is 5.4ms and 2.4ms of decryption time.
Fully content-based IMDb movie recommendation engine with Pearson similarity

With the advancement of technology and the updating of information, more people choose to watch movies on the Internet. In the world of brust data, users often encounter the difficulty of finding their favorite movies. Implementing the movie recommendation system of the media platform is one of the most effective ways to solve this problem. In order to avoid the imbalance of user data in the actual operation process, the content-based recommendation model is adopted in this research, aiming to find their similar variables in each movie, to calculate similarity index between each movie through the matrix and Pearson formula. The advantage of this method is that the potential interest of users for each movie can be discovered, and the probability of the movie being recommended will not be caused by the problem of the popularity of the movie.
Robot-assisted Breast Ultrasound Scanning Using Geometrical Analysis of the Seroma and Image Segmentation

In this paper, we propose a robotic ultrasound imaging method that scans the breast in two separate phases to acquire high-quality ultrasound images. Our proposed system controls five Degrees of Freedom (DoFs) of the robot that hold an ultrasound probe to perform precise scanning. This system finds the desired trajectory based on geometrical analysis of the target inside the breast in a pre-scan phase and uses this information to control the probe in a post-scan phase. The proposed method updates the desired values of rotational and translational movement of the probe in the post-scan by calculating the center of mass of segmented target in each acquired frame and the average of image confidence map. The proposed method has been tested experimentally on a plastisol phantom. Given a specific trajectory, the position and orientation of the probe have been controlled at each point of the trajectory. The experiments’ result shows us that our proposed visual servoing algorithm successfully controls the probe to look at target tissue and is fast enough for use in a robotic control loop.
The Emergency Remote Teaching: A State University Experience

Due to the COVID-19 pandemic, emergency remote teaching in higher education institutions across Turkey in 2020 has become mandatory as of March. Emergency remote teaching practices added new applications for most of the instructors such as virtual classroom, online teaching, distance evaluation; those revealed different opinions based on experience on the positive and negative aspects and quality of the applications. This research was conducted to evaluate all these experiences held at a state university with a distance education center and a distance education database system but having only limited experiences on online courses before the emergency remote teaching in Turkey. The data set of the study was composed of the opinions of a group of instructors who participated in the distance teaching process for the first time and the official correspondence and reports made to organize, manage and evaluate the process during the first period of emergency remote teaching. The research is a qualitative case study where data obtained from different data sets are analyzed comparatively, inductively, and deductively. The results of the research show that within the scope of emergency remote teaching, the regulations regarding the application content in different faculties of the university should be differentiated, and this can be partially met during the emergency distance education process. In the context of effective and efficient use of the emergency remote teaching process, positive/negative situations have been experienced in terms of adapting to the database used in the teaching process, adapting existing teaching approaches, measuring and evaluating learning outcomes, and ensuring the participation of students in all these. Although instructors generally consider distance education as more student-centered practices and find the opportunities, it will provide positive in this context, but the effect of compulsory emergency remote teaching on learning outcomes was uncertain. Some suggestions are presented for the efficacy, effectiveness, and efficiency of the implementation.
Self-Calibrated Contact-less Touch Recognition based on Finger Depth Displacement Detection

We used Leap Motion with processing language to develop a contact-less touch-pad. Leap Motion is based on gestures expressed by hands and fingers. It helps reality affect other digital devices easily and directly. To achieve a dynamic 3D input in the reality, rather than simply documenting movement, it is necessary to investigate the fingers’ actual location of fingers and their location relative to the device. Through the relationship between the two positions, continuous environments can be described as compositions of discrete coordinates. Thus, changes in position can be considered gestures. Using this method, devices can respond to finger positions as well as gestures. The proposed method, in which the cursor follows a particular finger location, can replace the mouse by using existing example code in Leap Motion. Such a cursor made by processing language can move and click just like a mouse. In other words, Leap Motion can play the same role as a physical ATM keypad. The completed keypad will facilitate contact-less activity by replacing many machines that include keypads.
G3AN++

Video generation task is a challenging problem which involves the modelling of complex real-world dynamics. Most of the existing methods have designed deep networks to tackle high-dimensional video data distributions. However, the utilization of wide networks is still under explored. Inspired by the success of wide networks in the image recognition literature, we present G3AN++, a three-stream generative adversarial network for video. The three streams are spatial, temporal and spatio-temporal processing branches. In pursuit of improving the quality of video generation, we make our network wider by splitting the spatial stream into two parallel identical branches learning complementary feature representations. We further introduce a novel adaptive masking layer to impose the complementary constraint. The masking layer encourages the parallel branches to learn distinct and richer visual features. Extensive quantitative and qualitative analysis demonstrates that our model outperforms the existing state-of-the-art methods by a significant margin on Weizmann Action, UvA-Nemo Smile and UCF101 Action datasets. Additional exploration reveals that G3AN++ is capable of disentangling the appearance and motion. We also show that the proposed method can be easily extended to solve the hard task of text-to-video generation.
Non-Mathematical Musings on Information Theory and Networked Musical Practice

Claude Shannon’s 1948 paper ‘A Mathematical Theory of Communication’ provided the essential foundation for the digital/information revolution that enables these very pixels to glow in meaningful patterns and permeates nearly every aspect of modern life. Information Theory, born fully grown from this paper, has been applied and mis-applied to a multitude of disciplines in the last 70-odd years, from quantum physics to psychology. Shannon himself famously decried those jumping on the ‘scientific bandwagon’ of Information Theory without sufficient mathematical rigour. Nevertheless, having a brief personal connection to Dr Shannon (and being extremely grateful for it), I will take the liberty of colouring some of my experience with computer network music with less-than-rigorous insights gained from his work.
Conceptual Design Using Virtual Reality: Case Study with Portable Light

In the creation of new products, the conceptual design is the key to generating valid and competitive ideas. In this paper, which applies Virtual Reality (VR) to the generation of new concepts, it is explored the generation of previous ideas through the modelling of virtual sketches. For this purpose, Gravity Sketch software has been used as a tool for conceptual design. Gravity Sketch allows the functional evaluation of the product, as the concepts are generated as 3D models that can be tested in the immersive VR environment. The case study focuses on the design of portable light concepts made in VR, which had subsequently been analysed and tested with several users who have entered the virtual environment. Users had been able to visualise the concepts and interact with them to test the product's grip and operation. The results obtained in this immersive study had been valuable for the final development of the product. Furthermore, the conceptual design applying VR technology allows for intuitive designs derived from the three-dimensional environment, reducing design time and the costs associated with prototyping.
Analysis of Information Island in Civil Aviation Industry under Epidemic Situation

The problem of information island in civil aviation industry has existed for a long time, especially under the epidemic situation. This paper analyzes the causes and effects of information island in civil aviation industry, and clarifies the necessity and urgency of solving the problem. Taking the airline operation control system as an example, this paper proposes to build an integrated information platform based on data island, application island and business island, in order to solve the problem of information isolated island in civil aviation industry and help civil aviation industry information construction develop with high quality under the epidemic situation.
Groups Allocation Based on Sentiment-Epistemic Analysis in Online Learning Environment

Collaborative learning methods in an online learning environment, encourages students to interact actively among themselves in a work group. Instructors or lecturers need to combine potential students to work together as a group, but this task is not easy since the characteristics of a student are sometimes not explicitly known. In this preliminary research, we propose a solution to answer this problem. Our methodology is composed in three steps. It begins with the sentiment analysis process with a textual history of online conversation or discussion. The next step is to classify the text into one of predefined epistemic groups. Further, we visualize the model in an epistemic network graph which is based on singular value decomposition. The group allocation is built based on k-means clustering. The case study in this paper is related to information technology-based subjects, and thus we classify our sentiment-epistemic analysis in three collaborative aspects, i.e.: project management, attitude and technology affinity. Our results show that by combining sentiment-epistemic analysis and k-means clustering, a holistic group allocation can be produced which would be beneficial in a collaborative learning environment.
Real Time Human Activity Recognition Using Acceleration and First-Person Camera data

The aim of this work is to present an automated method, working in real time, for human activity recognition based on acceleration and first-person camera data. A Long-Short-Term-Memory (LSTM) model has been built for recognizing locomotive activities (i.e. walking, sitting, standing, going upstairs, going downstairs) from acceleration data, while a ResNet model is employed for the recognition of stationary activities (i.e. eating, reading, writing, watching TV working on PC). The outcomes of the two models are fused in order for the final decision, regarding the performed activity, to be made. For the training, testing and evaluation of the proposed models, a publicly available dataset and an "in-house" dataset are utilized. The overall accuracy of the proposed algorithmic pipeline reaches 87.8%.
Toward object alphabet augmentation for object detection in very high‐resolution satellite images

Summary Object detection in very high‐resolution satellite images has become an important tool in many fields. So there is a vital need to build a more precise and accurate detector. There is a great challenge to detect very small and condensed objects with their different semantics shapes. Recently, convolutional neural networks with different structures have achieved awesome performance. We propose object alphabet augmentation method that extracts object instants during the training process to form an alphabet for these objects in a newly generated dataset. The proposed method uses the DarkNet‐53 framework to get the main features for each object using two new generated datasets for alphabet augmentation up‐down where objects are placed in up down, and alphabet augmentation horizontal where objects are placed in their original setting. Experiments are conducted on datasets randomly generated from the DOTA dataset. The experimental results show that the proposed method has improved the accuracy of detection of the target objects of the DOTA dataset for most object classes, especially for the up‐down dataset.
Malaria Disease Diagnosis from a Blood Smear Samples using the Deep Learning MobileNet Models

Malaria is caused by a bite of a female Malaria mosquito known as Anopheles mosquitoes and is life-threatening to human lives. Infected mosquitoes carry parasites and transmit them to a human person. An estimated 229 million reported cases of malaria-affected worldwide, and it claims more than 400 thousand lives and mostly children below five years of age, according to the report of the World Health Organization. Early prevention and treatment of malaria can cut disease spread and avoid deaths. The common techniques implemented by the medical authorities are either microscopy or rapid diagnostic test. Deep learning is a popular method used in solving classification problems. One such method is the Convolutional Neural Networks (CNN) that captures raw images in the pixel that learns to extract features from learned images and classifies the input image. In this study, the researchers were able to implement three versions MobileNet models of CNN. A total of 27,558 malaria parasitized and uninfected images were used. These datasets were divided into trained data of 22,046 images, test data of 4,134 images, and validation data of 1,378 images. The trained models generated an accuracy rate between 94% to 96.5%, having the MobileNetV3 with the highest accuracy rate earned of 96.5%. In contrast, the lowest recorded accuracy rate was the MobileNetV2 with a rate of The models were able to predict malaria parasitized and malaria uninfected images.